The patterning of finance/security : a designerly walkthrough of challenger banking apps

Culture is being ‘appified’. Diverse, pre-existing everyday activities are being redesigned so they happen with and through apps. While apps are often encountered as equivalent icons in apps stores or digital devices, the processes of appification – that is, the actions required to turn something into an app – vary significantly. In this article, we offer a comparative analysis of a number of ‘challenger’ banking apps in the United Kingdom. As a retail service, banking is highly regulated and banks must take steps to identify and verify their customers before entering a retail relationship. Once established, this ‘secured’ financial identity underpins a lot of everyday economic activity. Adopting the method of the walkthrough analysis, we study the specific ways these processes of identifying and verifying the identity of the customer (now the user) occur through user onboarding. We argue that banking apps provide a unique way of binding the user to an identity, one that combines the affordances of smart phones with the techniques, knowledge and patterns of user experience design. With the appification of banking, we see new processes of security folded into the everyday experience of apps. Our analysis shows how these binding identities are achieved through what we refer to as the patterning of finance/security. This patterning is significant, moreover, given its availability for wider circulation beyond the context of retail banking apps

Realizing the Potential of Marine Biotechnology : Challenges and Opportunities

A paid open access option is available for this journal. Author's final version or publisher's version/PDF Authors may deposit in funding agency designated repository after 12 monthsPeer reviewedPublisher PD

Open source environment to define constraints in route planning for GIS-T

Route planning for transportation systems is strongly related to shortest path algorithms, an optimization problem extensively studied in the literature. To find the shortest path in a network one usually assigns weights to each branch to represent the difficulty of taking such branch. The weights construct a linear preference function ordering the variety of alternatives from the most to the least attractive.Postprint (published version

Computing and Plotting Correlograms by Python and R Libraries for Correlation Analysis of the Environmental Data in Marine Geomorphology

International audienceThe geomorphology of the Mariana Trench, the deepest ocean trench on the Earth, has a complex character: its transverse profile is asymmetric, the slopes are higher on the side of the Mariana island arc. The shape of the Mariana Trench is a strongly elongated, arched in plan and lesser rectilinear depression. The slopes of the trench are dissected by deep underwater canyons with various narrow steps on the slopes of various shapes and sizes, caused by active tectonic and sedimentation processes. Understanding of factors that may affect the shape of the geomorphology of such complex structure requires advanced methods of numerical computing. Current research is focused on the analysis of the geomorphology of the Mariana Trench by application of statistical libraries embedded in Python and R programming languages for the data analysis. Workflow algorithms include processing a data set by analysis, computing and visual plotting of the graphs. The research aims is to understand the environmental interactions affecting submarine geomorphology of the Mariana Trench by statistical data analysis. Technically, used algorithms included libraries of Python (Seaborn, Matplotlib, Pandas, SciPy and NumPy) and libraries of R ({hexbin}, {ggally}, {ggplot2}). Technically, following types of the statistical analysis were tested for computing and plotting: correlograms, histograms, strip plots, ridgeline plots and hexagonal diagrams for the bathymetric and geomorphic analysis. Python, being a high-level language, shown more straightforward approach for the statistical data analysis, while R implies more power in the data visualization. The results of the geospatial data modelling show detected correlation between various factors (geology, bathymetry, tectonics) affecting submarine geomorphology that reveal unevenness in its structure. Both programming languages demonstrated significant functionality for the spatial data analysis. The effective and accurate geospatial data visualization demonstrated by Python and R proves high potential of their application in the geomorphological studies

