Samoa’s education policy: Negotiating a hybrid space for values

This paper analyses the education policy of Samoa to examine the values that are presented within as relevant to the education system. Drawing on the theory of postcolonialism and globalization, we illustrate how the global and local interact within the education policy to create a hybrid, heterogeneous mix of values and, while the policy acknowledges the significance of Samoan values, it is principally directed towards universal values being incorporated into the education system. We undertake a critical policy analysis to illustrate how the hybrid set of values are indicative of a neo-colonial discourse and argue that universal values are required, however, these need to be equally matched with local Samoan values for the education policy to be highly relevant, authentic and applicable to the Samoan education context.

A hybrid agent architecture for modeling autonomous agents in SAGE

This paper highlights the Hybrid agent construction model being developed that allows the description and development of autonomous agents in SAGE (Scalable, fault Tolerant Agent Grooming Environment) - a second generation FIPA-Compliant Multi-Agent system. We aim to provide the programmer with a generic and well defined agent architecture enabling the development of sophisticated agents on SAGE, possessing the desired properties of autonomous agents - reactivity, pro-activity, social ability and knowledge based reasoning. © Springer-Verlag Berlin Heidelberg 2005.

Consumer Evaluation of Hybrid Innovations (summary section only)

Hybrid innovations, or new products that combine two existing product categories into one, are increasingly popular in today’s marketplace. Despite this proliferation, few studies address them. The purpose of this thesis is to examine consumer evaluation of hybrid innovations by focusing on consumer categorization of such innovations and on factors contributing positively and negatively to their evaluation. This issue is examined by means of three studies. The first study addresses the proportion of consumers categorizing hybrid products as single- versus dual-purpose, what contributes to such a categorization, what differences can be found between the two groups, and if categorization can and should be included in models of innovation adoption. The second study expands on the scope by including motivation as a predictor of consumer evaluation and examines two cognitive and affective factors and their differential impact on innovation evaluation. Finally, the third study examines the product comparisons single- versus dual-purpose categorization induce. These three essays together build up a broader understanding of hybrid innovation evaluation. The thesis uses theories from both psychology and marketing to examine the issues at hand. Conceptual combination and analogical learning theories from psychology are used to comprehend categorization and knowledge transfer. From marketing, consumer behavior and innovation adoption studies are addressed to better understand the link between categorization and product evaluation and the factors contributing to product evaluation. The main results of the current thesis are that (1) most consumers categorize hybrid products as single- and not as dual-purpose products, (2) consumers that categorize them as dual-purpose find them more attractive (3) motivation has a significant effect on consumer evaluation of innovations; cognitive factors promote an emphasis on product net benefits, whereas affective factors induce consumers to consider product meaning in the form of categorization and perceived product complexity, (4) categorization constrains subsequent product evaluation, and (5) categorization can and should be included to models of innovation adoption. Maria Sääksjärvi is associated with CERS, the Center for Relationship Marketing and Service Management at the Swedish School of Economics and Business Administration

Structural characterization of backbone-expanded helices in hybrid peptides: (αγ) n and (αβ) n sequences with unconstrained β and γ homologues of L-Val

Learning your αβγ's: The diversity of hydrogen-bonding patterns in backbone-expanded hybrid helices is shown by crystal-structure determination of several oligomeric peptides (see scheme; C=gray; H=white; O=red; N=blue). C 12 helices were observed in the αγ peptide series for n=2-8. In comparison, the αα peptide and αβ peptide sequences show C 10 and mixed C 14/C 15 helices, respectively. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cramer-Rao-Type Bounds for Sparse Bayesian Learning

In this paper, we derive Hybrid, Bayesian and Marginalized Cramer-Rao lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement vector Sparse Bayesian Learning (SBL) problem of estimating compressible vectors and their prior distribution parameters. We assume the unknown vector to be drawn from a compressible Student-prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We extend the MCRB to the case where the compressible vector is distributed according to a general compressible prior distribution, of which the generalized Pareto distribution is a special case. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error (MSE) in the estimates. Further, we illustrate the tightness and utility of the bounds through simulations, by comparing them with the MSE performance of two popular SBL-based estimators. We find that the MCRB is generally the tightest among the bounds derived and that the MSE performance of the Expectation-Maximization (EM) algorithm coincides with the MCRB for the compressible vector. We also illustrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector for several values of the number of observations and at different signal powers.

