Abstract: Provided herein are methods and systems relating to location-based services such as social networking, providing demographic information, tracking mobile devices, providing business information, providing an adaptable user interface, remotely effecting a change on a portable electronic device, providing a geofence, outputting location-based information on a mobile device, varying transmissions to and from a mobile device, providing location-based alerts, verifying transactions and tailoring information to the behavior of a user.

Abstract: Evolview is an interactive tree visualization tool designed to help researchers in visualizing phylogenetic trees and in annotating these with additional information. It offers the user with a platform to upload trees in most common tree formats, such as Newick/Phylip, Nexus, Nhx and PhyloXML, and provides a range of visualization options, using fifteen types of custom annotation datasets. The new version of Evolview was designed to provide simple tree uploads, manipulation and viewing options with additional annotation types. The 'dataset system' used for visualizing tree information has evolved substantially from the previous version, and the user can draw on a wide range of additional example visualizations. Developments since the last public release include a complete redesign of the user interface, new annotation dataset types, additional tree visualization styles, full-text search of the documentation, and some backend updates. The project management aspect of Evolview was also updated, with a unified approach to tree and project management and sharing. Evolview is freely available at: https://www.evolgenius.info/evolview/.

Abstract: Internet of Things (IoT) devices are increasingly found in everyday homes, providing useful functionality for devices such as TVs, smart speakers, and video doorbells. Along with their benefits come potential privacy risks, since these devices can communicate information about their users to other parties over the Internet. However, understanding these risks in depth and at scale is difficult due to heterogeneity in devices' user interfaces, protocols, and functionality.In this work, we conduct a multidimensional analysis of information exposure from 81 devices located in labs in the US and UK. Through a total of 34,586 rigorous automated and manual controlled experiments, we characterize information exposure in terms of destinations of Internet traffic, whether the contents of communication are protected by encryption, what are the IoT-device interactions that can be inferred from such content, and whether there are unexpected exposures of private and/or sensitive information (e.g., video surreptitiously transmitted by a recording device). We highlight regional differences between these results, potentially due to different privacy regulations in the US and UK. Last, we compare our controlled experiments with data gathered from an in situ user study comprising 36 participants.

Abstract: Background: Network meta‐analysis (NMA) is a powerful analysis method used to identify the best treatments for a condition and is used extensively by health care decision makers. Although software routines exist for conducting NMA, they require considerable statistical programming expertise to use, which limits the number of researchers able to conduct such analyses. Objectives: To develop a web‐based tool allowing users with only standard internet browser software to be able to conduct NMAs using an intuitive “point and click” interface and present the results using visual plots. Methods: Using the existing netmeta and Shiny packages for R to conduct the analyses, and to develop the user interface, we created the MetaInsight tool which is freely available to use via the web. Results: A package was created for conducting NMA which satisfied our objectives, and this is described, and its application demonstrated, using an illustrative example. Conclusions: We believe that many researchers will find our package helpful for facilitating NMA as well as allowing decision makers to scrutinize presented results visually and in real time. This will impact on the relevance of statistical analyses for health care decision making and sustainably increase capacity by empowering informed nonspecialists to be able to conduct more clinically relevant reviews. It is also hoped that others will be inspired to create similar tools for other advanced specialist analyses methods using the freely available technologies we have adopted.

Abstract: Genome annotation is the process of identifying the location and function of a genome's encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, we present Apollo, an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform. Some of Apollo's newer user interface features include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible.

Abstract: Every person is unique, with individual behavioural characteristics: how one moves, coordinates, and uses their body. In this paper we investigate body motion as behavioural biometrics for virtual reality. In particular, we look into which behaviour is suitable to identify a user. This is valuable in situations where multiple people use a virtual reality environment in parallel, for example in the context of authentication or to adapt the VR environment to users' preferences. We present a user study (N=22) where people perform controlled VR tasks (pointing, grabbing, walking, typing), monitoring their head, hand, and eye motion data over two sessions. These body segments can be arbitrarily combined into body relations, and we found that these movements and their combination lead to characteristic behavioural patterns. We present an extensive analysis of which motion/relation is useful to identify users in which tasks using classification methods. Our findings are beneficial for researchers and practitioners alike who aim to build novel adaptive and secure user interfaces in virtual reality.