Advancing Philanthropy Through Data Analytics

Most foundations are engaged in the art of the possible. They invest in organizations and programs aimed at transforming current realities into better possibilities and in ideas that "push the envelope" in ways that test the edges of what could be. But few foundations are taking advantage of a proven tool for expanding the possible in their own grant making and mission effectiveness: data analysis. Analytic methods are routinely used and considered essential in nearly every other sector of the economy. In healthcare, retail and financial services, to name just a few hotbeds, analytics has dramatically affected what -- with a given amount of time and money -- is possible to measure, to manage, to learn, to change and to achieve. The foundation world -- which holds over USD $1.5 trillion in assets globally and$646.1 billion in the U.S. alone, with annual grant making of approximately $100 billion globally and$46.9 billion in the U.S. -- uses analytic methods to assess, select, monitor, and report on its capital market investments for the 95% of its corpus that generates revenue. These very same methods, with even the introduction of the most basic analytic techniques, will provide demonstrable gains for the remaining 5% of the corpus that is distributed for charitable purposes. Foundations can gain visibility into how resources are allocated across their organization, view grant distribution compared to per capita need and explore outcomes data... among many other uses. For grant making organizations, analytics are a key that can be used to unlock answers to vital questions such as:How well does our grant making align with our strategy and stated tactics?Which grantees produce the best outcomes in support of our mission and strategy?Has this intervention strategy been tried before and, if so, how well did it work?Does this strategy merit replication, and is there evidence that it can be replicated and/or scaled?If we committed the same grant making budget differently, could we produce a greater impact?Board members gain visibility into the execution of top-level strategies and timely enough operational feedback to actually refine their strategic plans and, therefore, better influence desired outcomes in alignment with their mission. Foundations leaders and senior managers gain insights into what is working and clear indicators of where improvements are needed. Program managers gain time-saving tools that simplify their work and help them steer toward grant making objectives. Data analysis also improves communication and coordination by helping all participants arrive at a clear and common understanding of what types of grants and/or investments are being deployed and how they are influencing outcomes. Moreover, improved transparency enables stakeholders and the community at large to better see what investments are accomplishing. This paper looks at some of these early achievements in Kuity's work with The California Endowment (TCE). It also discusses where the nonprofit sector is headed in the implementation of more advanced analytic methods that will yield even greater benefits

Youth Advantage Versus Gender Penalty: Selecting and Electing Young Candidates

Young people are under-represented in formal politics. While this may be a mere projection of their lack among voters and party members, the article investigates whether being young is a disadvantage in election processes, and if age effects differ by gender. Bridging the literature on gender & politics and political behavior, the article draws on an innovative sequential mixed-method design. Studying the 2019 Irish local elections, it uses 33 interviews to build hypotheses, which are subsequently tested on an original candidate-level dataset (n = 1884). The findings suggest that, when controlling for party affiliation and political status, being young can provide a net electoral advantage to male candidates. In contrast, young female candidates appear to be advantaged by their age but penalized by their gender. The article thus contributes to our understanding about the conditions right at the start of political careers and the emergence of intersectional representational inequalities.publishedVersio

Visualization of multidimensional data with collocated paired coordinates and general line coordinates

Often multidimensional data are visualized by splitting n-D data to a set of low dimensional data. While it is useful it destroys integrity of n-D data, and leads to a shallow understanding complex n-D data. To mitigate this challenge a difficult perceptual task of assembling low-dimensional visualized pieces to the whole n-D vectors must be solved. Another way is a lossy dimension reduction by mapping n-D vectors to 2-D vectors (e.g., Principal Component Analysis). Such 2-D vectors carry only a part of information from n-D vectors, without a way to restore n-D vectors exactly from it. An alternative way for deeper understanding of n-D data is visual representations in 2-D that fully preserve n-D data. Methods of Parallel and Radial coordinates are such methods. Developing new methods that preserve dimensions is a long standing and challenging task that we address by proposing Paired Coordinates that is a new type of n-D data visual representation and by generalizing Parallel and Radial coordinates as a General Line coordinates. The important novelty of the concept of the Paired Coordinates is that it uses a single 2-D plot to represent n-D data as an oriented graph based on the idea of collocation of pairs of attributes. The advantage of the General Line Coordinates and Paired Coordinates is in providing a common framework that includes Parallel and Radial coordinates and generating a large number of new visual representations of multidimensional data without lossy dimension reduction

Predicting hotel bookings cancellation with a machine learning classification model

Booking cancellations have significant impact on demand-management decisions in the hospitality industry. To mitigate the effect of cancellations, hotels implement rigid cancellation policies and overbooking tactics, which in turn can have a negative impact on revenue and on the hotel reputation. To reduce this impact, a machine learning based system prototype was developed. It makes use of the hotel’s Property Management Systems data and trains a classification model every day to predict which bookings are “likely to cancel” and with that calculate net demand. This prototype, deployed in a production environment in two hotels, by enforcing A/B testing, also enables the measurement of the impact of actions taken to act upon bookings predicted as “likely to cancel”. Results indicate good prototype performance and provide important indications for research progress whilst evidencing that bookings contacted by hotels cancel less than bookings not contacted.info:eu-repo/semantics/acceptedVersio