Toward automated generation of parametric BIMs based on hybrid video and laser scanning data

Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A technical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each process, input and output is explained, along with the steps needed to validate them. Preliminary experiments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

We want to touch IT. Are hybrid laptops just new fancy gadgets or a new revolution?

While the number of traditional laptops and computers sold has dipped slightly year over year, manufacturers have developed new hybrid laptops with touch screens to build on the tactile trend. This market is moving quickly to make touch the rule rather than the exception and the sales of these devices have tripled since the launch of Windows 8 in 2012, to reach more than sixty million units sold in 2015. Unlike tablets, that benefit from easy-to-use applications specially designed for tactile interactions, hybrid laptops are intended to be used with regular user-interfaces. Hence, one could ask whether tactile interactions are suited for every task and activity performed with such interfaces. Since hybrid laptops are increasingly used in educational situations, this study focuses on information search tasks which are commonly performed for learning purposes. It is hypothesized that tasks that require complex and/or less common gestures will increase user's cognitive load and impair task performance in terms of efficacy and efficiency. A study was carried out in a usability laboratory with 30 participants for whom prior experience with tactile devices has been controlled. They were asked to perform information search tasks on an online encyclopaedia by using only the touch screen of and hybrid laptop. Tasks were selected with respect to their level of cognitive demand (amount of information that had to be maintained in working memory) and the complexity of gestures needed (left and/or right clicks, zoom, text selection and/or input.), and grouped into 4 sets accordingly. Task performance was measured by the number of tasks succeeded (efficacy) and time spent on each task (efficiency). Perceived cognitive load was assessed thanks to a questionnaire given after each set of tasks. An eye tracking device was used to monitor users' attention allocation and to provide objective cognitive load measures based on pupil dilation and the Index of Cognitive Activity. Each experimental run took approximately one hour. The results of this within-subjects design indicate that tasks involving complex gestures led to a lower efficacy, especially when the tasks were cognitively demanding. Regarding efficacy, there is no significant differences between sets of tasks excepted for tasks with low cognitive demand and complex gestures that required more time to be achieved. Surprisingly, users that declared the biggest experience with tactile devices spent more time than less frequent users. Cognitive load measures indicate that participants reported having devoted more mental effort in the interaction when they had to use complex gestures.

A Competence-Based Course Authoring Concept for Learning Platforms with Legacy Assignment Tools

This paper is concerned with several of the most important aspects of Competence-Based Learning (CBL): course authoring, assignments, and categorization of learning content. The latter is part of the so-called Bologna Process (BP) and can effectively be supported by integrating knowledge resources like, e.g., standardized skill and competence taxonomies into the target implementation approach, aiming at making effective use of an open integration architecture while fostering the interoperability of hybrid knowledge-based e-learning solutions. Modern scenarios ask for interoperable software solutions to seamlessly integrate existing e-learning infrastructures and legacy tools with innovative technologies while being cognitively efficient to handle. In this way, prospective users are enabled to use them without learning overheads. At the same time, methods of Learning Design (LD) in combination with CBL are getting more and more important for production and maintenance of easy to facilitate solutions. We present our approach of developing a competence-based course-authoring and assignment support software. It is bridging the gaps between contemporary Learning Management Systems (LMS) and established legacy learning infrastructures by embedding existing resources via Learning Tools Interoperability (LTI). Furthermore, the underlying conceptual architecture for this integration approach will be explained. In addition, a competence management structure based on knowledge technologies supporting standardized skill and competence taxonomies will be introduced. The overall goal is to develop a software solution which will not only flawlessly merge into a legacy platform and several other learning environments, but also remain intuitively usable. As a proof of concept, the so-called platform independent conceptual architecture model will be validated by a concrete use case scenario.

De la cooperativa agroalimentaria a la "learning netchain". Hacia un planteamiento teorico interorganizativo e interpersonal

Se propone un planteamiento teórico/conceptual para determinar si las relaciones interorganizativas e interpersonales de la netchain de las cooperativas agroalimentarias evolucionan hacia una learning netchain. Las propuestas del trabajo muestran que el mayor grado de asociacionismo y la mayor cooperación/colaboración vertical a lo largo de la cadena están positivamente relacionados con la posición horizontal de la empresa focal más cercana del consumidor final. Esto requiere una planificación y una resolución de problemas de manera conjunta, lo que está positivamente relacionado con el mayor flujo y diversidad de la información/conocimiento obtenido y diseminado a lo largo de la netchain. Al mismo tiempo se necesita desarrollar un contexto social en el que fluya la información/conocimiento y las nuevas ideas de manera informal y esto se logra con redes personales y, principalmente, profesionales y con redes internas y, principalmente, externas. Todo esto permitirá una mayor satisfacción de los socios de la cooperativa agroalimentaria y de sus distribuidores y una mayor intensidad en I+D, convirtiéndose la netchain de la cooperativa agroalimentaria, así, en una learning netchain.

