Supporting the learning and networking experiences of doctoral students

This study explores the impact of a Graduate Virtual Research Environment (GVRE) on the learning and networking experiences of research students. The GVRE was established to support and enhance research skills and employability training across a university. It provides an extensive range of resources including video reflections based on the experiences of students and staff; GVRE members are encouraged to comment and engage in discussions on these resources. Our work is framed using social theories of learning and the role of communities in the support and development of research students. In particular, we are interested in exploring the challenges involved in developing communities and networks for students whose main focus is their individual research. The GVRE was made available to over 600 students and in this research we explore its impact on the experiences of research students. In particular, we investigate four questions: (a) what impact does the students use of the GVRE have on the development of their research skills; (b) what impact does membership of the GVRE have on the networks and communities of research students; (c) how do research students view the relationships between their research skills training programme, their individual research and the GVRE; and (d) how do research students currently use social media. We use an interpretivist approach and our data sources include site statistics, responses to a questionnaire and also feedback from a focus group. Our findings indicate that networking remains an issue and students suggested approaches to facilitating this using the GVRE: (1) A clearer pathway from skills need identification to skills acquisition; (2) Rewards for activities around networking - possibly through credit on the training scheme; (3) Activities that would involve research directly. Feedback on the GVRE indicated that it is valued by research students as it facilitates the development of their research skills. In terms of marketing the GVRE to research students important factors identified were: the ease of access to the site, the overview it gives of the PhD process; and the value of the site to students around the defining moments of their studies when the students felt they needed additional advice and guidance.

Identification of meat spoilage by FTIR spectroscopy and neural networks

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid determination of meat spoilage, Fourier transform infrared (FTIR) spectroscopy technique, with the help of advanced learning-based methods, was attempted in this work. FTIR spectra were obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total viable counts. A fuzzy principal component algorithm has been also developed to reduce the dimensionality of the spectral data. The results confirmed the superiority of the adopted scheme compared to the partial least squares technique, currently used in food microbiology.

A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.

Network mechanisms of intentional learning

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flex- ible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ven- tral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple dis- tinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.

Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).

Joint Power Amplifiers and I/Q Modulator Impairments Modelling and Compensation for LTE Transmitters Using Artificial Neural Networks

This work introduces joint power amplifier (PA) and I/Q modulator modelling and compensation for LongTerm Evolution (LTE) transmitters using artificial neural networks (ANNs). The proposed solution util-izes a powerful nonlinear autoregressive with exogenous inputs (NARX) ANN architecture, which yieldsnoticeable results for high peak to average power ratio (PAPR) LTE signals. Given the ANNs learning capa-bilities, this one-step solution, which includes the mitigation of both PA nonlinearity and I/Q modulatorimpairments, is both accurate and adaptable

Learning Environments in Design Studio Culture

The paper reports on a study of design studio culture from a student perspective. Learning in design studio culture has been theorised variously as a signature pedagogy emulating professional practice models, as a community of practice and as a form of problem-based learning, all largely based on the study of teaching events in studio. The focus of this research has extended beyond formally recognized activities to encompass the student’s experience of their social and community networks, working places and study set-ups, to examine how these have contributed to studio culture and how there have been supported by studio teaching. Semi-structured interviews with final year undergraduate students of architecture formed the basis of the study using an interpretivist approach informed by Actor-network theory, with studio culture featured as the focal actor, enrolling students and engaging with other actors, together constituting an actor-network of studio culture. The other actors included social community patterns and activities; the numerous working spaces (including but not limited to the studio space itself); the equipment, tools of trade and material pre-requisites for working; the portfolio enrolling the other actors to produce work for it; and the various formal and informal events associated with the course itself. Studio culture is a highly charged social arena: The question is how, and in particular, which aspects of it support learning? Theoretical models of situated learning and communities of practice models have informed the analysis, with Bourdieu’s theory of practice, and his interrelated concepts of habitus, field and capital providing a means of relating individually acquired habits and modes of working to social contexts. Bourdieu’s model of habitus involves the externalisation through the social realm of habits and knowledge previously internalised. It is therefore a useful model for considering whole individual learning activities; shared repertoires and practices located in the social realm. The social milieu of the studio provides a scene for the exercise and display of ‘practicing’ and the accumulation of a form of ‘practicing-capital’. This capital is a property of the social milieu rather than the space, so working or practicing in the company of others (in space and through social media) becomes a more valued aspect of studio than space or facilities alone. This practicing-capital involves the acquisition of a habitus of studio culture, with the transformation of physical practices or habits into social dispositions, acquiring social capital (driving the social milieu) and cultural capital (practicing-knowledge) in the process. The research drew on students’ experiences, and their practicing ‘getting a feel for the game’ by exploring the limits or boundaries of the field of studio culture. The research demonstrated that a notional studio community was in effect a social context for supporting learning; a range of settings to explore and test out newly internalised knowledge, demonstrate or display ideas, modes of thinking and practicing. The study presents a nuanced interpretation of how students relate to a studio culture that involves a notional community, and a developing habitus within a field of practicing that extends beyond teaching scenarios.