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363 results for “mobile apps”
Investigating Types and Survivability of Performance Bugs in Mobile Apps
<p>Replication package of the paper entitled "Investigating Types and Survivability of Performance Bugs in Mobile Apps" published in The Empirical Software Engineering Journal</p>
Context-Aware Dataset: STS - South Tyrol Suggests IoT Mobile App Data
<p><strong>STS dataset </strong>was collected by a context-aware recommender system mobile app named as<strong> <a href="https://play.google.com/store/apps/details?id=it.unibz.sts.android&hl=en">"South Tyrol Suggests"</a></strong>. The app provides <strong>context-aware recommendations</strong> for attractions, events, public services, restaurants, and much more based on the rating preferences and personality factors of users.</p> <p><strong>Contextual</strong> <strong>variables</strong> includes </p> <ul> <li><strong>distance:</strong> far away, near by</li> <li><strong>time available:</strong> half day, one day, more than one day</li> <li><strong>temperature:</strong> burning, hot, warm, cool, cold, freezing</li> <li><strong>crowdedness:</strong> crowded, not crowded, empty</li> <li><strong>knowledge of surroundings:</strong> new to area, returning visitor, citizen of the area</li> <li><strong>season:</strong> spring, summer, autumn, winter</li> <li><strong>budget:</strong> budget traveler, price for quality, high spender</li> <li><strong>daytime:</strong> morning, noon, afternoon, evening, night</li> <li><strong>weather:</strong> clear sky, sunny, cloudy, rainy, thunderstorm, snowing</li> <li><strong>companion:</strong> alone, with friends/colleagues, with family, with girlfriend/boyfriend, with children</li> <li><strong>mood:</strong> happy, sad, active, lazy weekday: weekday, weekend</li> <li><strong>travel goal:</strong> visiting friends, business, religion, health care, social event, education, scenic/landscape, hedonistic/fun, activity/sport</li> <li><strong>means of transport:</strong> no transportation means, a bicycle, a car, public transport</li> </ul> <p>More details can be found here:</p> <p><em>Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. <a href="https://www.researchgate.net/profile/Mehdi_Elahi2/publication/283502363_Techniques_for_cold-starting_context-aware_mobile_recommender_systems_for_tourism/links/56ccaa7608ae059e37507cc0.pdf">"<strong>Techniques for cold-starting context-aware mobile recommender systems for tourism</strong>."</a> Intelligenza Artificiale 8, no. 2 (2014): 129-143.</em></p>
Energy-Saving Strategies for Mobile Web Apps and their Measurement: Results from a Decade of Research - Dataset
<p>In 2022, over half of the web traffic was accessed through mobile devices. By reducing the energy consumption of mobile web apps, we can not only extend the battery life of our devices, but also make a significant contribution to energy conservation efforts. For example, if we could save only 5% of the energy used by web apps, we estimate that it would be enough to shut down one of the nuclear reactors in Fukushima. This paper presents a comprehensive overview of energy-saving experiments and related approaches for mobile web apps, relevant for researchers and practitioners. To achieve this objective, we conducted a systematic literature review and identified 44 primary studies for inclusion. Through the mapping and analysis of scientific papers, this work contributes: (1) an overview of the energy-draining aspects of mobile web apps, (2) a comprehensive description of the methodology used for the energy-saving experiments, and (3) a categorization and synthesis of various energy-saving approaches.</p>
FIGURE 2 in DigApp and TaphonomApp: Two new open-access palaeontological and archaeological mobile apps
FIGURE 2. DiggApp Offline modified for excavations at Batallones-10 palaeontological site. A "New Specimen" screen, with "Completeness", "Consolidation", "Preservation" and "Articulation" fields added. B, "Taxonomical Identification" dropdown menu modified to include Batallones-10 faunal list.
FIGURE 3. A in DigApp and TaphonomApp: Two new open-access palaeontological and archaeological mobile apps
FIGURE 3. A, TaphonomApp "Taphonomical Analysis" screen. B, TaphonomApp "New Specimen" scrollable screen.
