Profiling user activities with minimal traffic traces

Understanding user behavior is essential to personalize and enrich a user's online experience. While there are significant benefits to be accrued from the pursuit of personalized services based on a fine-grained behavioral analysis, care must be taken to address user privacy concerns. In this paper, we consider the use of web traces with truncated URLs - each URL is trimmed to only contain the web domain - for this purpose. While such truncation removes the fine-grained sensitive information, it also strips the data of many features that are crucial to the profiling of user activity. We show how to overcome the severe handicap of lack of crucial features for the purpose of filtering out the URLs representing a user activity from the noisy network traffic trace (including advertisement, spam, analytics, webscripts) with high accuracy. This activity profiling with truncated URLs enables the network operators to provide personalized services while mitigating privacy concerns by storing and sharing only truncated traffic traces. In order to offset the accuracy loss due to truncation, our statistical methodology leverages specialized features extracted from a group of consecutive URLs that represent a micro user action like web click, chat reply, etc., which we call bursts. These bursts, in turn, are detected by a novel algorithm which is based on our observed characteristics of the inter-arrival time of HTTP records. We present an extensive experimental evaluation on a real dataset of mobile web traces, consisting of more than 130 million records, representing the browsing activities of 10,000 users over a period of 30 days. Our results show that the proposed methodology achieves around 90% accuracy in segregating URLs representing user activities from non-representative URLs

Efficient AUC Optimization for Information Ranking Applications

Adequate evaluation of an information retrieval system to estimate future performance is a crucial task. Area under the ROC curve (AUC) is widely used to evaluate the generalization of a retrieval system. However, the objective function optimized in many retrieval systems is the error rate and not the AUC value. This paper provides an efficient and effective non-linear approach to optimize AUC using additive regression trees, with a special emphasis on the use of multi-class AUC (MAUC) because multiple relevance levels are widely used in many ranking applications. Compared to a conventional linear approach, the performance of the non-linear approach is comparable on binary-relevance benchmark datasets and is better on multi-relevance benchmark datasets.Comment: 12 page

The perceived psychological responsibilities of a strength and conditioning coach

Research is limited in exploring the specific psychology-oriented responsibilities of the strength and conditioning professional. The present research explored the psychological responsibilities adopted by accredited strength and conditioning coaches. Participants comprised 10 coaches working within the United Kingdom, 3 within the United States, and 5 within Australia offering a cross-section of experience from various sport disciplines and educational backgrounds. Participants were interviewed either in person or via Skype. Thematic clustering was followed using interpretative phonological analysis to identify common themes. Over half (61%) of the respondents reported that their position as a strength and conditioning coach required additional psychology-oriented responsibilities. These comprised a counseling role in the absence of a psychologist and the use of "softer skills" in a mentoring role to the athlete during a challenging situation. The coach could play an influential role in shaping the mentality of the team. The coach identifies how the role results in working to relay information from the athlete to other support staff and similarly from the support staff to the athlete. In addition to identifying the resonant psychology-oriented responsibilities, discussion is made with specific focus on the ethical boundary within which strength and conditioning coaches must reside regarding the competencies to provide psychological support

Nanostructures and super-hydrophobic properties on the leaves of an indigenous Australian plant Eucalyptus pleurocarpa

This study presents the results of a topographical survey of the surface features found on the leaves of an indigenous Australian plant Eucalyptus pleurocarpa (Tallerack). Field emission scanning electron microscopy was used to examine the size and morphology of various micrometer and nanometre scale features presented on the leaf surface. In particular, the features formed by the epicuticular waxes were investigated and quantified. Analysis of water contact angle measurements carried out on the adaxial surface indicated that the leaf surface was super-hydrophobic (158.00 ± 4.30°), while the abaxial surface was found to be hydrophobic (150.20 ± 3.90°). Microscopy examination revealed that the leaf surfaces contained an array of stomata surrounded by a rugged surface region dominated by a rim and bumps. The stomata rims and surface bumps surrounding the stomata were adorned with nanometre scale pillar structures. On the adaxial surface the mean diameter of these pillar structures was estimated to be 300 ± 50 nm and lengths ranging from 1 to 7µm. While the self-cleaning experiments demonstrated that the Tallerack leaf could be effectively cleaned using a fine spray of water droplets that rolled over the surface picking up both hydrophilic (Ballotini microspheres) and hydrophobic (carbon black toner) contaminants

