An Extended Event Reasoning Framework for Decision Support under Uncertainty

To provide in-time reactions to a large volume of surveil- lance data, uncertainty-enabled event reasoning frameworks for CCTV and sensor based intelligent surveillance system have been integrated to model and infer events of interest. However, most of the existing works do not consider decision making under uncertainty which is important for surveillance operators. In this paper, we extend an event reasoning framework for decision support, which enables our framework to predict, rank and alarm threats from multiple heterogeneous sources.

Decision Making by Credal Nets

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. This feature makes the model particularly suited for the implementation of classifiers and knowledge-based systems. When working with sets of (instead of single) probability distributions, the identification of the optimal option can be based on different criteria, some of them eventually leading to multiple choices. Yet, most of the inference algorithms for credal nets are designed to compute only the bounds of the posterior probabilities. This prevents some of the existing criteria from being used. To overcome this limitation, we present two simple transformations for credal nets which make it possible to compute decisions based on the maximality and E-admissibility criteria without any modification in the inference algorithms. We also prove that these decision problems have the same complexity of standard inference, being NP^PP-hard for general credal nets and NP-hard for polytrees.

Chaotropicity: a key factor in product tolerance of biofuel-producing microorganisms

Fermentation products can chaotropically disorder macromolecular systems and induce oxidative stress, thus inhibiting biofuel production. Recently, the chaotropic activities of ethanol, butanol and vanillin have been quantified (5.93, 37.4, 174kJkg(-1)m(-1) respectively). Use of low temperatures and/or stabilizing (kosmotropic) substances, and other approaches, can reduce, neutralize or circumvent product-chaotropicity. However, there may be limits to the alcohol concentrations that cells can tolerate; e.g. for ethanol tolerance in the most robust Saccharomyces cerevisiae strains, these are close to both the solubility limit (<25%, w/v ethanol) and the water-activity limit of the most xerotolerant strains (0.880). Nevertheless, knowledge-based strategies to mitigate or neutralize chaotropicity could lead to major improvements in rates of product formation and yields, and also therefore in the economics of biofuel production.

Application of Evidence Theory and Discounting Techniques to Aerospace Design

Critical decisions are made by decision-makers throughout
the life-cycle of large-scale projects. These decisions are crucial as they
have a direct impact upon the outcome and the success of projects. To aid
decision-makers in the decision making process we present an evidential
reasoning framework. This approach utilizes the Dezert-Smarandache
theory to fuse heterogeneous evidence sources that suffer from levels
of uncertainty, imprecision and conflicts to provide beliefs for decision
options. To analyze the impact of source reliability and priority upon
the decision making process, a reliability discounting technique and a
priority discounting technique, are applied. A maximal consistent subset
is constructed to aid in dening where discounting should be applied.
Application of the evidential reasoning framework is illustrated using a
case study based in the Aerospace domain.

Bacterial algorithm applied for fuzzy rule extraction

This paper presents a method of using the so-colled "bacterial algorithm" (4,5) for extracting a fuzzy rule base from a training set. The bewly proposed bacterial evolutionary algorithm (BEA) is shown. In our application one bacterium corresponds to a fuzzy rule system.

Managing sales people strategically when promoting new products: Incorporating market orientation into a sales management control framework

Salespeople play a pivotal role in promoting new products. Therefore, managers need to know what control mechanism (i.e., output-based control, behavior-based control, or knowledge-based control) can improve their salespeople's new product sales performance. Furthermore, managers may be able to assist salespeople in performing better by having a strong market orientation. The literature has been inconsistent regarding the effects of sales management control mechanisms and has not yet incorporated market orientation into a sales management control framework. The current study surveyed 315 Taiwanese salespeople from publicly traded electronics companies with the aim of contributing to the sales management literature. The results show that sales management controls can directly affect salespeople's innovativeness, which, in turn, affects new product sales performance. However, sales management controls cannot affect performance directly. Furthermore, market orientation can positively moderate the relationship between salespeople's innovativeness and new product sales performance.

