Practical Activities. Subject: Ecology. Undergraduate Studies in Biology and Marine Sciences. Ecology Department

Notebook of practical activities in Ecology during the 2on course of Biology career.

STORM - a novel information fusion and cluster interpretation technique

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.

Capital structure, risk and asymmetric information : theory and evidence

This paper proposes a principal-agent model between banks and firms with risk and asymmetric information. A mixed form of finance to firms is assumed. The capital structure of firms is a relevant cause for the final aggregate level of investment in the economy. In the model analyzed, there may be a separating equilibrium, which is not economically efficient, because aggregate investments fall short of the first-best level. Based on European firm-level data, an empirical model is presented which validates the result of the relevance of the capital structure of firms. The relative magnitude of equity in the capital structure makes a real difference to the profits obtained by firms in the economy.

Landscaping on shifting ground: teacher education in a digitally transforming world

For almost three decades, the landscape of teacher education has been modestly shaped by the exploration of practices that made use of what were, at the time, current instances of computing and communication technologies (CCTs). More broadly and importantly however, the deployment of CCTs globally has, over the same time period (1980-2008), supported a reshaping of the planet's social, economic and political circumstances in which all forms of education operate. Despite the enormity of these shifts the focus in teacher education has remained largely at site, reflecting a similar focus in schools. In fact, the patterns of adaption and response in teacher education to each new instance of high-tech product are now quite predictable. Thus, while teacher education's engagement with CCTs can be mapped as a kind of minor landscaping, a process which attends more to appearance than substance, it is landscaping effectively premised upon a stable geography, one that resembles that of thirty years ago. This paper explores the changed and changing geography of a world heavily shaped by the ongoing deployment and use of more and more powerful CCTs. The analysis suggests that if we continue to attend only to landscaping, teacher education will be at risk of being terraformed.

An overview of Fog computing and its security issues

Fog computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage and application services to end users. In this article, we elaborate the motivation and advantages of Fog computing and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state of the art of Fog computing and similar work under the same umbrella. Distinguished from other reviewing work of Fog computing, this paper further discloses the security and privacy issues according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of system security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device. In addition, we discuss the authentication and authorization techniques that can be used in Fog computing. An example of authentication techniques is introduced to address the security scenario where the connection between Fog and Cloud is fragile.

Integrating internet-of-things with the power of cloud computing and the intelligence of big Data analytics- a three layered approach

This paper is written through the vision on integrating Internet-of-Things (IoT) with the power of Cloud Computing and the intelligence of Big Data analytics. But integration of all these three cutting edge technologies is complex to understand. In this research we first provide a security centric view of three layered approach for understanding the technology, gaps and security issues. Then with a series of lab experiments on different hardware, we have collected performance data from all these three layers, combined these data together and finally applied modern machine learning algorithms to distinguish 18 different activities and cyber-attacks. From our experiments we find classification algorithm RandomForest can identify 93.9% attacks and activities in this complex environment. From the existing literature, no one has ever attempted similar experiment for cyber-attack detection for IoT neither with performance data nor with a three layered approach.

Using Amos graphics to enhance the understanding and communication of multiple regression

Support for the saying “a picture is worth a 1000 words’ has been consistently found within statistics education. Graphical images are effective in promoting understanding and communication of statistical concepts and results to a variety of audiences. The computer software package, AMOS, was developed for the analysis of structural equation models (SEM) and has a user-friendly graphical interface. However, courses in SEM are generally found only at the postgraduate level. This paper argues that the graphical interface of AMOS has the potential to enhance conceptual understanding and communication of results in undergraduate statistical courses. More specifically, approaches to the teaching and communication of results of multiple regression models when using SPSS and AMOS will be examined and compared.