A hybrid harmony search with arithmetic crossover operation for economic dispatch

Economic dispatch (ED) problems often exhibit non-linear, non-convex characteristics due to the valve point effects. Further, various constraints and factors, such as prohibited operation zones, ramp rate limits and security constraints imposed by the generating units, and power loss in transmission make it even more challenging to obtain the global optimum using conventional mathematical methods. Meta-heuristic approaches are capable of solving non-linear, non-continuous and non-convex problems effectively as they impose no requirements on the optimization problems. However, most methods reported so far mainly focus on a specific type of ED problems, such as static or dynamic ED problems. This paper proposes a hybrid harmony search with arithmetic crossover operation, namely ACHS, for solving five different types of ED problems, including static ED with valve point effects, ED with prohibited operating zones, ED considering multiple fuel cells, combined heat and power ED, and dynamic ED. In this proposed ACHS, the global best information and arithmetic crossover are used to update the newly generated solution and speed up the convergence, which contributes to the algorithm exploitation capability. To balance the exploitation and exploration capabilities, the opposition based learning (OBL) strategy is employed to enhance the diversity of solutions. Further, four commonly used crossover operators are also investigated, and the arithmetic crossover shows its efficiency than the others when they are incorporated into HS. To make a comprehensive study on its scalability, ACHS is first tested on a group of benchmark functions with a 100 dimensions and compared with several state-of-the-art methods. Then it is used to solve seven different ED cases and compared with the results reported in literatures. All the results confirm the superiority of the ACHS for different optimization problems.

Machine learning methods for quantitative structure-property relationship modeling

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2014

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.

Online problem-based learning : perceptions of nursing educators /

A qualitative study was conducted to detennine 5 nursing educators' perceptions about the online application of a problem-based learning strategy in undergraduate nursing education. The question asked in the study was: Can the essential elements of face-to-face problem-based learning be supported in an online format? The data for this study came from 2 individual tape-recorded interviews with each of the 5 participants over a 3-month period and from a researchjournaI. The educators felt that student-centered learning and critical thinking could be supported within an online format. However, they noted that challenges could exist in terms of developing tutor roles, fostering student self-direction, facilitating group process and connections, and incorporating a nursing philosophy of online learning. The importance of tailoring an online problem-based learning course to reflect educators' philosophies and values in nursing emerged as an important theme from the interview responses. Overall, the participants suggested that an ideal environment would blend both face-to-face and online elements and that fewer elements would be offered in the first 2 years of the nursing program. They described a hybrid model of problem-based learning in which the online component could be used to support face-to-face sessions.

Web based content and hybrid teaching: student perceptions of the effectiveness of using web based content and hyper-linked teaching units in teaching hybrid business and marketing post secondary classes /

This exploratory, descriptive action research study is based on a survey of a sample of convenience consisting of 172 college and university marketing students, and 5 professors who were experienced in teaching in an internet based environment. The students that were surveyed were studying e-commerce and international business in 3^^ and 4*'' year classes at a leading imiversity in Ontario and e-commerce in 5^ semester classes at a leading college. These classes were taught using a hybrid teaching style with the contribution of a large website that contained pertinent text and audio material. Hybrid teaching employs web based course materials (some in the form of Learning Objects) to deliver curriculimi material both during the attended lectures and also for students accessing the course web page outside of class hours. The survey was in the form on an online questionnaire. The research questions explored in this study were: 1. What factors influence the students' ability to access and learn from web based course content? 2. How likely are the students to use selected elements of internet based curriculum for learning academic content? 3. What is the preferred physical environment to facilitate learning in a hybrid environment? 4. How effective are selected teaching/learning strategies in a hybrid environment? The findings of this study suggest that students are very interested in being part of the learning process by contributing to a course web site. Specifically, students are interested in audio content being one of the formats of online course material, and have an interest in being part of the creation of small audio clips to be used in class.