When writing long documents in LateX like Thesis or books you might want to implement bibliographies on a chapter by chapter basis. This end of chapter bibliographies can be achieved using the bibunits1 package. Here’s an example
\documentclass{article}\usepackage{bibunits}\defaultbibliography{references}\begin{document}\bibliographyunit[\section]\section{Introduction}
References to some work~\cite{Rodrigues:2014}
that is of the most scientific interest and
is based on his previous work~\cite{Rodrigues:2012}.
\putbib\section{The End}
A new section with a new bibligaphy~\cite{Rodrigues:2013}
at the end, but all the science of complexity
any human could have~\cite{Rodrigues:2010}.
\putbib\end{document}
\documentclass{article}
\usepackage{bibunits}
\defaultbibliography{references}\begin{document}
\bibliographyunit[\section]\section{Introduction}
References to some work~\cite{Rodrigues:2014}
that is of the most scientific interest and
is based on his previous work~\cite{Rodrigues:2012}.
\putbib
\section{The End}
A new section with a new bibligaphy~\cite{Rodrigues:2013}
at the end, but all the science of complexity
any human could have~\cite{Rodrigues:2010}.
\putbib\end{document}
The main trick is to define how to divide the bibliographies and to compile each of the sub bibliography files generated by latex. Read the documentation for details!
The past decade has seen the rapid development of the online newsroom. News published online are the main outlet of news surpassing traditional printed newspapers. This poses challenges to the production and to the consumption of those news. With those many sources of information available it is important to find ways to cluster and organise the documents if one wants to understand this new system.
Traditional approaches to the problem of clustering documents usually embed the documents in a suitable similarity space. Previous studies have reported on the impact of the similarity measures used for clustering of textual corpora. These similarity measures usually are calculated for bag of words representations of the documents. This makes the final document-word matrix high dimensional. Feature vectors with more than 10,000 dimensions are common and algorithms have severe problems with the high dimensionality of the data.
A novel bio inspired approach to the problem of traversing the news is presented. It finds Hamiltonian cycles over documents published by the newspaper The Guardian. A Second Order Swarm Intelligence algorithm based on Ant Colony Optimisation was developed that uses a negative pheromone to mark unrewarding paths with a “no-entry” signal. This approach follows recent findings of negative pheromone usage in real ants .
In this case study the corpus of data is represented as a bipartite relation between documents and keywords entered by the journalists to characterise the news. A new similarity measure between documents is presented based on the Q- analysis description of the simplicial complex formed between documents and keywords. The eccentricity between documents (two simplicies) is then used as a novel measure of similarity between documents.
The results prove that the Second Order Swarm Intelligence algorithm performs better in benchmark problems of the travelling salesman problem, with faster convergence and optimal results. The addition of the negative pheromone as a non-entry signal clearly improved the quality of the solutions. The application of the algorithm to the corpus of news of The Guardian creates a coherent navigation system among the news. This allows the users to navigate the news published during a certain period of time in a semantic sequence instead of a time sequence.
This work as broader application as it can be applied to many cases where the data is mapped to bipartite relations (e.g. protein expressions in cells, sentiment analysis, brand awareness in social media, routing problems), as it highlights the connectivity of the underlying complex system.
Peer Assessment in Architecture Education – Brno – ICTPI'14 – Mafalda Teixeira de Sampayo, David Sousa-Rodrigues, Cristian Jimenez-Romero, and Jeffrey Johnson
The role of peer assessment in education has become of particular interest in recent years, mainly because of its potential benefits in improving student’s learning and benefits in time management by allowing teachers and tutors to use their time more efficiently to get the results of student’s assessments quicker. Peer assessment has also relevant in the context of distance learning and massive open online courses (MOOCs).
The discipline of architecture is dominated by an artistic language that has its own way of being discussed and applied. The architecture project analysis and criticism goes beyond the technical components and programme requirements that need to be fulfilled. Dominating the architecture language is an essential tool in the architect’s toolbox. In this context peer assessment activities can help them develop skills early in their undergraduate education.
In this work we show how peer assessment acts as a formative activity in architecture teaching. Peer assessment leads the students to develop critical and higher order thinking processes that are fundamental for the analysis of architecture projects. The applicability of this strategy to massive open online education systems has to be considered as the heterogeneous and unsupervised environment requires confidence in the usefulness of this approach. To study this we designed a local experiment to investigate the role of peer experiment in architecture teaching.
This experiment showed that students reacted positively to the peer assessment exercise and looked forward to participating when it was announced. Previously to the assessment students felt engaged by the responsibility of marking their colleagues. Subsequently to the first iteration of the peer assessment, professors registered that students used elements of the qualitative assessment in their architecture discourse, and tried to answer the criticisms pointed to their projects by their colleagues. This led their work in directions some hadn’t considered before.
The marks awarded by the students are in good agreement with the final scores awarded by the professors. Only in 5 cases the average score of the peer assessment differed more than 10% from marks given by the professors. It was also observed that the professor’s marks where slightly higher than the average of the peer marking. No correlation was observed between the marks given by a student as marker and the final score given to that student by the professors.
The data produced in this experiment shows peer assessment as a feedback mechanism in the construction of a critical thought process and in the development of an architectural discourse. Also it shows that students tend to mark their colleagues with great accuracy. Both of these results are of great importance for possible application of peer assessment strategies to massive open online courses and distance education.