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 3. Screenshots of the new VC app
<p>The user will be able to stay logged in inside the app and to receive different types of notifications. The application will have the ability to be set up for working offline, while users have the possibility to change the application language, which can be useful for foreign or Erasmus students. Additionally, users will be able to customize the app based on some preferences available in a settings screen, check their grades, send direct messages to other students or professors and also save important documents under “My files” section. During the beta phase of development, a focus group with teachers will be organized in order to present the new apps and to understand what new features are needed to support the courses facilitation on the go.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 2. Screenshots of the Virtual Campus mobile application (d, e)
<p>The VC app was downloaded and used by 350 students, representing a percentage of 5% of the total number of the students enrolled on the platform courses. The application has features corresponding to the desktop version of the platform. In developing it, there were applied principles related to mobile learning usability and design, trying to provide enhanced possibilities for learning on-the-go and also specific notifications (Machun et al., 2012; Harrison et al., 2013; Mocofan, 2017). Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figire 1. Screenshots of the Virtual Campus mobile application (a, b, c)
<p>Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.</p>
Artifact for MobiCom'23: Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility
<p>This dataset contains the anonymized failure data collected from our physical and device farms over a three-month period. The failure data involves 5,918 physical devices as well as 5,918 virtualized devices running on ARM commodity servers.</p> <p>For more details, please visit our website (<a href="https://android-emulation-testing.github.io/">Android-Emulation-Testing.github.io</a>) or read our paper:</p> <ul> <li>[MobiCom'23] Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility</li> </ul> <p>If you use our dataset in your work, please reference it using:</p> <pre><code>@inproceedings {lin2022virtual, author = {Lin, Hao and Qiu, Jiaxing and Wang, Hongyi and Li, Zhenhua and Gong, Liangyi and Gao, Di and Liu, Yunhao and Qian, Feng and Zhang, Zhao and Yang, Ping and Xu, Tianyin}, title = {{Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility}}, booktitle = {The 29th Annual International Conference on Mobile Computing and Networking (ACM MobiCom'23)}, year = {2023}, publisher = {ACM} }</code></pre> <p> </p> <p> </p>
PIMPmyHospital: a Mobile App to Improve Emergency Care Efficiency and Communication
ClinicalTrials.gov study NCT05203146. IPD Sharing: YES. Countries: 1. Publications: 2.
The Development of a Mobile-based App to Increase Uptake of Pre-Exposure Prophylaxis (PrEP) by Men Who Have Sex With Men in China
ClinicalTrials.gov study NCT04426656. IPD Sharing: YES. Countries: 1. Publications: 2.
My Dose Coach Mobile App to Support Insulin Titration and Maintenance
ClinicalTrials.gov study NCT04678661. IPD Sharing: YES. Countries: 1. Publications: 3.
Multimodal Mobile Intervention Application (App) to Address Sexual Dysfunction in Hematopoietic Stem Cell Transplant Survivors
ClinicalTrials.gov study NCT03967379. IPD Sharing: YES. Countries: 1. Publications: 0.