Developing a Technoself System to Improve Lifelong Learning Engagement

Lifelong learning exists today in the context of a cultural and societal shift to a knowledge-based, technology-enhanced, and rapidly-changing economy. It has a significant impact on people’s lives and has become of vital importance with the emergence of new technologies that change how people communicate, collect information, and collaborate with others. The emerging technologies, such as social networking, interactive media and game technology, have expanded a new dimension of self – ‘technoself’ driven by socio-technical innovations and taken an important step forward in lifelong learning through the Technology Enhanced Learning (TEL). The TEL encourages learners as producers to embed personalized knowledge and collective experience on individualized learning within professional practice. It becomes more personal and social than traditional lifelong learning, especially about the ‘learning as socially grounded’ aspects. This paper studies the development of technoself system during lifelong learning and introduces technoself enhanced learning as a novel sociological framework of lifelong learning to couple the educational dimension with social dimension in order to enhance learner engagement by shaping personal learning focus and setting. We examine how people construct their own inquiry and learn from others, how people shift and adapt in these technoself-enhanced learning environments, and how learner engagement is improving as the involvement of learners as producers in lifelong learning. We further discuss the barriers and the positive and negative unintended consequences of using technology for lifelong learning.

Residential heat pump installations: the role of vocational education and training

Meeting European emissions targets is reliant on innovative renewable technologies, particularly ‘renewable heat’ from heat pumps. Heat pump performance is driven by Carnot efficiency and optimum performance requires the lowest possible space heating flow temperatures leading to greater sensitivity to poor design, installation and operation. Does sufficient training and installer capacity exist for this technology? This paper situates the results of heat pump field trial performance in a socio-technical context, identifying how far installer competence requirements are met within the current vocational education and training (VET) system and considers possible futures. Few UK installers have formal heat pump qualifications at National Vocational Qualification (NVQ) level 3 and heat pump VET is generally through short-course provision where the structure of training is largely unregulated with no strict adherence to a common syllabus or a detailed training centre specification. Prerequisites for short-course trainees, specifically the demand for heating system knowledge based on metric design criteria, is limited and proof of ‘experience’ is an accepted alternative to formal educational qualifications. The lack of broader educational content and deficiencies in engineering knowledge will have profound negative impacts on both the performance and market acceptance of heat pumps. Possible futures to address this problem are identified.

An intelligent tutoring approach to support students and technicians in electrical installation design

Designing electric installation projects, demands not only academic knowledge, but also other types of knowledge not easily acquired through traditional instructional methodologies. A lot of additional empirical knowledge is missing and so the academic instruction must be completed with different kinds of knowledge, such as real-life practical examples and simulations. On the other hand, the practical knowledge detained by the most experienced designers is not formalized in such a way that is easily transmitted. In order to overcome these difficulties present in the engineers formation, we are developing an Intelligent Tutoring System (ITS), for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students.

Embedded platform for ECG biometric recognition

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Prática de ensino supervisionado no 1.º e 2.º ciclo do ensino básico: a desenvolvimento da consciência linguística num 1.º ano de escolaridade

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e do 2.º Ciclo do Ensino Básico

ESEIG Mobile: an m-learning approach in a superior school

In recent years, mobile learning has emerged as an educational approach to decrease the limitation of learning location and adapt the teaching-learning process to all type of students. However, the large number and variety of Web-enabled devices poses challenges for Web content creators who want to automatic get the delivery context and adapt the content to mobile devices. This paper studies several approaches to adapt the learning content to mobile phones. It presents an architecture for deliver uniform m-Learning content to students in a higher School. The system development is organized in two phases: firstly enabling the educational content to mobile devices and then adapting it to all the heterogeneous mobile platforms. With this approach, Web authors will not need to create specialized pages for each kind of device, since the content is automatically transformed to adapt to any mobile device capabilities from WAP to XHTML MP-compliant devices.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.