A novel framework for modeling value for the customer, an essay on negotiation

This paper proposes a novel framework for modelling the Value for the Customer, the so-called the Conceptual Model for Decomposing Value for the Customer (CMDVC). This conceptual model is first validated through an exploratory case study where the authors validate both the proposed constructs of the model and their relations. In a second step the authors propose a mathematical formulation for the CMDVC as well as a computational method. This has enabled the final quantitative discussion of how the CMDVC can be applied and used in the enterprise environment, and the final validation by the people in the enterprise. Along this research, we were able to confirm that the results of this novel quantitative approach to model the Value for the Customer is consistent with the company's empirical experience. The paper further discusses the merits and limitations of this approach, proposing that the model is likely to bring value to support not only the contract preparation at an Ex-Ante Negotiation Phase, as demonstrated, but also along the actual negotiation process, as finally confirmed by an enterprise testimonial.

Corporate brand identity management – proposal of a new framework

The term “corporate brand” has been widely used in literature since the eighties. According to Balmer (1998) this concept tends to be used as an alternative to the concept of corporate identity. The author argues that the use of branding principles to discuss corporate identity has tended to align the area more closely with marketing. However, the literature on brand management (Aaker, 1991; Kapferer, 1991 and de Chernatony and McDonald, 1992), gives little attention to the corporate brand” (p. 985). Based on the concepts of corporate brand, brand identity and B2B relationship, the authors are interested in eliminating this gap in literature by designing a framework of corporate brand identity management. The aim of this investigation is to investigate the impact of B2B relationships in corporate brand identity management. The methodology used is quantitative analysis of surveys and scale development. The originality of this paper is to investigate the influence of the relationship between brands in corporate brand identity. This investigation is very important to help the decisions of the corporate brand managers and academics. According to literature, namely on corporate brands (Balmer 2002b, Hatch and Schultz, 2001, 2003) and on brand identity (Kapferer, 1991, 2008, Aaker, 1996, de Chernatony, 1999) the authors developed a corporate brand identity management framework considering relationships between brands a context variable with definite impact on identity management as stated by Hakansson and Snehota (1989, 1995). These authors consider that organisations´ identity management is pursued under a relational perspective with impact on identity management. Most researchers on identity and corporate brand emphasise the importance of external influences (Kennedy, 1977; King, 1991; de Chernatony, 1999; Balmer and Gray, 2000; Balmer, 2002a). Those influences concern legislation, concurrence, political issues... and stakeholders’ perceptions and reputations (due to the holistic approach demanded by corporate brands). In this context the authors claim the importance of another influence: B2B relationships. This decision is inspired in sociological studies (Mannheim, 1950; and Tajfel and Turner, 1979) regarding individual identity. These authors claim that individuals form their personality by interacting in the social field. The authors argue that corporate brand identity also develops itself under a relational approach. The relationships selected to pursue this investigation are the ones that are developed by Portuguese universities and investigation centres that cooperate by developing investigation. Those centres are administrative and financially autonomous

Group decision making and Quality-of-information in e-Health systems

Knowledge is central to the modern economy and society. Indeed, the knowledge society has transformed the concept of knowledge and is more and more aware of the need to overcome the lack of knowledge when has to make options or address its problems and dilemmas. One’s knowledge is less based on exact facts and more on hypotheses, perceptions or indications. Even when we use new computational artefacts and novel methodologies for problem solving, like the use of Group Decision Support Systems (GDSSs), the question of incomplete information is in most of the situations marginalized. On the other hand, common sense tells us that when a decision is made it is impossible to have a perception of all the information involved and the nature of its intrinsic quality. Therefore, something has to be made in terms of the information available and the process of its evaluation. It is under this framework that a Multi-valued Extended Logic Programming language will be used for knowledge representation and reasoning, leading to a model that embodies the Quality-of-Information (QoI) and its quantification, along the several stages of the decision-making process. In this way, it is possible to provide a measure of the value of the QoI that supports the decision itself. This model will be here presented in the context of a GDSS for VirtualECare, a system aimed at sustaining online healthcare services.

Demand response in future power systems management – a conceptual framework and simulation tool

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Framework de controlo de acessos para Triple-stores com interface Sail API

Grande parte dos triples-stores são open source e desenvolvidos em Java, disponibilizando interfaces standards e privadas de acesso. A grande maioria destes sistemas não dispõe de mecanismos de controlo de acessos nativos, o que dificulta ou impossibilita a sua adopção em ambientes em que a segurança dos factos é importante (e.g. ambiente empresarial). Complementarmente observa-se que o modelo de controlo de acesso a triplos e em particular a triplos descritos por ontologias não está standardizado nem sequer estabilizado, havendo diversos modelos de descrição e algoritmos de avaliação de permissões de acesso. O trabalho desenvolvido nesta tese/dissertação propõe um modelo e interface de controlo de acesso que permite e facilite a sua adopção por diferentes triple-stores já existentes e a integração dos triples-stores com outros sistemas já existentes na organização. Complementarmente, a plataforma de controlo de acesso não impõe qualquer modelo ou algoritmo de avaliação de permissões, mas pelo contrário permite a adopção de modelos e algoritmos distintos em função das necessidades ou desejos. Finalmente demonstra-se a aplicabilidade e validade do modelo e interface propostos, através da sua implementação e adopção ao triple-store SwiftOWLIM já existente, que não dispõe de mecanismo de controlo de acessos nativo.

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.

Estratégias de implementação de plataformas de e-maintenance na indústria: desenvolvimento de uma aplicação para um sistema logístico de viaturas

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Six thinking hats for electricity markets simulation: adapting meeting tools to agent decision

In almost all industrialized countries, the energy sector has suffered a severe restructuring that originated a greater complexity in market players’ interactions. The complexity that these changes brought made way for the creation of decision support tools that facilitate the study and understanding of these markets. MASCEM – “Multiagent Simulator for Competitive Electricity Markets” arose in this context providing a framework for evaluating new rules, new behaviour, and new participants in deregulated electricity markets. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. ALBidS is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This tool’s goal is to force the thinker to move outside his habitual thinking style. It was developed to be used mainly at meetings in order to “run better meetings, make faster decisions”. This dissertation presents a study about the applicability of the Six Thinking Hats technique in Decision Support Systems, particularly with the multiagent paradigm like the MASCEM simulator. As such this work’s proposal is of a new agent, a meta-learner based on STH technique that organizes several different ALBidS’ strategies and combines the distinct answers into a single one that, expectedly, out-performs any of them.

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.

Prognostic modelling of breast cancer patients: a benchmark of predictive models with external validation

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Group decision making for selection of an information system in a business context

The main objective of this work is to report on the development of a multi-criteria methodology to support the assessment and selection of an Information System (IS) framework in a business context. The objective is to select a technological partner that provides the engine to be the basis for the development of a customized application for shrinkage reduction on the supply chains management. Furthermore, the proposed methodology di ers from most of the ones previously proposed in the sense that 1) it provides the decision makers with a set of pre-defined criteria along with their description and suggestions on how to measure them and 2)it uses a continuous scale with two reference levels and thus no normalization of the valuations is required. The methodology here proposed is has been designed to be easy to understand and use, without a specific support of a decision making analyst.

Intelligent decision support systems for collaboration in industrial plants

Dissertação apresentada para obtenção do Grau de Doutor em Sistemas de Informação Industriais, Engenharia Electrotécnica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.