Analyzing App Store Comments and Quality Attributes for Defining an Inspection Checklist for Mobile Educational Games
<p>To evaluate educational games, several techniques have been proposed considering different quality attributes. However, there are still several educational games for the mobile context that have low scores in the app stores. These stores allow users to make comments to evaluate the applications, as this data can be useful for the development team that aims to meet users' expectations. The analysis of comments made by users can help identify which attributes impact the use of mobile educational games. In this paper, an inspection checklist is proposed to evaluate mobile educational games. To complement the attributes identified in the analysis of comments, attributes from existing techniques for evaluating mobile educational games were also considered. The final evaluation form contains a total of 82 attributes distributed in evaluation categories, such as: user interface, mobility, pedagogy, gameplay, among others. To evaluate the proposed technique, an evaluation was carried out with the checklist in two mobile educational games available in the Google Play Store, different from those used to define the technique. The initial results indicate that the proposed checklist allows the identification of problems pointed out by users in the comments left in the app store.</p> <p> </p> <p><a href="https://zenodo.org/api/files/8ca1af4d-6f7c-440f-a272-9366de2ad3ef/SBES%202020%20-%20Ideias%20Inovadoras%20e%20Resultados%20Emergentes%20-%20Analisando%20atributos%20de%20qualidade%20e%20coment%C3%A1rios%20de%20lojas%20de%20aplicativos%20para%20a%20defini%C3%A7%C3%A3o%20de%20uma%20t%C3%A9cnica%20de%20inspe%C3%A7%C3%A3o%20de%20jogos%20educacionais%20m%C3%B3veis.mp4">SBES 2020 - Ideias Inovadoras e Resultados Emergentes</a></p>
Unveiling Competition Dynamics in Mobile App Markets through User Reviews
<p>This replication package contains the datasets and evaluation results for the research titled <i>"<strong>Unveiling Competition Dynamics in Mobile App Markets through User Reviews"</strong>, </i>by Quim Motger, Xavier Franch, Vincenzo Gervasi and Jordi Marco.</p><p>Latest version of the full code is available at: <a href="https://github.com/quim-motger/app-market-analysis">https://github.com/quim-motger/app-market-analysis</a></p>
The codes and datasets for the paper titled "Don't Confuse! Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users"
<h3>Project Title:</h3> <p>Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users</p> <h3>Description:</h3> <p>This project enhances GUI navigation accessibility for visually impaired users by analyzing GUI structures, identifying issues, and optimizing navigation flow.</p> <h3>Contents:</h3> <ol> <li><strong>Risk Warnings</strong></li> <li><strong>Variable Explanations</strong></li> <li><strong>Function Descriptions</strong></li> <li><strong>Usage Instructions</strong></li> <li><strong>Contact Information</strong></li> </ol> <h3>1. Risk Warnings:</h3> <ul> <li>Navigation analysis focuses on visible nodes only.</li> <li>Code maintenance issue in loop C.</li> <li>Prior reading of the "Info" button warning is essential.</li> <li>Potential information loss in the reordering algorithm.</li> </ul> <h3>2. Variable Explanations:</h3> <ul> <li>Constants: parameter1, parameter2, outputSign.</li> <li>Global Variables: nodeString, nodeList, intToRect, intToDir, intToInfo, infoToInt, intToSubRect, intToSubKind.</li> <li>Local Variables: sortedSon, sign, acceptable.</li> </ul> <h3>3. Function Descriptions:</h3> <ul> <li><strong>isNodeVisibleOnScreen</strong>: Checks if a node is visible on the screen.</li> <li><strong>Gestalt</strong>: Conducts a depth-first search traversal of all nodes and records the Gestalt_inspired order.</li> <li><strong>checkNodeNecessity</strong>: Further checks if a node is necessary for navigation.</li> <li><strong>DFS</strong>: Used for the reordering algorithm.</li> </ul> <h3>4. Usage Instructions:</h3> <ul> <li>Ensure thorough understanding of risk warnings.</li> <li>Modify and maintain the code as necessary.</li> <li>Read the warning prompt before using the "Info" button.</li> <li>Exercise caution with potential information loss in reordering.</li> </ul> <h3>5. Contact Information:</h3> <ul> <li>Developer: Mengxi Zhang</li> <li>Email: <a target="_new">zmxalakay@126.com</a></li> </ul> <p><strong>Note</strong>: This README provides a brief overview. Refer to the User_Guidelines documentation for detailed information.</p>
PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing
<p>Collected data for paper: PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing</p>
FPCA - From mobile app-based crowdsourcing to crowd-trusted food price estimates in Nigeria: pre-processing and post-sampling strategy for optimal statistical inference
