SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES COMPUTERS AND INTERNET USE - DP02 Universe - Total households Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 The 2008 Broadband Improvement Act mandated the collection of data about computer and internet use. As a result, three questions were added to the 2013 American Community Survey (ACS) to measure these topics. The computer use question asked if anyone in the household owned or used a computer and included four response categories for a desktop or laptop, a smartphone, a tablet or other portable wireless computer, and some other type of computer. Respondents selected a checkbox for “Yes” or “No” for each response category. Respondents could select all categories that applied. Question asked if any member of the household has access to the internet. “Access” refers to whether or not someone in the household uses or can connect to the internet, regardless of whether or not they pay for the service. If a respondent answers “Yes, by paying a cell phone company or Internet service provider”, they are asked to select the type of internet service.

Statistics of how many adults access the internet and use different types of technology covering: home internet access how people connect to the web how often people use the web/computers whether people use mobile devices whether people buy goods over the web whether people carried out specified activities over the internet For more information see the ONS website and the UKDS website.

Ziel dieser Studie war es, den Einfluss verschiedener Anreizsysteme auf die Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung unter deutschen Smartphone-Besitzern experimentell zu messen. Die Daten stammen aus einer Webumfrage unter deutschen Smartphone-Nutzern ab 18 Jahren, die aus einem deutschen, nicht wahrscheinlichen Online-Panel rekrutiert wurden. Im Dezember 2017 beantworteten 1.214 Teilnehmer einen Fragebogen zu den Themen Smartphone-Nutzung und -Fähigkeiten, Datenschutz und Sicherheit, allgemeine Einstellungen gegenüber der Umfrageforschung und Forschungseinrichtungen. Darüber hinaus enthielt der Fragebogen ein Experiment zur Bereitschaft, an der mobilen Datenerhebung unter verschiedenen Anreizbedingungen teilzunehmen. Themen: Besitz von Smartphone, Handy, Desktop- oder Laptop-Computer, Tablet-Computer und/oder E-Book-Reader; Art des Smartphones; Bereitschaft zur Teilnahme an der mobilen Datenerfassung unter verschiedenen Anreizbedingungen; Wahrscheinlichkeit des Herunterladens der App zur Teilnahme an dieser Forschungsstudie; Befragter möchte lieber an der Studie teilnehmen, wenn er 100 Euro erhalten könnte; Gesamtbetrag, den der Befragte für die Teilnahme an der Studie verdienen müsste (offene Antwort); Grund, warum der Befragte nicht an der Forschungsstudie teilnehmen würde; Bereitschaft zur Teilnahme an der Studie für einen Anreiz von insgesamt 60 Euro; Bereitschaft zur Aktivierung verschiedener Funktionen beim Herunterladen der App (Interaktionshistorie, Smartphone-Nutzung, Merkmale des sozialen Netzwerks, Netzqualitäts- und Standortinformationen, Aktivitätsdaten); vorherige Einladung zum Herunterladen der Forschungs-App; Herunterladen der Forschungs-App; Häufigkeit der Nutzung des Smartphones; Smartphone-Aktivitäten (Browsen, E-Mails, Fotografieren, Anzeigen/Post-Social-Media-Inhalte, Einkaufen, Online-Banking, Installieren von Apps, Verwenden von GPS-fähigen Apps, Verbinden über Bluethooth, Spielen, Streaming von Musik/Videos); Selbsteinschätzung der Kompetenz im Umgang mit dem Smartphone; Einstellung zu Umfragen und Teilnahme an Forschungsstudien (persönliches Interesse, Zeitverlust, Verkaufsgespräch, interessante Erfahrung, nützlich); Vertrauen in Institutionen zum Datenschutz (Marktforschungsunternehmen, Universitätsforscher, Regierungsbehörden wie das Statistische Bundesamt, Mobilfunkanbieter, App-Unternehmen, Kreditkartenunternehmen, Online-Händler und Social-Media-Plattformen); allgemeine Datenschutzbedenken; Gefühl der Datenschutzverletzung durch Banken und Kreditkartenunternehmen, Steuerbehörden, Regierungsbehörden, Marktforschung, soziale Netzwerke, Apps und Internetbrowser; Bedenken zur Datensicherheit bei Smartphone-Aktivitäten für Forschungszwecke (Online-Umfrage, Umfrage-Apps, Forschungs-Apps, SMS-Umfrage, Kamera, Aktivitätsdaten, GPS-Ortung, Bluetooth). Demographie: Geschlecht, Alter; Bundesland; höchster Schulabschluss; höchstes berufliches Bildungsniveau. Zusätzlich verkodet wurden: laufende Nummer; Dauer (Reaktionszeit in Sekunden); Gerätetyp, mit dem der Fragebogen ausgefüllt wurde. The goal of this study was to experimentally measure the influence of different incentive schemes on the willingness to participate in passive mobile data collection among German smartphone owners. The data come from a web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2017, 1,214 respondents completed a questionnaire on smartphone use and skills, privacy and security concerns, general attitudes towards survey research and research institutions. In addition, the questionnaire included an experiment on the willingness to participate in mobile data collection under different incentive conditions. Topics: Ownership of smartphone, cell phone, desktop or laptop computer, tablet computer, and/or e-book reader; type of smartphone; willingness to participate in mobile data collection under different incentive conditions; likelihood of downloading the app to particiapte in this research study; respondent would rather participate in the study if he could receive 100 euros; total amount to be earned for the respondent ot participate in the study (open answer); reason why the respondent wouldn´t participate in the research study; willlingness to participate in the study for an incentive of 60 euros in total; willingness to activate different functions when downloading the app (interaction history, smartphone usage, charateristics of the social network, network quality and location information, activity data); previous invitation for research app download; research app download; frequency of smartphone use; smartphone activities (browsing, e-mails, taking pictures, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, playing games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, government authorities such as the Federal Statistical Office, mobile service provider, app companies, credit card companies, online retailer, and social media platforms); general privacy concern; feeling of privacy violation by banks and credit card companies, tax authorities, government agencies, market research, social networks, apps, and internet browsers; concern regarding data security with smartphone activities for research purposes (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth). Demography: sex, age; federal state; highest level of school education; highest level of vocational education. Additionally coded was: running number; duration (response time in seconds); device type used to fill out the questionnaire.