Abstract: The MESSAGE Integrated Assessment Model (IAM) developed by IIASA has been a central tool of energy-environment-economy systems analysis in the global scientific and policy arena. It played a major role in the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC); it provided marker scenarios of the Representative Concentration Pathways (RCPs) and the Shared Socio-Economic Pathways (SSPs); and it underpinned the analysis of the Global Energy Assessment (GEA). Alas, to provide relevant analysis for current and future challenges, numerical models of human and earth systems need to support higher spatial and temporal resolution, facilitate integration of data sources and methodologies across disciplines, and become open and transparent regarding the underlying data, methods, and the scientific workflow. In this manuscript, we present the building blocks of a new framework for an integrated assessment modeling platform; the \ecosystem" comprises: i) an open-source GAMS implementation of the MESSAGE energy++ system model integrated with the MACRO economic model; ii) a Java/database backend for version-controlled data management, iii) interfaces for the scientific programming languages Python & R for efficient input data and results processing workflows; and iv) a web-browser-based user interface for model/scenario management and intuitive \drag-and-drop" visualization of results. The framework aims to facilitate the highest level of openness for scientific analysis, bridging the need for transparency with efficient data processing and powerful numerical solvers. The platform is geared towards easy integration of data sources and models across disciplines, spatial scales and temporal disaggregation levels. All tools apply best-practice in collaborative software development, and comprehensive documentation of all building blocks and scripts is generated directly from the GAMS equations and the Java/Python/R source code.

Abstract: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains. PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. CONCLUSIONS FINDINGS BACKGROUND

Abstract: With the recent increase in the interest of individuals in health, lifecare, and disease, hospital medical services have been shifting from a treatment focus to prevention and health management. The medical industry is creating additional services for health- and life-promotion programs. This change represents a medical-service paradigm shift due to the prolonged life expectancy, aging, lifestyle changes, and income increases, and consequently, the concept of the smart health service has emerged as a major issue. Due to smart health, the existing health-promotion medical services that typically have been operated by large hospitals have been developing into remote medical-treatment services where personal health records are used in small hospitals; moreover, a further expansion has been occurring in the direction of u-Healthcare in which health conditions are continuously monitored in the everyday lives of the users. However, as the amount of data is increasing and the medical-data complexity is intensifying, the limitations of the previous approaches are increasingly problematic; furthermore, since even the same disease can show different symptoms depending on the personal health conditions, lifestyle, and genome information, universal healthcare is not effective for some patients, and it can even generate severe side effects. Thus, research on the AI-based healthcare that is in the form of mining-based smart health, which is a convergence technology of the 4IR, is actively being carried out. Particularly, the introduction of various smart medical equipment for which healthcare big data and a running machine have been combined and the expansion of the distribution of smartphone wearable devices have led to innovations such as personalized diagnostic and treatment services and chronic-disease management and prevention services. In addition, various already launched applications allow users to check their own health conditions and receive the corresponding feedback in real time. Based on these innovations, the preparation of a way to determine a user’s current health conditions, and to respond properly through contextual feedback in the case of unsound health conditions, is underway. However, since the previously made healthcare-related applications need to be linked to a wearable device, and they provide medical feedback to users based solely on specific biometric data, inaccurate information can be provided. In addition, the user interfaces of some healthcare applications are very complicated, causing user inconvenience regarding the attainment of desired information. Therefore, we propose a chatbot-based healthcare service with a knowledge base for cloud computing. The proposed method is a mobile health service in the form of a chatbot for the provision of fast treatment in response to accidents that may occur in everyday life, and also in response to changes of the conditions of patients with chronic diseases. A chatbot is an intelligent conversation platform that interacts with users via a chatting interface, and since its use can be facilitated by linkages with the major social network service messengers, general users can easily access and receive various health services. The proposed framework enables a smooth human–robot interaction that supports the efficient implementation of the chatbot healthcare service. The design of the framework comprises the following four levels: data level, information level, knowledge level, and service level.

Abstract: Artificial intelligence can provide organizations with prescriptive options for decision-making. Based on the notions of algorithmic decision-making and user involvement, we assess the role of arti...

