Negative frequency dependent prey selection by two canid predators and its implications for the conservation of a threatened rodent in arid Australia

Unprecedented anthropogenic changes to biodiversity and biogeography demand a greater understanding of the consequences of altered faunal composition for ecosystem functioning. Selective predation has important, yet poorly understood effects on ecosystem stability, and can be strongly influenced by the relative frequencies of different prey types in the environment. Yet, how predators adjust their selection for prey according to their environmental frequency is often overlooked. Here, we assessed frequency dependent selection of prey by dingoes and foxes in the Australian desert, biannually, across a nine-year period (2007-2016). Both predators exhibited potentially destabilizing, negative frequency dependent selection for prey. Foxes persisted to preferentially consume a threatened, native rodent (Notomys fuscus) when it was environmentally scarce. Bolstered by the observation that N.fuscus occurs at low densities in areas where foxes are common, our results suggest that N.fuscus is particularly vulnerable to predation by this predator; possibly because it is naive and/or lacks adaptations to avoid or escape predation by the relatively recently introduced fox. Dingoes tended to consume reptiles when they were scarce; potentially constituting a conservation concern if selected reptilian taxa are threatened. Foxes avoided, thus were unlikely to control populations of overabundant kangaroos, while both foxes and dingoes showed a preference for, and may therefore control populations of invasive rabbits. The integration of our results into the relative suites of (de)stabilizing influences exerted by dingoes and foxes is important to provide a more dynamic insight into how each predator impacts their naturally fluctuating ecosystems

AZERTY amélioré: computational design on a national scale

International audienceFrance is the first country in the world to adopt a keyboard standard informed by computational methods, improving the performance, ergonomics, and intuitiveness of the keyboard while enabling input of many more characters. We describe a human-centric approach developed jointly with stakeholders to utilize computational methods in the decision process not only to solve a well-defined problem but also to understand the design requirements, to inform subjective views, or to communicate the outcomes. To be more broadly useful, research must develop computational methods that can be used in a participatory and inclusive fashion respecting the different needs and roles of stakeholders

Élaboration de la disposition AZERTY modernisée

Document de travail utilisé dans la rédaction de l'annexe F “Élaboration de la disposition AZERTY modernisée” de la norme AFNOR Z 71-300 : “Dispositions de clavier bureautique français”

Assignment Problems for Optimizing Text Input

Text input methods are an integral part of our daily interaction with digital devices. However, their design poses a complex problem: for any method, we must decide which input action (a button press, a hand gesture, etc.) produces which symbol (e.g., a character or word). With only 26 symbols and input actions, there are already more than 10^26 distinct solutions, making it impossible to find the best one through manual design. Prior work has shown that we can use optimization methods to search such large design spaces efficiently and automatically find the best solution for a given task and objective. However, work in this domain has been limited mostly to the performance optimization of keyboards. The Ph.D. thesis advances the field of text-entry optimization by enlarging the space of optimizable text-input methods and proposing new criteria for assessing their optimality. Firstly, the design problem is formulated as an assignment problem for integer programming. This enables the use of standard mathematical solvers and algorithms for efficiently finding good solutions. Then, objective functions are developed, for assessing their optimality with respect to motor performance, ergonomics, and learnability. The corresponding models extend beyond interaction with soft keyboards, to consider multi-finger input, novel sensors, and alternative form factors. In addition, the thesis illustrates how to formulate models from prior work in terms of an assignment problem, providing a coherent theoretical basis for text-entry optimization. The proposed objectives are applied in the optimization of three assignment problems: text input with multi-finger gestures in mid-air, text input on a long piano keyboard, and -- for a contribution to the official French keyboard standard -- input of special characters via a physical keyboard. Combining the proposed models offers a multi-objective optimization approach able to capture the complex cognitive and motor processes during typing. Finally, the dissertation discusses future work that is needed to solve the long-standing problem of finding the optimal layout for physical keyboards, in light of empirical evidence that prior models are insufficient to respond to the diverse typing strategies people employ with modern keyboards. The thesis advances the state of the art in text-entry optimization by proposing novel objective functions that quantify the performance, ergonomics and learnabilityof a text input method. The objectives presented are formulated as assignment problems, which can be solved with integer programming via standard mathematical solvers or heuristic algorithms. While the work focused on text input, the assignment problem can be used to model other design problems in HCI (e.g., how best to assign commands to UI controls or distribute UI elements across several devices), for which the same problem formulations, optimization techniques, and even models could be applied