English(North America) Scripted Monologue Smartphone and PC speech dataset, collected from monologue based on given scripts, covering common expressions. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(302 North American), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

Internetnutzung. Themen: Internetzugang im Haushalt; Art des Internetzugangs (z.B. über Telefon, ISDN, ADSL, via Kabel oder WLAN); Art der Hardware für den Internetzugang (z.B. PC, Laptop, TV set-top box, Spielkonsole, Mobiltelefon mit WAP, Palm); vorhandene Sicherheitsvorrichtungen am Computer mit Internetzugang (Anti-Virus Software, smart card reader, Verschlüsselungssoftware, Firewall, elektronische Signatur); Orte der Internetnutzung (zu Hause, Arbeitsplatz, Schule, public access point, Cybercafe usw.); Nutzungshäufigkeit; aufgetretene Sicherheitsprobleme bei der Internetnutzung (Virenbefall, Missbrauch der Kreditkartennummer, Spam); durchgeführte Behördenkontakte via Internet; Häufigkeit privater Einkäufe im Internet und Art dabei aufgetretener Probleme (verspätete Lieferung, fehlgeschlagene Lieferungen, keine Rückgabemöglichkeit, Probleme bei der Bezahlung, unzureichende Preisinformationen, schlechter Kundenservice, Missbrauch der persönlichen Daten z.B. in Form von Spam, Probleme mit der Navigation auf den Webseiten der Anbieter, unlautere/irreführende Angebote); Herkunftsregion der Anbieterseiten bei privaten Interneteinkäufen; weitere Nutzungsarten für das Internet: E-Mails, Online-Banking, Nachrichten und Informationsbeschaffung, Gesundheitsinformationen, Jobangebote, Teilnahme an Foren und Chats, Weiterbildung, Reiseinformationen, Ticketbuchungen. Demographie: Geschlecht; Alter; Studienstatus; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Land; Fragebogennummer; Gewichtungsfaktor. Internet use and online activities. Topics: internet connection at home; type of internet access in the household: standard telephone line, ISDN, ADSL, special modem using the television cable, mobile / wireless connection; internet connection via: desktop computer, laptop, TV set-top box, video game console, mobile telephone, handheld / pocket computer; security features of the aforementioned device: antivirus software, smart card reader or other authentication device, encryption software, firewall software, electronic signature software, other security feature; internet use (at home, at work, at school); frequency of internet use; experienced security problems while using the internet: computer virus, fraudulent use of credit card number, unsolicited e-mail (spamming), other security problems, no problems; contact to public administration via the internet: to find administrative information, send e-mail, carry out procedures online, other reasons, no contact; frequency of purchasing products or services via the internet; experienced problems with regard to shopping on the internet: late delivery, product or service not delivered, no possibility to return faulty or unwanted goods, payment problems, unclear pricing, unsatisfactory communication, unauthorized use of personal data, website navigation problems, misleading offer; purchase of goods and services via websites in the following regions: EU, in Europe but outside the EU, North America, Latin America, Africa, Asia, Oceania; online activities: email, banking, read news, seek health-related information, find job ads, take part in forums, improve training or education, seek information on travels, book event tickets. Demography: sex; age; current type of education; age at end of education; occupation; professional position; region; type of community; household composition and household size. Additionally coded was: country; questionnaire number; weighting factor. Telephone interview Bevölkerung im Alter von 15 Jahren und älter

