Participatory content creation and collective intelligence in Rio de Janeiro's favelas

This proposal combines ethnographic techniques and discourse studies to investigate a collective of people engaged with audiovisual productions who collaborate in Curta Favela’s workshops in Rio de Janeiro’s favelas. ‘Favela’ is often translated simply as ‘slum’ or ‘shantytown’, but these terms connote negative characteristics such as shortage, poverty, and deprivation which end up stigmatizing these low income suburbs. Curta Favela (Favela Shorts) is an independent project in which all participants join to use photography and participatory audiovisual production as tools for social change and to raise consciousness. As cameras are not affordable for favela dwellers, Curta Favela’s volunteers teach favela residents how they can use their mobile phones and compact cameras to take pictures and make movies, and afterwards, how they can edit the data using free editing video software programs and publish it on the Internet. To record audio, they use their mp3 or mobile phones. The main aim of this study is to shed light not only on how this project operates, but also to highlight how collective intelligence can be used as a way of fighting against a lack of basic resources.

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

How to design extended finite state machine test models in Java

This chapter is a tutorial that teaches you how to design extended finite state machine (EFSM) test models for a system that you want to test. EFSM models are more powerful and expressive than simple finite state machine (FSM) models, and are one of the most commonly used styles of models for model-based testing, especially for embedded systems. There are many languages and notations in use for writing EFSM models, but in this tutorial we write our EFSM models in the familiar Java programming language. To generate tests from these EFSM models we use ModelJUnit, which is an open-source tool that supports several stochastic test generation algorithms, and we also show how to write your own model-based testing tool. We show how EFSM models can be used for unit testing and system testing of embedded systems, and for offline testing as well as online testing.

Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution

Power system stabilizer (PSS) is one of the most important controllers in modern power systems for damping low frequency oscillations. Many efforts have been dedicated to design the tuning methodologies and allocation techniques to obtain optimal damping behaviors of the system. Traditionally, it is tuned mostly for local damping performance, however, in order to obtain a globally optimal performance, the tuning of PSS needs to be done considering more variables. Furthermore, with the enhancement of system interconnection and the increase of system complexity, new tools are required to achieve global tuning and coordination of PSS to achieve optimal solution in a global meaning. Differential evolution (DE) is a recognized as a simple and powerful global optimum technique, which can gain fast convergence speed as well as high computational efficiency. However, as many other evolutionary algorithms (EA), the premature of population restricts optimization capacity of DE. In this paper, a modified DE is proposed and applied for optimal PSS tuning of 39-Bus New-England system. New operators are introduced to reduce the probability of getting premature. To investigate the impact of system conditions on PSS tuning, multiple operating points will be studied. Simulation result is compared with standard DE and particle swarm optimization (PSO).

Supplementary Information : Hogan, Holland, Holloway, Petit and Read : Read Classification for Next Generation Sequencing, ESANN 2013, April 2013

This item provides supplementary materials for the paper mentioned in the title, specifically a range of organisms used in the study. The full abstract for the main paper is as follows: Next Generation Sequencing (NGS) technologies have revolutionised molecular biology, allowing clinical sequencing to become a matter of routine. NGS data sets consist of short sequence reads obtained from the machine, given context and meaning through downstream assembly and annotation. For these techniques to operate successfully, the collected reads must be consistent with the assumed species or species group, and not corrupted in some way. The common bacterium Staphylococcus aureus may cause severe and life-threatening infections in humans,with some strains exhibiting antibiotic resistance. In this paper, we apply an SVM classifier to the important problem of distinguishing S. aureus sequencing projects from alternative pathogens, including closely related Staphylococci. Using a sequence k-mer representation, we achieve precision and recall above 95%, implicating features with important functional associations.

Identity theft, computers and behavioral biometrics

The increase of online services, such as eBanks, WebMails, in which users are verified by a username and password, is increasingly exploited by Identity Theft procedures. Identity Theft is a fraud, in which someone pretends to be someone else is order to steal money or get other benefits. To overcome the problem of Identity Theft an additional security layer is required. Within the last decades the option of verifying users based on their keystroke dynamics was proposed during login verification. Thus, the imposter has to be able to type in a similar way to the real user in addition to having the username and password. However, verifying users upon login is not enough, since a logged station/mobile is vulnerable for imposters when the user leaves her machine. Thus, verifying users continuously based on their activities is required. Within the last decade there is a growing interest and use of biometrics tools, however, these are often costly and require additional hardware. Behavioral biometrics, in which users are verified, based on their keyboard and mouse activities, present potentially a good solution. In this paper we discuss the problem of Identity Theft and propose behavioral biometrics as a solution. We survey existing studies and list the challenges and propose solutions.

