Interpersonal pattern dynamics and adaptive behavior in multiagent neurobiological systems : conceptual model and data

Ecological dynamics characterizes adaptive behavior as an emergent, self-organizing property of interpersonal interactions in complex social systems. The authors conceptualize and investigate constraints on dynamics of decisions and actions in the multiagent system of team sports. They studied coadaptive interpersonal dynamics in rugby union to model potential control parameter and collective variable relations in attacker–defender dyads. A videogrammetry analysis revealed how some agents generated fluctuations by adapting displacement velocity to create phase transitions and destabilize dyadic subsystems near the try line. Agent interpersonal dynamics exhibited characteristics of chaotic attractors and informational constraints of rugby union boxed dyadic systems into a low dimensional attractor. Data suggests that decisions and actions of agents in sports teams may be characterized as emergent, self-organizing properties, governed by laws of dynamical systems at the ecological scale. Further research needs to generalize this conceptual model of adaptive behavior in performance to other multiagent populations.

Toward a fuzzy domain ontology extraction method for adaptive e-learning

With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, for instructors to read through and analyze the online messages to predict the progress of their students on the fly. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy domain ontology extraction algorithm. The proposed mechanism can automatically construct concept maps based on the messages posted to online discussion forums. By browsing the concept maps, instructors can quickly identify the progress of their students and adjust the pedagogical sequence on the fly. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance e-Learning.

The explicit hazard model – part 1: theoretical development

Modern Engineering Asset Management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Appropriate mathematical models that are capable of estimating times to failures and the probability of failures in the future are essential in EAM. In most real-life situations, the lifetime of an engineering asset is influenced and/or indicated by different factors that are termed as covariates. Hazard prediction with covariates is an elemental notion in the reliability theory to estimate the tendency of an engineering asset failing instantaneously beyond the current time assumed that it has already survived up to the current time. A number of statistical covariate-based hazard models have been developed. However, none of them has explicitly incorporated both external and internal covariates into one model. This paper introduces a novel covariate-based hazard model to address this concern. This model is named as Explicit Hazard Model (EHM). Both the semi-parametric and non-parametric forms of this model are presented in the paper. The major purpose of this paper is to illustrate the theoretical development of EHM. Due to page limitation, a case study with the reliability field data is presented in the applications part of this study.

The Explicit Hazard Model – Part 2 : Applications

Hazard and reliability prediction of an engineering asset is one of the significant fields of research in Engineering Asset Health Management (EAHM). In real-life situations where an engineering asset operates under dynamic operational and environmental conditions, the lifetime of an engineering asset can be influenced and/or indicated by different factors that are termed as covariates. The Explicit Hazard Model (EHM) as a covariate-based hazard model is a new approach for hazard prediction which explicitly incorporates both internal and external covariates into one model. EHM is an appropriate model to use in the analysis of lifetime data in presence of both internal and external covariates in the reliability field. This paper presents applications of the methodology which is introduced and illustrated in the theory part of this study. In this paper, the semi-parametric EHM is applied to a case study so as to predict the hazard and reliability of resistance elements on a Resistance Corrosion Sensor Board (RCSB).

The studio sans frontières : the open-studio as an answer to the challenges of teaching contemporary art

Since at least the 1960s, art has assumed a breadth of form and medium as diverse as social reality itself. Where once it was marginal and transgressive for artists to work across a spectrum of media, today it is common practice. In this ‘post-medium’ age, fidelity to a specific branch of media is a matter of preference, rather than a code of practice policed by gallerists, curators and critics. Despite the openness of contemporary art practice, the teaching of art at most universities remains steadfastly discipline-based. Discipline-based art teaching, while offering the promise of focussed ‘mastery’ of a particular set of technical skills and theoretical concerns, does so at the expense of a deeper and more complex understanding of the possibilities of creative experimentation in the artist’s studio. By maintaining an hermetic approach to medium, it does not prepare students sufficiently for the reality of art making in the twenty-first century. In fact, by pretending that there is a select range of techniques fundamental to the artist’s trade, discipline-based teaching can often appear to be more engaged with the notion of skills preservation than purposeful art training. If art schools are to survive and prosper in an increasingly vocationally-oriented university environment, they need to fully synthesise the professional reality of contemporary art practice into their approach to teaching and learning. This paper discusses the way in which the ‘open’ studio approach to visual art study at QUT endeavours to incorporate the diversity and complexity of contemporary art while preserving the sense of collective purpose that discipline-based teaching fosters. By allowing students to independently develop their own art practices while also applying collaborative models of learning and assessment, the QUT studio program aims to equip students with a strong sense of self-reliance, a broad awareness and appreciation of contemporary art, and a deep understanding of studio-based experimentation unfettered by the boundaries of traditional media: all skills fundamental to the practice of contemporary art.

Higher Order Thinking through ICT: What do middle years teachers think really matters?

The middle years of schooling has emerged as an important focus in Australian education. Student disengagement and alienation, the negative effects of non-completion of the senior years of schooling and underachievement have raised concerns about the quality of education during the middle years. For many schools, reshaping the middle years has involved incorporating Information and Communication Technologies (ICT) to motivate students. However, simultaneously there is a need to ensure that programs are academically rigorous. There is little doubt that there are potential benefits to integrating ICT into programs for middle years’ students. However, little is known about how middle years’ teachers perceive higher order thinking, which is a component of academic rigour. This paper investigates the question of What are teachers’ perceptions of higher order thinking in an ICT environment? The study is underpinned by socio-cultural theory which is based on the belief that learning occurs through social interaction and that individuals are shaped by the social and cultural tools and instruments they engage with. This investigation used a collective case study design. Two methods were used for data collection. These methods are semi-structured interviews with individual teachers and a class and a focus group discussion with teachers. Findings indicate that teachers hold various perceptions of higher order thinking that lead to productive approaches to integrating ICT in middle years’ classrooms. The paper highlights that there may be a continuum of perceptions of higher order thinking with ICT. This continuum may inform professional developers who are guiding and supporting teachers to integrate ICT into middle years’ classrooms.

