Improvement of a dynamic accumulator at ICICS 07 and its application in multi-user keyword-based retrieval on encrypted data

A dynamic accumulator is an algorithm, which merges a large set of elements into a constant-size value such that for an element accumulated, there is a witness confirming that the element was included into the value, with a property that accumulated elements can be dynamically added and deleted into/from the original set. Recently Wang et al. presented a dynamic accumulator for batch updates at ICICS 2007. However, their construction suffers from two serious problems. We analyze them and propose a way to repair their scheme. We use the accumulator to construct a new scheme for common secure indices with conjunctive keyword-based retrieval.

Dynamic self-organization phenomena in complex ionized gas systems : new paradigms and technological aspects

An overview of dynamic self-organization phenomena in complex ionized gas systems, associated physical phenomena, and industrial applications is presented. The most recent experimental, theoretical, and modeling efforts to understand the growth mechanisms and dynamics of nano- and micron-sized particles, as well as the unique properties of the plasma-particle systems (colloidal, or complex plasmas) and the associated physical phenomena are reviewed and the major technological applications of micro- and nanoparticles are discussed. Until recently, such particles were considered mostly as a potential hazard for the microelectronic manufacturing and significant efforts were applied to remove them from the processing volume or suppress the gas-phase coagulation. Nowadays, fine clusters and particulates find numerous challenging applications in fundamental science as well as in nanotechnology and other leading high-tech industries.

Keyword field-free conjunctive keyword searches on encrypted data and extension for dynamic groups

We consider the following problem: a user stores encrypted documents on an untrusted server, and wishes to retrieve all documents containing some keywords without any loss of data confidentiality. Conjunctive keyword searches on encrypted data have been studied by numerous researchers over the past few years, and all existing schemes use keyword fields as compulsory information. This however is impractical for many applications. In this paper, we propose a scheme of keyword field-free conjunctive keyword searches on encrypted data, which affirmatively answers an open problem asked by Golle et al. at ACNS 2004. Furthermore, the proposed scheme is extended to the dynamic group setting. Security analysis of our constructions is given in the paper.

Lower partial moment and information entropy-based dynamic electricity purchasing strategy

In the electricity market environment, load-serving entities (LSEs) will inevitably face risks in purchasing electricity because there are a plethora of uncertainties involved. To maximize profits and minimize risks, LSEs need to develop an optimal strategy to reasonably allocate the purchased electricity amount in different electricity markets such as the spot market, bilateral contract market, and options market. Because risks originate from uncertainties, an approach is presented to address the risk evaluation problem by the combined use of the lower partial moment and information entropy (LPME). The lower partial moment is used to measure the amount and probability of the loss, whereas the information entropy is used to represent the uncertainty of the loss. Electricity purchasing is a repeated procedure; therefore, the model presented represents a dynamic strategy. Under the chance-constrained programming framework, the developed optimization model minimizes the risk of the electricity purchasing portfolio in different markets because the actual profit of the LSE concerned is not less than the specified target under a required confidence level. Then, the particle swarm optimization (PSO) algorithm is employed to solve the optimization model. Finally, a sample example is used to illustrate the basic features of the developed model and method.

Detection of dynamic primary user with cooperative spectrum sensing

In this paper we demonstrate that existing cooperative spectrum sensing formulated for static primary users cannot accurately detect dynamic primary users regardless of the information fusion method. Performance error occurs as the sensing parameters calculated by the conventional detector result in sensing performance that violates the sensing requirements. Furthermore, the error is accumulated and compounded by the number of cooperating nodes. To address this limitation, we design and implement the duty cycle detection model for the context of cooperative spectrum sensing to accurately calculate the sensing parameters that satisfy the sensing requirements. We show that longer sensing duration is required to compensate for dynamic primary user traffic.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

AR and gamification concepts to reduce driver boredom and risk taking behaviours

Young males are over-represented in road crashes. Part of the problem is their proneness to boredom, a hardwired personality factor that can lead to risky driving. This paper presents a theoretical understanding of boredom in the driving context and demonstrates convincing arguments to investigate the role of boredom further. Specifically, this paper calls for the design of innovative technologies and applications that make safe driving more pleasurable and stimulating for young males, e.g., by applying gamification techniques. We propose two design concepts through the following questions: A. Can the simulation of risky driving reduce actual risky driving? B. Can the replacement of risky driving stimuli with alternative stimuli reduce risky driving? We argue that considering these questions in the future design of automotive user-interfaces and personal ubiquitous computing devices could effectively reduce risky driving behaviours among young males.

Good choices, great future : an applied theatre prevention program to reduce alcohol-related risky behaviours during Schoolies

Introduction and Aims. The contextual and temporal factors of post-school celebratory events ('Schoolies') place young people at elevated risk of excessive drinking compared with other social occasions. This study investigates the impact of an applied theatre prevention program 'Choices' in reducing the risk of drinking and other risk behaviours during Schoolies celebrations. Design and Methods. Choices was delivered in the last term of Year 12 across 28 North Queensland schools. A total of 352 school leavers (43.1% male, mean age=17.14years) completed a questionnaire at Whitsunday Schoolies, Queensland, Australia on 23-24 November 2010. Nearly 49% of respondents had attended Choices. The survey included measures of alcohol use, illicit drug use and associated problems during Schoolies and a month prior to Schoolies. Results. After controlling for gender and pre-Schoolies drinking, school leavers who attended Choices were significantly less likely to report illicit drug use (OR=0.51, P<0.05) and problem behaviours (OR=0.40, P<0.01) than those who did not attend Choices. There was, however, no intervention effect in risky drinking (i.e. drank on 5 or more days, typical amount five or more standard drink and binge drank on 3 or more days) at Schoolies (OR=0.92, P=0.80). Discussion and Conclusions. Delivery of a youth-specific applied theatre prevention program employing a harm minimisation framework may be effective in reducing high-risk behaviours associated with alcohol consumption at celebratory events, even if young people expect to engage in excessive alcohol consumption. [Quek L-H, White A, Low C, Brown J, Dalton N, Dow D, Connor JP. Good choices, great future: An applied theatre prevention program to reduce alcohol-related risky behaviours during Schoolies. Drug Alcohol Rev 2012;31:897–902]

Emotion recognition of static and dynamic faces in autism spectrum disorder

There is substantial evidence for facial emotion recognition (FER) deficits in autism spectrum disorder (ASD). The extent of this impairment, however, remains unclear, and there is some suggestion that clinical groups might benefit from the use of dynamic rather than static images. High-functioning individuals with ASD (n = 36) and typically developing controls (n = 36) completed a computerised FER task involving static and dynamic expressions of the six basic emotions. The ASD group showed poorer overall performance in identifying anger and disgust and were disadvantaged by dynamic (relative to static) stimuli when presented with sad expressions. Among both groups, however, dynamic stimuli appeared to improve recognition of anger. This research provides further evidence of specific impairment in the recognition of negative emotions in ASD, but argues against any broad advantages associated with the use of dynamic displays.