Host-based P2P flow identification and use in real-time

Data identification is a key task for any Internet Service Provider (ISP) or network administrator. As port fluctuation and encryption become more common in P2P traffic wishing to avoid identification, new strategies must be developed to detect and classify such flows. This paper introduces a new method of separating P2P and standard web traffic that can be applied as part of a data mining process, based on the activity of the hosts on the network. Unlike other research, our method is aimed at classifying individual flows rather than just identifying P2P hosts or ports. Heuristics are analysed and a classification system proposed. The accuracy of the system is then tested using real network traffic from a core internet router showing over 99% accuracy in some cases. We expand on this proposed strategy to investigate its application to real-time, early classification problems. New proposals are made and the results of real-time experiments compared to those obtained in the data mining research. To the best of our knowledge this is the first research to use host based flow identification to determine a flows application within the early stages of the connection.

Using location-aware technology for learning Geography in a real digital space outside the classroom

The use of new mobile technologies is still in its infancy in many secondary schools and there is limited evidence of the educational and pedagogical benefits on pupils’ learning in the formal school context. This qualitative study focuses on the use of handheld devices to teach a topic in geography to an examination class. Action research combined with pupil observations and focus group interviews are used to capture the pupils’ experiences of using mediascapes. Activity Theory is used as a lens to structure the analysis of the data and to report on the cognitive and affective impact of m-learning on pupils’ academic performance in the topic. Increased attainment and the development of wider skills for lifelong learning were identified in the study. The adaptability of the majority of pupils to the technology resulted in increased levels of willingness to learn in this novel context.

Economies of scaling: More evidence that allometry of metabolism is linked to activity, metabolic rate and habitat

Organismal metabolic rates influence many ecological processes, and the mass-specific metabolic rate of organisms decreases with increasing body mass according to a power law. The exponent in this equation is commonly thought to be the three-quarter-power of body mass, determined by fundamental physical laws that extend across taxa. However, recent work has cast doubt as to the universality of this relationship, the value of 0.75 being an interspecies 'average' of scaling exponents that vary naturally between certain boundaries. There is growing evidence that metabolic scaling varies significantly between even closely related species, and that different values can be associated with lifestyle, activity and metabolic rates. Here we show that the value of the metabolic scaling exponent varies within a group of marine ectotherms, chitons (Mollusca: Polyplacophora: Mopaliidae), and that differences in the scaling relationship may be linked to species-specific adaptations to different but overlapping microhabitats. Oxygen consumption rates of six closely related, co-occurring chiton species from the eastern Pacific (Vancouver Island, British Columbia) were examined under controlled experimental conditions. Results show that the scaling exponent varies between species (between 0.64 and 0.91). Different activity levels, metabolic rates and lifestyle may explain this variation. The interspecific scaling exponent in these data is not significantly different from the archetypal 0.75 value, even though five out of six species-specific values are significantly different from that value. Our data suggest that studies using commonly accepted values such as 0.75 derived from theoretical models to extrapolate metabolic data of species to population or community levels should consider the likely variation in exponents that exists in the real world, or seek to encompass such error in their models. This study, as in numerous previous ones, demonstrates that scaling exponents show large, naturally occurring variation, and provides more evidence against the existence of a universal scaling law. © 2012 Elsevier B.V.

Indoor and Outdoor monitoring of photocatalytic activity using a mobile phone app. and a photocatalytic activity indicator ink (paii)

A resazurin (Rz) based photocatalyst activity indicator ink (paii) is used to test the activity of commercial self-cleaning materials. The semiconductor photocatalyst driven colour change of the ink is monitored indoors and outside using a simple mobile phone application that measures the RGB colour components of the digital image of the paii-covered, irradiated sample in real time. The results correlate directly with those generated using a traditional, lab-bound method of analysis (UV–vis spectrophotometry).

Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments

Abstract
Publicly available, outdoor webcams continuously view the world and share images. These cameras include traffic cams, campus cams, ski-resort cams, etc. The Archive of Many Outdoor Scenes (AMOS) is a project aiming to geolocate, annotate, archive, and visualize these cameras and images to serve as a resource for a wide variety of scientific applications. The AMOS dataset has archived over 750 million images of outdoor environments from 27,000 webcams since 2006. Our goal is to utilize the AMOS image dataset and crowdsourcing to develop reliable and valid tools to improve physical activity assessment via online, outdoor webcam capture of global physical activity patterns and urban built environment characteristics.
This project’s grand scale-up of capturing physical activity patterns and built environments is a methodological step forward in advancing a real-time, non-labor intensive assessment using webcams, crowdsourcing, and eventually machine learning. The combined use of webcams capturing outdoor scenes every 30 min and crowdsources providing the labor of annotating the scenes allows for accelerated public health surveillance related to physical activity across numerous built environments. The ultimate goal of this public health and computer vision collaboration is to develop machine learning algorithms that will automatically identify and calculate physical activity patterns.