Towards an acoustic environmental observatory

The need for large scale environmental monitoring to manage environmental change is well established. Ecologists have long used acoustics as a means of monitoring the environment in their field work, and so the value of an acoustic environmental observatory is evident. However, the volume of data generated by such an observatory would quickly overwhelm even the most fervent scientist using traditional methods. In this paper we present our steps towards realising a complete acoustic environmental observatory - i.e. a cohesive set of hardware sensors, management utilities, and analytical tools required for large scale environmental monitoring. Concrete examples of these elements, which are in active use by ecological scientists, are also presented

Estimation and compensation of clipping noise in OFDMA systems

We propose an efficient and low-complexity scheme for estimating and compensating clipping noise in OFDMA systems. Conventional clipping noise estimation schemes, which need all demodulated data symbols, may become infeasible in OFDMA systems where a specific user may only know his own modulation scheme. The proposed scheme first uses equalized output to identify a limited number of candidate clips, and then exploits the information on known subcarriers to reconstruct clipped signal. Simulation results show that the proposed scheme can significantly improve the system performance.

Mining Negative Relevance Feedback for Information Filtering

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

One small difference after another : stories of grassroots leaders

Grassroots groups – autonomous, not-for-profit groups made up of volunteers – and grassroots initiatives play an invaluable, yet often invisible, role in our communities. The informal processes and collective efforts of grassroots associations, social movements, self-help groups and local action collectives are central to civil society and community building. Grassroots leaders are critical to such initiatives, yet little is known about their influences, motivations, successes and challenges. This study aims to address this dearth in the research literature by noting the experiences of a sample of grassroots community leaders to help gain a greater knowledge about community leadership in action. In-depth semi-structured interviews were held with nine grassroots leaders from a broad cross-section of sectors of interest. The criteria for selection were that these leaders were not in a formal non-profit organisation, were not paid for their work yet were leading grassroots groups or initiatives involved in active community building, campaigning or self-help. The paper reflects on findings in regard to the formative experiences that impacted upon the community leaders’ direction in life, their beliefs and ideas about what it means to be a leader, the strategies they use to lead and challenges they continue to face, and the role of learning and support in maintaining and developing their roles. Finally, the key themes relating to grassroots leadership and how these leaders enhance their own effectiveness and resilience are explored.

Inducing shades of meaning by matrix methods : a first step towards thematic analysis of opinion

This article explores two matrix methods to induce the ``shades of meaning" (SoM) of a word. A matrix representation of a word is computed from a corpus of traces based on the given word. Non-negative Matrix Factorisation (NMF) and Singular Value Decomposition (SVD) compute a set of vectors corresponding to a potential shade of meaning. The two methods were evaluated based on loss of conditional entropy with respect to two sets of manually tagged data. One set reflects concepts generally appearing in text, and the second set comprises words used for investigations into word sense disambiguation. Results show that for NMF consistently outperforms SVD for inducing both SoM of general concepts as well as word senses. The problem of inducing the shades of meaning of a word is more subtle than that of word sense induction and hence relevant to thematic analysis of opinion where nuances of opinion can arise.

Augmenting web service discovery by cognitive semantics and abduction

We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity.

Spoken term detection using fast phonetic decoding

While spoken term detection (STD) systems based on word indices provide good accuracy, there are several practical applications where it is infeasible or too costly to employ an LVCSR engine. An STD system is presented, which is designed to incorporate a fast phonetic decoding front-end and be robust to decoding errors whilst still allowing for rapid search speeds. This goal is achieved through mono-phone open-loop decoding coupled with fast hierarchical phone lattice search. Results demonstrate that an STD system that is designed with the constraint of a fast and simple phonetic decoding front-end requires a compromise to be made between search speed and search accuracy.

Unsupervised speaker adaptation for telephone call transcription

The use of the PC and Internet for placing telephone calls will present new opportunities to capture vast amounts of un-transcribed speech for a particular speaker. This paper investigates how to best exploit this data for speaker-dependent speech recognition. Supervised and unsupervised experiments in acoustic model and language model adaptation are presented. Using one hour of automatically transcribed speech per speaker with a word error rate of 36.0%, unsupervised adaptation resulted in an absolute gain of 6.3%, equivalent to 70% of the gain from the supervised case, with additional adaptation data likely to yield further improvements. LM adaptation experiments suggested that although there seems to be a small degree of speaker idiolect, adaptation to the speaker alone, without considering the topic of the conversation, is in itself unlikely to improve transcription accuracy.

Bustracker : digitally augmented public transportation

Public transportation is an environment with great potential for applying location-based services through mobile devices. The BusTracker study is looking at how real-time passenger information systems can provide a core platform to improve commuters’ experiences. These systems rely on mobile computing and GPS technology to provide accurate information on transport vehicle locations. BusTracker builds on this mobile computing platform and geospatial information. The pilot study is running on the open source BugLabs computing platform, using a GPS module for accurate location information.