Trust based collaborative filtering

k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful algorithm supporting recommender systems, attempts to relieve the problem of information overload by generating predicted ratings for items users have not expressed their opinions about; to do so, each predicted rating is computed based on ratings given by like-minded individuals. Like-mindedness, or similarity-based recommendation, is the cause of a variety of problems that plague recommender systems. An alternative view of the problem, based on trust, offers the potential to address many of the previous limiations in CF. In this work we present a varation of kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one another by evaluating the utility of the rating information they receive. This method redefines the way CF is performed, and while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms the basic similarity-based methods in terms of prediction accuracy

Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions

Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification

Streets as Public Spaces: Lessons from Street Vending in Ahmedabad, India

Public spaces go beyond the typical definition of being an open space. They reflect the diversity and vibrancy of the urban fabric and hold the power to create memories. Among all public spaces, streets emerge as the most public. Streets are engines of economic activities, social hubs, and platforms for civic engagement. They break socio-economic divides and foster social cohesion. Planning, designing, and managing better public spaces have become important global discussions. Sustainable Development Goals (8 and 11) and the New Urban Agenda emphasize the significance of inclusive and sustainable economy and safe, accessible and quality public spaces for all. The proposed article uses the case of street vending to understand the manifestation of these goals in an Indian context by assessing street vendors’ role in Ahmedabad’s urban fabric through extensive spatial analysis of 4,000 vendors at four different time points of the day, perception studies of their clientele disaggregated by gender, income and age, and their relationship with surrounding land-use and street hierarchy. It showcases how street vendors make the streets more vibrant by increasing activities, safer through ensuring inflow of people, and inclusive in its true sense by allowing people from different backgrounds to participate in the exchange of goods and services. It further argues that street vendors are vital elements of more equitable and exciting streets and public space

Comparing cities’ cycling patterns using online shared bicycle maps

Bicycle sharing systems are increasingly being deployed in urban areas around the world, alongside online maps that disclose the state (i.e., location, number of bicycles/number of free parking slots) of stations in each city. Recent work has demonstrated how regularly monitoring these online maps allows for a granular analysis of a city’s cycling trends; further, the literature indicates that different cities have unique spatio-temporal patterns, reducing the generalisability of any insights or models derived from a single system. In this work, we analyse 4.5 months of online bike-sharing map data from 10 cities which, combined, have 996 stations. While an aggregate comparison supports the view of cities having unique usage patterns, results of applying unsupervised learning to the temporal data shows that, instead, only the larger systems display heterogeneous behaviour, indicating that many of these systems share intrinsic similarities. We further show how these similarities are reflected in the predictability of stations’ occupancy data via a cross-city comparison of the error that a variety of approaches achieve when forecasting the number of bicycles that a station will have in the near future.We close by discussing the impact of uncovering these similarities on how future bicycle sharing systems can be designed, built, and managed.This is the accepted manuscript. The final published version is available at http://link.springer.com/article/10.1007%2Fs11116-015-9599-9

The Dark Triad of personality and momentary affective states: an experience sampling study

background The Dark Triad (DT; Machiavellianism, narcissism, psychopathy) refers to three distinct but interrelated socially undesirable traits which are associated with an antagonistic and exploitative strategy of conduct. The aim of the current study was to investigate the relationships between the DT traits and momentary affective states using a longitudinal approach. participants and procedure University students (N = 81) completed personality measures and participated in an 8-day experience-sampling study. Subjects provided n = 2572 responses. Multilevel analyses were used to assess relationships between the DT and affect. results All the DT traits were associated with negative affect: the two psychopathy dimensions (boldness and meanness) negatively, and the remaining traits positively. Boldness and grandiose narcissism were associated with positive affect. The presence of others differentiated the relationships between Machiavellianism and meanness and negative affective states. conclusions The findings showed a tendency to experience more negative affect in everyday life in people with higher levels of grandiose and vulnerable narcissism, Machiavellianism, and disinhibition

Studying commuting behaviours using collaborative visual analytics

Mining a large origin–destination dataset of journeys made through London’s Cycle Hire Scheme (LCHS), we develop a technique for automatically classifying commuting behaviour that involves a spatial analysis of cyclists’ journeys. We identify a subset of potential commuting cyclists, and for each individual define a plausible geographic area representing their workplace. All peak-time journeys terminating within the vicinity of this derived workplace in the morning, and originating from this derived workplace in the evening, we label commutes. Three techniques for creating these workplace areas are compared using visual analytics: a weighted mean-centres calculation, spatial k-means clustering and a kernel density-estimation method. Evaluating these techniques at the individual cyclist level, we find that commuters’ peak-time journeys are more spatially diverse than might be expected, and that for a significant portion of commuters there appears to be more than one plausible spatial workplace area. Evaluating the three techniques visually, we select the density-estimation as our preferred method. Two distinct types of commuting activity are identified: those taken by LCHS customers living outside of London, who make highly regular commuting journeys at London’s major rail hubs; and more varied commuting behaviours by those living very close to a bike-share docking station. We find evidence of many interpeak journeys around London’s universities apparently being taken as part of cyclists’ working day. Imbalances in the number of morning commutes to, and evening commutes from, derived workplaces are also found, which might relate to local availability of bikes. Significant decisions around our workplace analysis, and particularly these broader insights into commuting behaviours, are discovered through exploring this analysis visually. The visual analysis approach described in the paper is effective in enabling a research team with varying levels of analysis experience to participate in this research. We suggest that such an approach is of relevance to many applied research contexts

