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.

Workflow patterns as a means to model task succession in games – a preliminary case study

Over about the last decade, people involved in game development have noted the need for more formal models and tools to support the design phase of games. It is argued that the present lack of such formal tools is currently hindering knowledge transfer among designers. Formal visual languages, on the other hand, can help to more effectively express, abstract and communicate game design concepts. Moreover, formal tools can assist in the prototyping phase, allowing designers to reason about and simulate game mechanics on an abstract level. In this paper we present an initial investigation into whether workflow patterns – which have already proven to be effective for modeling business processes – are a suitable way to model task succession in games. Our preliminary results suggest that workflow patterns show promise in this regard but some limitations, especially in regard to time constraints, currently restrict their potential.

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.

It’s Not Just a Game: Contemporary Challenges for Games Research and the Internet

In this final chapter we will raise a number of issues that we have encountered as we have put this collection of papers together. In doing this, we also reflect upon the seven challenges for video game theory that Bernard Perron and Mark Wolf(2009)put forward in the introduction to the second video game theory reader given it is probably one of the most recent assessments in the area at the time of writing. These challenges are concerned with Terminology and Accuracy, History, Methodology, Technology, Interactivity, Play and the Integration of Interdisciplinary Approaches. These issues will be brought up throughout this chapter, but not necessarily in mutually exclusive fashion...

Creative expression as the objective in video games

This study explores the idea of video games where the players are not just allowed to express themselves creatively, but are challenged to do so and are judged based on the quality of their creative expression. The outcome of the research is a series of six games which comment on this idea. The study also raises further questions regarding how current video games are constructed and designed in comparison with non-computer games, and invites a further evolution of the craft of video game design in a direction that focuses more on interpreting and reacting to what the player is doing.

Drinking games participation among female students at a regional Australian university

Participation in drinking games (DGs) by university students is often associated with heavy drinking and negative social and health impacts. Although research in Australia indicates that university students tend to drink at risky levels, there is paucity of literature on DGs among students, especially those residing at regional universities. This research examined drinking among female college students of white background. Eighteen female students participated in face-to-face in-depth interviews to describe their DG experiences. Most women played DGs for social and monetary reasons, with many drinking high volumes of alcohol during the game. Excessive drinking was linked with the type of beverage consumed. Despite knowing the health risks associated with DGs, there was a strong social imperative for these young women to play these games. Research and public health initiatives to better understand and address problematic drinking activities in rural and regional Australia have tended to ignore women and the dominant white populations whose heavy drinking has been largely restricted to private spheres.

Online Gaming in Context : the Social and Cultural Significance of Online Games

There is little question of the social, cultural and economic importance of video games in the world today, with gaming now rivalling the movie and music sectors as a major leisure industry and pastime. The significance of video games within our everyday lives has certainly been increased and shaped by new technologies and gaming patterns, including the rise of home-based games consoles, advances in mobile telephone technology, the rise in more 'sociable' forms of gaming, and of course the advent of the Internet. This book explores the opportunities, challenges and patterns of gameplay and sociality afforded by the Internet and online gaming. Bringing together a series of original essays from both leading and emerging academics in the field of game studies, many of which employ new empirical work and innovative theoretical approaches to gaming, this book considers key issues crucial to our understanding of online gaming and associated social relations, including: patterns of play, legal and copyright issues, player production, identity construction, gamer communities, communication, patterns of social exclusion and inclusion around religion, gender and disability, and future directions in online gaming.

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.

Brain networks subserving the evaluation of static and dynamic facial expressions

Because moving depictions of face emotion have greater ecological validity than their static counterparts, it has been suggested that still photographs may not engage ‘authentic’ mechanisms used to recognize facial expressions in everyday life. To date, however, no neuroimaging studies have adequately addressed the question of whether the processing of static and dynamic expressions rely upon different brain substrates. To address this, we performed an functional magnetic resonance imaging (fMRI) experiment wherein participants made emotional expression discrimination and Sex discrimination judgements to static and moving face images. Compared to Sex discrimination, Emotion discrimination was associated with widespread increased activation in regions of occipito-temporal, parietal and frontal cortex. These regions were activated both by moving and by static emotional stimuli, indicating a general role in the interpretation of emotion. However, portions of the inferior frontal gyri and supplementary/pre-supplementary motor area showed task by motion interaction. These regions were most active during emotion judgements to static faces. Our results demonstrate a common neural substrate for recognizing static and moving facial expressions, but suggest a role for the inferior frontal gyrus in supporting simulation processes that are invoked more strongly to disambiguate static emotional cues.