Abstract: Chatbots are becoming increasingly popular as a human-computer interface. The traditional best practices normally applied to User Experience (UX) design cannot easily be applied to chatbots, nor can conventional usability testing techniques guarantee accuracy. WeightMentor is a bespoke self-help motivational tool for weight loss maintenance. This study addresses the following four research questions: How usable is the WeightMentor chatbot, according to conventional usability methods?; To what extend will different conventional usability questionnaires correlate when evaluating chatbot usability?; And how do they correlate to a tailored chatbot usability survey score?; What is the optimum number of users required to identify chatbot usability issues?; How many task repetitions are required for a first-time chatbot users to reach optimum task performance (i.e. efficiency based on task completion times)? This paper describes the procedure for testing the WeightMentor chatbot, assesses correlation between typical usability testing metrics, and suggests that conventional wisdom on participant numbers for identifying usability issues may not apply to chatbots. The study design was a usability study. WeightMentor was tested using a pre-determined usability testing protocol, evaluating ease of task completion, unique usability errors and participant opinions on the chatbot (collected using usability questionnaires). WeightMentor usability scores were generally high, and correlation between questionnaires was strong. The optimum number of users for identifying chatbot usability errors was 26, which challenges previous research. Chatbot users reached optimum proficiency in tasks after just one repetition. Usability test outcomes confirm what is already known about chatbots - that they are highly usable (due to their simple interface and conversation-driven functionality) but conventional methods for assessing usability and user experience may not be as accurate when applied to chatbots.

Abstract: We present Charticulator , an interactive authoring tool that enables the creation of bespoke and reusable chart layouts. Charticulator is our response to most existing chart construction interfaces that require authors to choose from predefined chart layouts, thereby precluding the construction of novel charts. In contrast, Charticulator transforms a chart specification into mathematical layout constraints and automatically computes a set of layout attributes using a constraint-solving algorithm to realize the chart. It allows for the articulation of compound marks or glyphs as well as links between these glyphs, all without requiring any coding or knowledge of constraint satisfaction. Furthermore, thanks to the constraint-based layout approach, Charticulator can export chart designs into reusable templates that can be imported into other visualization tools. In addition to describing Charticulator's conceptual framework and design, we present three forms of evaluation: a gallery to illustrate its expressiveness, a user study to verify its usability, and a click-count comparison between Charticulator and three existing tools. Finally, we discuss the limitations and potentials of Charticulator as well as directions for future research. Charticulator is available with its source code at https://charticulator.com .

Abstract: Mobile robots are playing a significant role in Higher Education science and engineering teaching, as they offer a flexible platform to explore and teach a wide-range of topics such as mechanics, electronics and software. Unfortunately the widespread adoption is limited by their high cost and the complexity of user interfaces and programming tools. To overcome these issues, a new affordable, adaptable and easy-to-use robotic platform is proposed. Mona is a low-cost, open-source and open-hardware mobile robot, which has been developed to be compatible with a number of standard programming environments. The robot has been successfully used for both education and research at The University of Manchester, UK.

Abstract: Evaluating algorithmic recommendations is an important, but difficult, problem. Evaluations conducted offline using data collected from user interactions with an online system often suffer from biases arising from the user interface or the recommendation engine. Online evaluation (A/B testing) can more easily address problems of bias, but depending on setting can be time-consuming and incur risk of negatively impacting the user experience, not to mention that it is generally more difficult when access to a large user base is not taken as granted. A compromise based on \em counterfactual analysis is to present some subset of online users with recommendation results that have been randomized or otherwise manipulated, log their interactions, and then use those to de-bias offline evaluations on historical data. However, previous work does not offer clear conclusions on how well such methods correlate with and are able to predict the results of online A/B tests. Understanding this is crucial to widespread adoption of new offline evaluation techniques in recommender systems. In this work we present a comparison of offline and online evaluation results for a particular recommendation problem: recommending playlists of tracks to a user looking for music. We describe two different ways to think about de-biasing offline collections for more accurate evaluation. Our results show that, contrary to much of the previous work on this topic, properly-conducted offline experiments do correlate well to A/B test results, and moreover that we can expect an offline evaluation to identify the best candidate systems for online testing with high probability.