Todos os anos chega esta altura e começa a discussão de quem é o melhor na angariação de alunos para os respectivos cursos de ensino de Arquitectura. Normalmente os jornais mostram apenas as notas dos últimos classificados e aí estala a discussão. A única forma de saber quem conseguiu atrair os melhores alunos de Arquitectura é mesmo comparar todos os que entraram para a universidade.
Um a um, comparar o primeiro com o primeiro, o segundo com o segundo, etc… desta forma evitam-se as notas do último aluno, que por vezes entraram por contigente especial, e evita-se a confusão de diferentes cursos terem diferentes números clausus. A única forma de saber qual é o melhor curso de arquitectura português é olhar para os dados totais. E depois perguntar-se honestamente em que linha daquele gráfico é que gostava de estar!
Uma versão sumariada do gráfico (Boxplot) acima ajuda a perceber ainda melhor:
Sem dúvida alguma que no panorama do Ensino da Arquitectura em Portugal o Porto continua a ser a que atrai os melhores alunos seguido-se os cursos de Lisboa, Coimbra e Minho. As universidades mais periféricas naturalmente são as que atraem menos os melhores alunos.
Mas se precisam realmente de um número, o boneco acima mostra que se calhar é muito mais interessante utilizar a média das notas de entrada do que a nota do último aluno:
Porto 186.1
ISCTE 173.5
Lisboa-IST 167.4
Minho 163.7
Lisboa-FA 156.9
Coimbra 156.0
UBI 136.5
Évora 133.6
Açores 132.6
Formatting text and labels in ggplot or ggplot2 axis is easy. A common task when producing plots for publication is to replace default labels. Default labels in axes tend to reflect the name of variables used and sometimes these are not the most descriptive labels. At least not when you are publishing the plots in a scientific journal. So let’s try to break down some ways to personalise ggplot plot axes.
For this formatting example I’ll use the movies dataset that is available in R. First thing we need to do is to load ggplot2 library and then the movies dataset
library(ggplot2)data(movies)
library(ggplot2)
data(movies)
The default ggplot axis labels
Traditionally the labels are set in the axis directly by ggplot from the aesthetics selected e.g.:
To make ggplot axes’ labels different we can use xlab and ylab. This defines x and y axis in ggplot easily.
p0+xlab('The glorious years of the movies')+ylab('The public ratings')
p0+xlab('The glorious years of the movies')+ylab('The public ratings')
Setting axes labels in ggplot with scales
p0+
scale_x_continuous('The glorious years of the movies (with scales)')+
scale_y_continuous('The public ratings (with scales)')
p0+
scale_x_continuous('The glorious years of the movies (with scales)')+
scale_y_continuous('The public ratings (with scales)')
Also worth investigating is the labs function that allow the change of the axes and the title e.g.:
p0+labs(
x='The glorious years of the movies (with labs)',
y='The public ratings (with labs)')
p0+labs(
x='The glorious years of the movies (with labs)',
y='The public ratings (with labs)'
)
Formatting labels text for size and rotation?
Ggplot can change axis label orientation, size and colour. To rotate the axes in ggplot you just add the angle property. To change size ou use size and for colour you uses color (Notice that a ggplot uses US-english spelling). Finally, note that you can use the face property to define if the font is bold or italic.
p0 + xlab('The Years of Cinema')+
ylab('Public Ratings')+
theme(
axis.text.x=element_text(angle=90, size=8),
axis.title.x=element_text(angle=10, color='red'),
axis.title.y=element_text(angle=80, color='blue', face='bold', size=14))
p0 + xlab('The Years of Cinema')+
ylab('Public Ratings')+
theme(
axis.text.x=element_text(angle=90, size=8),
axis.title.x=element_text(angle=10, color='red'),
axis.title.y=element_text(angle=80, color='blue', face='bold', size=14)
)
The formatting of the text in the labels is a bit counter intuitive because it uses a slightly different nomenclature. The formatting is done with the theme function and by defining element_text’s with the wanted format. In the example above the axis.text.x defines the ticks format and the axis.title.? define the labels format.
A good way to learn all the elements that a ggplot theme can format can be obtained from the help menu by entering ?theme. These examples are just scrapping the surface of what you can do but hope they can get you started in formatting text size and orientation inside ggplot plots.
Side Note: Did you noticed how crappy the movies from the 70s, 80s and 90s were?
Still, UNESCO reports that 250 million students worldwide cannot read, write, or count, even after four years of school. Close to 775 million adults– 64% of whom are women–still lack reading and writing skills, with the lowest rates in Sub-Saharan Africa and South and West Asia.
As leis islâmicas al-fikh abrangem a organização urbana das cidades islâmicas; não segue à risca as imposições de um plano, defende os interesses da família, e revela um modo autossuficiente de fazer cidade, sustentado por suas próprias leis. via arquitextos 169.04 oriente: Os regulamentos da cidade islâmica | vitruvius.
Millions of learners on platforms like edX and Coursera are generating terabytes of data tracking their activity in real time. Online learning platforms capture extraordinarily detailed records of student behavior, and now the challenge for researchers is to explore how these new datasets can be used to advance the science of learning.In this edX co-sponsored talk Justin Reich — educational researcher, co-founder of EdTechTeacher, and Berkman Fellow — examines current trends and future directions in research into online learning in large-scale settings. via Education Week