Space Harpoon Projectile Analysis for Space Debris Capture

Space debris will become a more prevalent issue in this decade as technological advancements and greater dependencies on communications require more satellites in orbit, with some companies already hosting mega-constellations. Mitigating the debris using a space-tether is the most viable method to construct a space debris capture regime with current technology levels. Harpoon heads for the tethers are key design interests as these will penetrate through the satellites and/or debris. The focus of this paper is the analysis of an aluminium 6082, flat and conical head design, used to perforate aluminium 1050A plates using a gas gun laboratory. The aim is to conduct experiments to achieve a low Minimum Ballistic Velocity (MBV) where this velocity is the minimum velocity needed to perforate a material. Maximum perforation intertwined with minimal fragmentation is the desired balance sought from the designs. A high-speed camera records times taken for the events before and after perforation which deduces the MBV. 10 bar, 12.5 bar, and 15 bar of pressure were used, as well as 3mm, 1.5mm and 1mm Aluminium plate thickness, to provide diverse results for analysis. The MBV was calculated at 49.54m/s for 3mm thickness, with the conical head. The plates were cooled using dry ice to mimic space-like environments where tensile and yield strength increased with the cooler climates, resulting in higher MBVs. After impact, perforation profiles are analysed using a DSLR camera, resulting in ‘punches’ of material with the flat head and ‘petaling’ for the conical head. Conical perforation allows for material to be retained within the plate whilst flat head designs possess the potential for further space debris creation. The results retain reliability through validation checks with an oscilloscope and taking tolerances throughout the experiment. The results feed as a foundation to venture into future work with further ergonomic and bespoke designs

The neural stem cell microenvironment

In mammals, neural stem cells appear early in development and remain active within the central nervous system for the whole life duration of the organism. During this developmental process they assume different cellular morphologies and reside within changing microenvironments whilst retaining the basic properties of a stem cell: multipotentiality and the ability to self renew. In this chapter, the basic morphological characteristics of neural stem cells will be reviewed, along with the fundamental structural components and signalling molecules of their microenvironments. In early neural development, when the patterning of the nervous system is established, neural stem cells are called neuroepithelial cells; they are situated among other neuroepithelial cells and they are exposed to various signals such as retinoic acid, sonic hedgehog and fibroblast growth factors. When neurogenesis commences, stem cells are transformed to radial glial cells and the complexity of their microenvironment increases due to the emergence of various types of neuronal progenitors, differentiated cells and extracellular signaling molecules. Finally, during adulthood, neural stem cells assume astroglial morphology and reside in specific microenvironments that are called neurogenic niches; small neurogenic islands where neurons and glia are continuously generated under the control of mechanisms largely similar to those operating during embryonic development

Putting mood in context: Using smartphones to examine how people feel in different locations

Does personality predict how people feel in different types of situations? The present research addressed this question using data from several thousand individuals who used a mood tracking smartphone application for several weeks. Results from our analyses indicated that people’s momentary affect was linked to their location, and provided preliminary evidence that the relationship between state affect and location might be moderated by personality. The results highlight the importance of looking at person-situation relationships at both the trait- and state-levels and also demonstrate how smartphones can be used to collect person and situation information as people go about their everyday lives.Engineering and Physical Sciences Research Council (Grant ID: EP/I032673/1)This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.jrp.2016.06.00

Probabilistic group recommendation via information matching

Increasingly, web recommender systems face scenarios where they need to serve suggestions to groups of users; for exam- ple, when families share e-commerce or movie rental web accounts. Research to date in this domain has proposed two approaches: computing recommendations for the group by merging any members’ ratings into a single profile, or com- puting ranked recommendations for each individual that are then merged via a range of heuristics. In doing so, none of the past approaches reason on the preferences that arise in individuals when they are members of a group . In this work, we present a probabilistic framework, based on the notion of information matching, for group recommendation. This model defines group relevance as a combination of the item’s relevance to each user as an individual and as a member of the group; it can then seamlessly incorporate any group rec- ommendation strategy in order to rank items for a set of individuals. We evaluate the model’s efficacy at generating recommendations for both single individuals and groups us- ing the MovieLens and MoviePilot data sets. In both cases, we compare our results with baselines and state-of-the-art collaborative filtering algorithms, and show that the model outperforms all others over a variety of ranking metrics