Semantic labelling for document feature patterns using ontological subjects

Finding and labelling semantic features patterns of documents in a large, spatial corpus is a challenging problem. Text documents have characteristics that make semantic labelling difficult; the rapidly increasing volume of online documents makes a bottleneck in finding meaningful textual patterns. Aiming to deal with these issues, we propose an unsupervised documnent labelling approach based on semantic content and feature patterns. A world ontology with extensive topic coverage is exploited to supply controlled, structured subjects for labelling. An algorithm is also introduced to reduce dimensionality based on the study of ontological structure. The proposed approach was promisingly evaluated by compared with typical machine learning methods including SVMs, Rocchio, and kNN.

Exploring intercultural and ethical understanding through ‘Ethical Intelligence’ and drama, in Asian texts for the Australian curriculum : English

This paper focuses on Australian texts with Asian representations, which will be discussed in terms of Ethical Intelligence (Weinstein, 2011) explored through drama. This approach aligns with the architecture of the Australian Curriculum: English (AC:E, v5, 2013); in particular the general capabilities of 'ethical understanding' and 'intercultural understandings.' It also addresses one aspect of the Cross Curriculum Priorities which is to include texts about peoples from Asia. The selected texts not only show the struggles undergone by the authors and protagonists, but also the positive contributions that diverse writers from Asian and Middle Eastern countries have made to Australia.

Energy efficient virtual machine placement in data centers

Electricity cost has become a major expense for running data centers and server consolidation using virtualization technology has been used as an important technology to improve the energy efficiency of data centers. In this research, a genetic algorithm and a simulation-annealing algorithm are proposed for the static virtual machine placement problem that considers the energy consumption in both the servers and the communication network, and a trading algorithm is proposed for dynamic virtual machine placement. Experimental results have shown that the proposed methods are more energy efficient than existing solutions.

Using real-time authentic online learning scenarios to teach criminal intelligence

Criminal intelligence is an area of expertise highly sought-after internationally and within a variety of justice-related professions; however, producing university graduates with the requisite professional knowledge, as well as analytical, organisational and technical skills presents a pedagogical and technical challenge to university educators. The situation becomes even more challenging when students are undertaking their studies by distance education. This best practice session showcases the design of an online undergraduate unit for final year justice students which uses an evolving real-time criminal scenario as the focus of authentic learning activities in order to prepare students for graduate roles within the criminal intelligence and justice professions. Within the unit, students take on the role of criminal intelligence analysts, applying relevant theories, models and strategies to solve a complex but realistic crime and complete briefings and documentation to industry standards as their major summative assessment task. The session will demonstrate how the design of the online unit corresponds to authentic learning principles, and will specifically map the elements of the unit design to Herrington & Oliver’s instructional design framework for authentic learning (2000; Herrington & Herrington 2006). The session will show how a range of technologies was used to create a rich learning experience for students that could be easily maintained over multiple unit iterations without specialist technical support. The session will also discuss the unique pedagogical affordances and challenges implicated in the location of the unit within an online learning environment, and will reflect on some of the lessons learned from the development which may be relevant to other authentic online learning contexts.

Using real-time authentic online learning scenarios to teach criminal intelligence : presentation

Criminal intelligence is an area of expertise highly sought-after internationally and within a variety of justice-related professions; however, producing university graduates with the requisite professional knowledge, as well as analytical, organisational and technical skills presents a pedagogical and technical challenge to university educators. The situation becomes even more challenging when students are undertaking their studies by distance education. This best practice session showcases the design of an online undergraduate unit for final year justice students which uses an evolving real-time criminal scenario as the focus of authentic learning activities in order to prepare students for graduate roles within the criminal intelligence and justice professions. Within the unit, students take on the role of criminal intelligence analysts, applying relevant theories, models and strategies to solve a complex but realistic crime and complete briefings and documentation to industry standards as their major summative assessment task. The session will demonstrate how the design of the online unit corresponds to authentic learning principles, and will specifically map the elements of the unit design to Herrington & Oliver’s instructional design framework for authentic learning (2000; Herrington & Herrington 2006). The session will show how a range of technologies was used to create a rich learning experience for students that could be easily maintained over multiple unit iterations without specialist technical support. The session will also discuss the unique pedagogical affordances and challenges implicated in the location of the unit within an online learning environment, and will reflect on some of the lessons learned from the development which may be relevant to other authentic online learning contexts.