Abstract: Sketches and real-world user interface examples are frequently used in multiple stages of the user interface design process. Unfortunately, finding relevant user interface examples, especially in large-scale datasets, is a highly challenging task because user interfaces have aesthetic and functional properties that are only indirectly reflected by their corresponding pixel data and meta-data. This paper introduces Swire, a sketch-based neural-network-driven technique for retrieving user interfaces. We collect the first large-scale user interface sketch dataset from the development of Swire that researchers can use to develop new sketch-based data-driven design interfaces and applications. Swire achieves high performance for querying user interfaces: for a known validation task it retrieves the most relevant example as within the top-10 results for over 60% of queries. With this technique, for the first time designers can accurately retrieve relevant user interface examples with free-form sketches natural to their design workflows. We demonstrate several novel applications driven by Swire that could greatly augment the user interface design process.

Abstract: This paper discusses the challenges in the collaboration between human operators and industrial robots for assembly operations focusing on safety and simplified interaction. A case study is presented, involving perception technologies for the robot in conjunction with wearable devices used by the operator. In terms of robot perception, a manual guidance module, an air pressor contact sensor namely skin, and a vision system for recognition and tracking of objects have been developed and integrated. Concerning the wearable devices, an advanced user interface including audio and haptic commands accompanied by augmented reality technology are used to support the operator and provide awareness by visualizing information related to production and safety aspects. In parallel, safety functionalities are implemented through collision detection technologies such as a safety skin and safety monitored regions delimiting the area of the robot activities. The complete system is coordinated under a common integration platform and it is validated in a case study of the white goods industry.

Abstract: In this paper we present the results of an exploratory study examining the potential of voice assistants (VA) for some groups of older adults in the context of Smart Home Technology (SHT). To research the aspect of older adults' interaction with voice user interfaces (VUI) we organized two workshops and gathered insights concerning possible benefits and barriers to the use of VA combined with SHT by older adults. Apart from evaluating the participants' interaction with the devices during the two workshops we also discuss some improvements to the VA interaction paradigm.

Abstract: Understanding user behavior and adapting to it has been an important focus area for applications. That adaptation is commonly called Personalization. Personalization has been sought after in gaming, personal assistants, dialogue managers, and other popular application categories. One of the challenges of personalization methods is the time they take to adapt to the user behavior or reactions. This sometimes is detrimental to user experience. The contribution of this work is twofold: (1) showing the applicability of granular (per user) personalization through the use of reinforcement learning, and (2) proposing a novel mitigation strategy to decrease the personalization time, through federated learning. To our knowledge, this paper is among the first to present an overall architecture for federated reinforcement learning (FRL), which includes the grouping policy, the learning policy, and the federation policy. We demonstrate the efficacy of the proposed architecture on a non-player character in the Atari game Pong, and scale the implementation across 3, 4, and 5 users. We demonstrate the success of the proposal through achieving a median improvement of ~17% on the personalization time.

Abstract: Graphical User Interfaces (GUIs) are amongst the most common user interfaces, enabling interactions with applications through mouse movements and key presses Tools for automated testing of programs through their GUI exist, however they usually rely on operating system or framework specific knowledge to interact with an application Due to frequent operating system updates, which can remove required information, and a large variety of different GUI frameworks using unique underlying data structures, such tools rapidly become obsolete, Consequently, for an automated GUI test generation tool, supporting many frameworks and operating systems is impractical We propose a technique for improving GUI testing by automatically identifying GUI widgets in screen shots using machine learning techniques As training data, we generate randomized GUIs to automatically extract widget information The resulting model provides guidance to GUI testing tools in environments not currently supported by deriving GUI widget information from screen shots only In our experiments, we found that identifying GUI widgets in screen shots and using this information to guide random testing achieved a significantly higher branch coverage in 18 of 20 applications, with an average increase of 425% when compared to conventional random testing