A rational model of preference learning and choice prediction by children

Young children demonstrate the ability to make inferences about the preferences of other agents based on their choices. However, there exists no overarching account of what children are doing when they learn about preferences or how they use that knowledge. We use a rational model of preference learning, drawing on ideas from economics and computer science, to explain the behavior of children in several recent experiments. Specifically, we show how a simple econometric model can be extended to capture two- to four-year-olds’ use of statistical information in inferring preferences, and their generalization of these preferences

Deep retrofit approaches: managing risks to minimise the energy performance gap

Energy use in buildings remains a significant part of overall energy demand. Deep renovation projects, delivered at scale, remain a challenging task to achieve a lower carbon building stock.The complexity of building renovation beyond standards and building specifications is related to inherent characteristics of buildings which require distinct project management techniques. While there are now more projects focusing on achieving operational performance, there is still very little research on the management of the renovation and retrofit process itself. Recognising that each project working on an existing building is unique in type, timing, energy goals and the roles/characteristics of people involved, the aim of this paper is to add to the current debate of how intervention approaches (one-off or over-time, whole-house, fabric-first room-by-room, measure-by-measure) are promoted by different policies, and with what impact. The paper discusses the complexity of a deep renovation project in terms of planning and management and the ways current policies can lead to unintended consequences in the short and long term, as well in lock-in effects that contribute to energy performance, and to the gap between designed and actual energy performance. Using a typology of risks, the issues associated with renovation processes and technologies were explored in a sample of cases studies from deep retrofits across the EU. The evidence from these shows that despite holistic planning for renovation, interventions tend to be carried out in phases. These contrasting time dimensions and the different retrofit approaches are discussed with risk profiles for each retrofit project, suggesting how risks emerge throughout a project. A series of risk mitigation strategies are suggested which, taken in combination to suit a specific project’s risk profile, may serve to reduce and potentially eliminate the building renovation energy performance gap

Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks

Personal carbon allowances revisited

Here we discuss how personal carbon allowances (PCAs) could play a role in achieving ambitious climate mitigation targets. We argue that recent advances in AI for sustainable development, together with the need for a low-carbon recovery from the COVID-19 crisis, open a new window of opportunity for PCAs. Furthermore, we present design principles based on the Sustainable Development Goals for the future adoption of PCAs. We conclude that PCAs could be trialled in selected climate-conscious technologically advanced countries, mindful of potential issues around integration into the current policy mix, privacy concerns and distributional impacts

NMR Evidence for Antiferromagnetic Transition in the Single-Component Molecular Conductor, [Au(tmdt)_{2}] at 110 K

We present the results of a ^{1}H NMR study of the single-component molecular conductor, [Au(tmdt)_{2}]. A steep increase in the NMR line width and a peak formation of the nuclear spin-lattice relaxation rate, 1/T_{1}, were observed at around 110 K. This behavior provides clear and microscopic evidences for a magnetic phase transition at considerably high temperature among organic conductors. The observed variation in 1/T_{1} with respect to temperature indicates the highly correlated nature of the metallic phase.Comment: 5pages, 6figures to be published in J. Phys. Soc. Jp

Adaptive Use of Information during Growth Can Explain Long-Term Effects of Early Life Experiences.

Development is a continuous process during which individuals gain information about their environment and adjust their phenotype accordingly. In many natural systems, individuals are particularly sensitive to early life experiences, even in the absence of later constraints on plasticity. Recent models have highlighted how the adaptive use of information can explain age-dependent plasticity. These models assume that information gain and phenotypic adjustments either cannot occur simultaneously or are completely independent. This assumption is not valid in the context of growth, where finding food results both in a size increase and learning about food availability. Here, we describe a simple model of growth to provide proof of principle that long-term effects of early life experiences can arise through the coupled dynamics of information acquisition and phenotypic change in the absence of direct constraints on plasticity. The increase in reproductive value from gaining information and sensitivity of behavior to experiences declines across development. Early life experiences have long-term impacts on age of maturity, yet-due to compensatory changes in behavior-our model predicts no substantial effects on reproductive success. We discuss how the evolution of sensitive windows can be explained by experiences having short-term effects on informational and phenotypic states, which generate long-term effects on life-history decisions.This research was funded by the European Union’s Seventh Framework Programme (FP7/2007-2011) under grant 259679 (IDEAL) awarded to T.U. T.W.F., A.D.H., and P.C.T. were supported by the European Research Council (ERC Advanced Grant 250209 Evomech to A. Houston). T.U. was supported by the Royal Society of London and the Knut and Alice Wallenberg Foundation. A.D.H. was supported by fellowships from the Wissenschaftskolleg zu Berlin and the Natural Environment Research Council (grant NE/L011921/1)