Computational Support for Functionality Selection in Interaction Design

| openaire: EC/H2020/637991/EU//COMPUTEDDesigning interactive technology entails several objectives, one of which is identifying and selecting appropriate functionality. Given candidate functionalities such as “print,” “bookmark,” and “share,” a designer has to choose which functionalities to include and which to leave out. Such choices critically affect the acceptability, productivity, usability, and experience of the design. However, designers may overlook reasonable designs because there is an exponential number of functionality sets and multiple factors to consider. This article is the first to formally define this problem and propose an algorithmic method to support designers to explore alternative functionality sets in early stage design. Based on interviews of professional designers, we mathematically define the task of identifying functionality sets that strike the best balance among four objectives: usefulness, satisfaction, ease of use, and profitability. We develop an integer linear programming solution that can efficiently solve very large instances (set size over 1,300) on a regular computer. Further, we build on techniques of robust optimization to search for diverse and surprising functionality designs. Empirical results from a controlled study and field deployment are encouraging. Most designers rated computationally created sets to be of the comparable or superior quality than their own. Designers reported gaining better understanding of available functionalities and the design space.Peer reviewe

Typing Behavior is About More than Speed: Users' Strategies for Choosing Word Suggestions Despite Slower Typing Rates

Mobile word suggestions can slow down typing, yet are still widely used. To investigate the apparent benefits beyond speed, we analyzed typing behavior of 15,162 users of mobile devices. Controlling for natural typing speed (a confounding factor not considered by prior work), we statistically show that slower typists use suggestions more often but are slowed down by doing so. To better understand how these typists leverage suggestions - if not to improve their speed - we extract eight usage strategies, including completion, correction, and next-word prediction. We find that word characteristics, such as length or frequency, along with the strategy, are predictive of whether a user will select a suggestion. We show how to operationalize our findings by building and evaluating a predictive model of suggestion selection. Such a model could be used to augment existing suggestion algorithms to consider people's strategic use of word predictions beyond speed and keystroke savings.ISSN:2573-014

Complex Interaction as Emergent Behaviour: Simulating Mid-Air Virtual Keyboard Typing using Reinforcement Learning.

Accurately modelling user behaviour has the potential to significantly improve the quality of human-computer interaction. Traditionally, these models are carefully hand-crafted to approximate specific aspects of well-documented user behaviour. This limits their availability in virtual and augmented reality where user behaviour is often not yet well understood. Recent efforts have demonstrated that reinforcement learning can approximate human behaviour during simple goal-oriented reaching tasks. We build on these efforts and demonstrate that reinforcement learning can also approximate user behaviour in a complex mid-air interaction task: typing on a virtual keyboard. We present the first reinforcement learning-based user model for mid-air and surface-aligned typing on a virtual keyboard. Our model is shown to replicate high-level human typing behaviour. We demonstrate that this approach may be used to augment or replace human testing during the validation and development of virtual keyboards

Observations on typing from 136 million keystrokes

| openaire: EC/H2020/637991/EU//COMPUTEDWe report on typing behaviour and performance of 168,000 volunteers in an online study. The large dataset allows detailed statistical analyses of keystroking patterns, linking them to typing performance. Besides reporting distributions and confirming some earlier findings, we report two new findings. First, letter pairs typed by different hands or fingers are more predictive of typing speed than, for example, letter repetitions. Second, rollover-typing, wherein the next key is pressed before the previous one is released, is surprisingly prevalent. Notwithstanding considerable variation in typing patterns, unsupervised clustering using normalised inter-key intervals reveals that most users can be divided into eight groups of typists that differ in performance, accuracy, hand and finger usage, and rollover. The code and dataset are released for scientific use.Peer reviewe