Abstract: The term Adaptive E-learning System (AES) refers to the set of techniques and approaches that are combined together to offer online courses to the learners with the aim of providing customized resources and interfaces. Most of these systems focus on adaptive contents which are generated to the learners without considering the learning styles of the learners. Learning style of the learner defines the way of learning the contents. The system should not only meet the individual need of the contents but also the customized user interface on the portal. Hence, an AES should mainly focus on recommending learning contents with Adaptive User Interface (AUI) on the portal. The work in the paper proposes a generic approach to provide the learning contents with AUI components based on the learning styles of the learners. The learning style adopted for the work is the Felder-Silverman Learning Style Model (FSLSM). The proposed approach defines generic rules which are generated automatically for any online course with the adaptive contents. Also, the approach takes care of new learners by providing learning path as a user interface component on the portal. The experiment has been conducted on engineering students for a particular online course. The portal is validated using parameters of usability testing by generating test cases and statistical analysis has been carried out to identify the impact of AUI components on the learning process. The result shows the well adaptation of user interface components and contents based on learning styles.

Abstract: HTTPS is one of the most important protocols used to secure communication and is, fortunately, becoming more pervasive. However, especially the long tail of websites is still not sufficiently secured. HTTPS involves different types of users, e.g., end users who are forced to make critical security decisions when faced with warnings or administrators who are required to deal with cryptographic fundamentals and complex decisions concerning compatibility. In this work, we present the first qualitative study of both end user and administrator mental models of HTTPS. We interviewed 18 end users and 12 administrators; our findings reveal misconceptions about security benefits and threat models from both groups. We identify protocol components that interfere with secure configurations and usage behavior and reveal differences between administrator and end user mental models. Our results suggest that end user mental models are more conceptual while administrator models are more protocol-based. We also found that end users often confuse encryption with authentication, significantly underestimate the security benefits of HTTPS, and ignore and distrust security indicators while administrators often do not understand the interplay of functional protocol components. Based on the different mental models, we discuss implications and provide actionable recommendations for future designs of user interfaces and protocols.

Abstract: Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (this http URL), the first software toolkit for appearance-based gaze estimation and interaction.

Abstract: Natural user interfaces are becoming popular. One of the most common natural user interfaces nowadays are voice activated interfaces, particularly smart personal assistants such as Google Assistant, Alexa, Cortana, and Siri. This paper presents the results of an evaluation of these four smart personal assistants in two dimensions: the correctness of their answers and how natural the responses feel to users. Ninety-two participants conducted the evaluation. Results show that Alexa and Google Assistant are significantly better than Siri and Cortana. However, there is no statistically significant difference between Alexa and Google Assistant.

Abstract: Cloud apps like Google Docs and Trello are popular because they enable real-time collaboration with colleagues, and they make it easy for us to access our work from all of our devices. However, by centralizing data storage on servers, cloud apps also take away ownership and agency from users. If a service shuts down, the software stops functioning, and data created with that software is lost. In this article we propose local-first software, a set of principles for software that enables both collaboration and ownership for users. Local-first ideals include the ability to work offline and collaborate across multiple devices, while also improving the security, privacy, long-term preservation, and user control of data. We survey existing approaches to data storage and sharing, ranging from email attachments to web apps to Firebase-backed mobile apps, and we examine the trade-offs of each. We look at Conflict-free Replicated Data Types (CRDTs): data structures that are multi-user from the ground up while also being fundamentally local and private. CRDTs have the potential to be a foundational technology for realizing local-first software. We share some of our findings from developing local-first software prototypes at the Ink & Switch research lab over the course of several years. These experiments test the viability of CRDTs in practice, and explore the user interface challenges for this new data model. Lastly, we suggest some next steps for moving towards local-first software: for researchers, for app developers, and a startup opportunity for entrepreneurs.

Abstract: The advantages of online courses over traditional education lie in the availability of individual training programs, but now the problem of involving and keeping students on the course is urgent for MOOC. Thus, despite the fact that test results on the reasons for unsuccessful completion of online courses by students are different, one of the identified factors was the interface design, while the problem of the influence of the interface on users is not adequately studied. In the present paper, a methodology for studying two different MOOC online platforms was developed, in order to identify factors that are more favorable for user convenience, as well as to identify the best design solutions. A comparative analysis of two MOOC platforms (Coursera and Open Education) made it possible to identify the best behavioral patterns and understand which details of the design of the online platform should be paid attention to and which should not. After carrying out a theoretical analysis and forming the methodology of work, as well as performing an experiment, recommendations were formulated for the development of the user interface of the Open Education platform.