<p>Timely and reliable monitoring of commodity food prices is an essential requirement for the assessment of market and food security risks and the establishment of early warning systems, especially in developing economies. However, data from regional or national systems for tracking changes of food prices in sub-Saharan Africa lacks the temporal or spatial richness and is often insufficient to inform targeted interventions. In addition to limited opportunity for [near-]real-time assessment of food prices, various stages in the commodity supply chain are mostly unrepresented, thereby limiting insights on stage-related price evolution. Yet, governments and market stakeholders rely on commodity price data to make decisions on appropriate interventions or commodity-focused investments. Recent rapid technological development indicates that digital devices and connectivity services are becoming affordable for many, including in remote areas of developing economies. This offers a great opportunity both for the harvesting of price data (via new data collection methodologies, such as crowdsourcing/crowdsensing — i.e. citizen-generated data — using mobile apps/devices), and for disseminating it (via web dashboards or other means) to provide real-time data that can support decisions at various levels and related policy-making processes. However, market information that aims at improving the functioning of markets and supply chains requires a continuous data flow as well as quality, accessibility and trust. More data does not necessarily translate into better information. Citizen-based data-generation systems are often confronted by challenges related to data quality and citizen participation, which may be further complicated by the volume of data generated compared to traditional approaches. Following the food price hikes during the first noughties of the 21st century, the European Commission's Joint Research Centre (JRC) started working on innovative methodologies for real-time food price data collection and analysis in developing countries. The work carried out so far includes a pilot initiative to crowdsource data from selected markets across several African countries, two workshops (with relevant stakeholders and experts), and the development of a spatial statistical quality methodology to facilitate the best possible exploitation of geo-located data. Based on the latter, the JRC designed the Food Price Crowdsourcing Africa (FPCA) project and implemented it within two states in Northern Nigeria. The FPCA is a credible methodology, based on the voluntary provision of data by a crowd (people living in urban, suburban, and rural areas) using a mobile app, leveraging monetary and non-monetary incentives to enhance contribution, which makes it possible to collect, analyse and validate, and disseminate staple food price data in real time across market segments. The granularity and high frequency of the crowdsourcing data open the door to real-time space-time analysis, which can be essential for policy and decision making and rapid response on specific geographic regions. <a href="https://datam.jrc.ec.europa.eu/datam/perm/news/870?rdr=1666109837893">Link to the project</a></p>
Data for "Boosting propagule transport models with individual-specific data from mobile apps"
<ol> <li>Management of invasive species and pathogens requires information about the traffic of potential vectors. Such information is often taken from vector traffic models fitted to survey data. Here, user-specific data collected via mobile apps offer new opportunities to obtain more accurate estimates and to analyze how vectors' individual preferences affect propagule flows. However, data voluntarily reported via apps may lack some trip records, adding a significant layer of uncertainty. We show how the benefits of app-based data can be exploited despite this drawback.</li> <li>Based on data collected via an angler app, we built a stochastic model for angler traffic in the Canadian province of Alberta. There, anglers facilitate the spread of whirling disease, a parasite-induced fish disease. The model is temporally and spatially explicit and accounts for individual preferences and repeating behaviour of anglers, helping to address the problem of missing trip records.</li> <li>We obtained estimates of angler traffic between all subbasins in Alberta. The model's accuracy exceeds that of direct empirical estimates even when fewer data were used to fit the model. The results indicate that anglers' local preferences and their tendency to revisit previous destinations reduce the number of long inter-waterbody trips potentially dispersing whirling disease. According to our model, anglers revisit their previous destination in 64% of their trips, making these trips irrelevant for the spread of whirling disease. Furthermore, 54% of fishing trips end in individual-specific spatially contained areas with mean radius of 54.7 km. Finally, although the fraction of trips that anglers report was unknown, we were able to estimate the total yearly number of fishing trips in Alberta, matching an independent empirical estimate.</li> <li>We make two major contributions: (1) we provide a model that uses mobile app data to boost the mechanistic accuracy of classic propagule transport models, and (2) we demonstrate the importance of individual-specific behaviour of vectors for propagule transport. Ignoring vectors' local preferences and their tendency to revisit previous destinations can lead to significant overestimates of vector traffic and biased estimates of propagule flows. This has clear implications for the management of invasive species and animal diseases.</li> </ol>
Wireframe CS Mobile App
<p>Wireframe CS Mobile App proposal</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.