Beyond Robotic Wastelands of Time: Abandoned Pedagogical Agents and New Pedalled Pedagogies

Chatbots, known as pedagogical agents in educational settings, have a long history of use, beginning with Alan Turing’s work. Since then online chatbots have become embedded into the fabric of technology. Yet understandings of these technologies are inchoate and often untheorised. Integration of chatbots into educational settings over the past five years suggests an increase in interest in the ways in which chatbots might be adopted and adapted for teaching and learning. This article draws on historical literature and theories that to date have largely been ignored in order to (re)contextualise two studies that used responsive evaluation to examine the use of pedagogical agents in education. Findings suggest that emotional interactions with pedagogical agents are intrinsic to a user’s sense of trust, and that truthfulness, personalisation and emotional engagement are vital when using pedagogical agents to enhance online learning. Such findings need to be considered in the light of ways in which notions of learning are being redefined in the academy and the extent to which new literacies and new technologies are being pedalled as pedagogies in ways that undermine what higher education is, is for, and what learning means.

Preterm nutritional intake and MRI phenotype at term age: a prospective observational study

Objective: To describe (1) the relationship between nutrition and the preterm-at-term infant phenotype, (2) phenotypic differences between preterm-at-term infants and healthy term born infants and (3) relationships between somatic and brain MRI outcomes. Design: Prospective observational study. Setting: UK tertiary neonatal unit. Participants: Preterm infants (<32 weeks gestation) (n=22) and healthy term infants (n=39) Main outcome measures: Preterm nutrient intake; total and regional adipose tissue (AT) depot volumes; brain volume and proximal cerebral arterial vessel tortuosity (CAVT) in preterm infants and in term infants. Results: Preterm nutrition was deficient in protein and high in carbohydrate and fat. Preterm nutrition was not related to AT volumes, brain volume or proximal CAVT score; a positive association was noted between human milk intake and proximal CAVT score (r=0.44, p=0.05). In comparison to term infants, preterm infants had increased total adiposity, comparable brain volumes and reduced proximal CAVT scores. There was a significant negative correlation between deep subcutaneous abdominal AT volume and brain volume in preterm infants (r=−0.58, p=0.01). Conclusions: Though there are significant phenotypic differences between preterm infants at term and term infants, preterm macronutrient intake does not appear to be a determinant. Our preliminary data suggest that (1) human milk may exert a beneficial effect on cerebral arterial vessel tortuosity and (2) there is a negative correlation between adiposity and brain volume in preterm infants at term. Further work is warranted to see if our findings can be replicated and to understand the causal mechanisms.

Just noticeable differences in perceived image contrast with changes in displayed image size

An evaluation of the change in perceived image contrast with changes in displayed image size was carried out. This was achieved using data from four psychophysical investigations, which employed techniques to match the perceived contrast of displayed images of five different sizes. A total of twenty-four S-shape polynomial functions were created and applied to every original test image to produce images with different contrast levels. The objective contrast related to each function was evaluated from the gradient of the mid-section of the curve (gamma). The manipulation technique took into account published gamma differences that produced a just-noticeable-difference (JND) in perceived contrast. The filters were designed to achieve approximately half a JND, whilst keeping the mean image luminance unaltered. The processed images were then used as test series in a contrast matching experiment. Sixty-four natural scenes, with varying scene content acquired under various illumination conditions, were selected from a larger set captured for the purpose. Results showed that the degree of change in contrast between images of different sizes varied with scene content but was not as important as equivalent perceived changes in sharpness.

Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

What is the best luminance contrast weighting-function for image quality optimization? Traditionally measured contrast sensitivity functions (CSFs), have been often used as weighting-functions in image quality and difference metrics. Such weightings have been shown to result in increased sharpness and perceived quality of test images. We suggest contextual CSFs (cCSFs) and contextual discrimination functions (cVPFs) should provide bases for further improvement, since these are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context of complex masking information. Image quality assessment is understood to require detection and discrimination of masked signals, making contextual sensitivity and discrimination functions directly relevant. In this investigation, test images are weighted with a traditional CSF, cCSF, cVPF and a constant function. Controlled mutations of these functions are also applied as weighting-functions, seeking the optimal spatial frequency band weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice psychophysical tests. We show that maximal quality for our test images, results from cCSFs and cVPFs, mutated to boost contrast in the higher visible frequencies.

Validation of the needle targeting accuracy of a MRI/TRUS- image-guided system for transperineal prostate cancer biopsy

Multi-parametric magnetic resonance imaging (mp-MRI) has become an increasingly important method for detecting and treating prostate cancer. Transrectal ultrasound (TRUS) is the most commonly used method for guiding prostate needle biopsy and remains the gold standard for diagnosis of prostate cancer. MRI-to-TRUS image reg- istration is an important technology for enabling computer-assisted targeting of the majority of prostate lesions that are visible in MRI but not independently distinguishable in TRUS images. The aim of this study was to estimate the needle placement accuracy of an image guidance system (SmartTargetÒ), developed by our research group, using a surgical training phantom.

Understanding citizen media as practice: agents, processes, publics

Much recent commentary on citizen media has focused on online platforms as means through which citizens may disseminate self-produced media content that challenges dominant discourses or makes visible hidden realities. This chapter goes beyond a concern with media content to explore the much broader range of socially situated practices that develop around citizen media. Drawing on Couldry’s proposal for a practice paradigm in media research, it suggests shifting the focus from ‘citizen media’ to ‘citizen media practices’ and demonstrates, through a case study of communication activism in the World Social Forum, how this framework can bring into view a broad range of citizen media practices (beyond those directly concerned with the production and circulation of media content), the different forms of agency that such practices make possible, and the social fabric they can help generate. I conclude by arguing that a practice framework necessitates a rethink of the way that the concept of (counter-) publics is used in the context of citizen media. Citizen media practices of the kind described here can be understood not only as practices of ‘making public’ previously unreported issues and perspectives, but as practices of public¬-making: practices that support the formation of publics.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Seller, buyer and VPP agents behavior on a simulated electricity market

Competitive electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is an electricity market simulator able to model market players and simulate their operation in the market. As market players are complex entities, having their characteristics and objectives, making their decisions and interacting with other players, a multi-agent architecture is used and proved to be adequate. MASCEM players have learning capabilities and different risk preferences. They are able to refine their strategies according to their past experience (both real and simulated) and considering other agents’ behavior. Agents’ behavior is also subject to its risk preferences.

MV producers and consumers agents characterization with DSM techniques

This paper consist in the establishment of a Virtual Producer/Consumer Agent (VPCA) in order to optimize the integrated management of distributed energy resources and to improve and control Demand Side Management DSM) and its aggregated loads. The paper presents the VPCA architecture and the proposed function-based organization to be used in order to coordinate the several generation technologies, the different load types and storage systems. This VPCA organization uses a frame work based on data mining techniques to characterize the costumers. The paper includes results of several experimental tests cases, using real data and taking into account electricity generation resources as well as consumption data.

Self-managing agents for dynamic scheduling in manufacturing

The main purpose of this paper is to propose a Multi-Agent Autonomic and Bio-Inspired based framework with selfmanaging capabilities to solve complex scheduling problems using cooperative negotiation. Scheduling resolution requires the intervention of highly skilled human problem-solvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing (AC) evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Opening Pousada da Serra da Estrela in Covilhã: A two-way impact analysis - Interacting with public agents

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Novel insights into the action of antimicrobial agents against human pathogens

Dissertation presented to obtain the Ph.D degree in Biochemistry