To what extent is the Mayer and Salovey (1997) model of emotional intelligence a useful predictor of leadership style and perceived leadership outcomes in Australian educational institutions?

Researchers have found that transformational leadership is related to positive outcomes in educational institutions. Hence, it is important to explore constructs that may predict leadership style in order to identify potential transformational leaders in assessment and selection procedures. Several studies in non-educational settings have found that emotional intelligence is a useful predictor of transformational leadership, but these studies have generally lacked methodological rigor and contextual relevance. This project, set in Australian educational institutions, employed a more rigorous methodology to answer the question: to what extent is the Mayer and Salovey (1997) model of emotional intelligence a useful predictor of leadership style and perceived leadership outcomes? The project was designed to move research in the field forward by using valid and reliable instruments, controlling for other predictors, obtaining an adequately sized sample of current leaders and collecting multiple ratings of their leadership behaviours. The study (N = 144 leaders and 432 raters) results indicated that emotional intelligence was not a useful predictor of leadership style and perceived leadership outcomes. In contrast, several of the other predictors in the study were found to predict leadership style.

The intervening role of Agreeableness in the relationship between trait emotional intelligence and Machiavellianism : reassessing the potential dark side of EI

Previous research into the potential ‘dark’ side of trait emotional intelligence (EI) has repeatedly demonstrated that trait EI is negatively associated with Machiavellianism. In this study, we reassess the potential dark side of trait EI, by testing whether Agreeableness mediates and/or moderates the relationship between trait EI and Machiavellianism. Hypothesized mediation and moderation effects were tested using a large sample of 884 workers who completed several self-report questionnaires. Results provide support for both hypotheses; Agreeableness was found to mediate and moderate the relationship between trait EI and Machiavellianism. Overall, results indicate that individuals high in trait EI tend to have low levels of Machiavellianism because they generally have a positive nature (i.e. are agreeable) and not because they are emotionally competent per se. Results also indicate that individuals high in ‘perceived emotional competence’ have the potential to be high in Machiavellianism, particularly when they are low in Agreeableness.

Timed Probabilistic Automaton : a bridge between Raven and Song Scope for automatic species recognition

Raven and Song Scope are two automated sound anal-ysis tools based on machine learning technique for en-vironmental monitoring. Many research works have been conducted upon them, however, no or rare explo-ration mentions about the performance and comparison between them. This paper investigates the comparisons from six aspects: theory, software interface, ease of use, detection targets, detection accuracy, and potential application. Through deep exploration one critical gap is identified that there is a lack of approach to detect both syllables and call structures, since Raven only aims to detect syllables while Song Scope targets call structures. Therefore, a Timed Probabilistic Automata (TPA) system is proposed which separates syllables first and clusters them into complex structures after.

The primary school as a logic machine

This paper makes a case for thinking about the primary school as a logic machine (apparatus) as a way of thinking about processes of in-school stratification. Firstly we discuss related literature on in-school stratification in primary schools, particularly as it relates to literacy learning. Secondly we explain how school reform can be thought about in terms of the idea of the machine or apparatus. In which case the processes of in-school stratification can be mapped as more than simply concerns about school organisation (such as students grouping) but also involve a politics of truth, played out in each school, that constitutes school culture and what counts as ‘good’ pedagogy. Thirdly, the chapter will focus specifically on research conducted into primary schools in the Northern Suburbs of Adelaide, one of the most educationally disadvantaged regions in Australia, as a case study of the relationship between in-school stratification and the reproduction of inequality. We will draw from more than 20 years of ethnographic work in primary school in the northern suburbs of Adelaide and provide a snapshot of a recent attempt to improve literacy achievement in a few Northern Suburbs public primary schools (SILA project). The SILA project, through diagnostic reviews, has provided a significant analysis of the challenges facing policy and practice in such challenging school contexts that also maps onto existing (inter)national research. These diagnostic reviews said ‘hard things’ that required attention by SILA schools and these included: · an over reliance on whole class, low level, routine tasks and hence a lack of challenge and rigour in the learning tasks offered to students ; · a focus on the 'code breaking' function of language at the expense of richer conceptualisations of literacy that might guide teachers’ understanding of challenging pedagogies ; · the need for substantial shifts in the culture of schools, especially unsettling deficit views of students and their communities ; · a need to provide a more ‘consistent’ approach to teaching literacy across the school; · a need to focus School Improvement Plans in order to implement a clear focus on literacy learning; and, · a need to sustain professional learning to produce new knowledge and practice . The paper will conclude with suggestions for further research and possible reform projects into the primary school as a logic machine.