Internetnutzung und Online-Aktivitäten. Themen: Internetzugang im Haushalt; Art des Internetzugangs (z.B. ISDN, ADSL, WLAN); Art der Hardware für den Internetzugang (z.B. PC, Laptop, digital TV, Spielkonsole, Mobiltelefon mit WAP, Palm); vorhandene Sicherheitsvorrichtungen an einem Computer mit Internetzugang (Anti-Virus Software, Firewall, elektronische Signatur usw.); Orte der Internetnutzung (zu Hause, Arbeitsplatz, Schule, public access point, Cybercafe usw.); Nutzungshäufigkeit; aufgetretene Sicherheitsprobleme bei der Internetnutzung (Virenbefall, Missbrauch der Kreditkartennummer, Spam); durchgeführte Behördenkontakte via Internet; Häufigkeit privater Einkäufe im Internet; Art der aufgetretenen Probleme bei Interneteinkäufen; Herkunftsregion der Anbieterseiten bei privaten Interneteinkäufen; weitere Nutzungsarten für das Internet: E-Mails, online banking, Nachrichten und Informationsbeschaffung, Gesundheitsinformationen, Jobangebote, Teilnahme an Foren und Chats, Weiterbildung, Reiseinformationen, Ticketbuchungen; Vorhandensein einer chronischen Krankheit oder dauerhaften Behinderung beim Befragten sowie dadurch verursachte Einschränkung des Alltagslebens. Demographie: Geschlecht; Alter; Ausbildungsgrad; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Region; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Fragebogennummer; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Gewichtungsfaktor. Internet use and online activities. Topics: internet connection at home; type of internet access in the household: standard telephone line, ISDN, ADSL, special modem using the TV cable, mobile / wireless connection; internet connection via: desktop computer, laptop, TV set-top box, video game console, mobile telephone, handheld / pocket computer; security features of the aforementioned device: antivirus software, smart card reader or other authentication device, encryption software, firewall software, electronic signature software, other security feature; internet use (at home, at work, at school); frequency of internet use; experienced security problems while using the internet: computer virus, fraudulent use of credit card number, unsolicited e-mail (spamming), other security problems, no problems; contact to public administration via the internet: to find administrative information, send e-mail, carry out procedures online, other reasons, no contact; frequency of purchasing products or services via the internet; experienced problems with regard to shopping on the internet: late delivery, product or service not delivered, no possibility to return faulty or unwanted goods, payment problems, unclear pricing, unsatisfactory communication, unauthorized use of personal data, website navigation problems, misleading offer; purchase of goods and services via websites in the following regions: EU, in Europe but outside the EU, North America, Latin America, Africa, Asia, Oceania; online activities: email, banking, read news, seek health-related information, find job ads, take part in forums, improve training or education, seek information on travels, book event tickets; chronic physical or mental health problem; limitation in daily activities due to health problems. Demography: sex; age; current type of education; age at end of education; occupation; professional position; type of community; region; household composition and household size. Additionally coded was: country; questionnaire number; date of interview; time of the beginning of the interview; duration of the interview; weighting factor.

Login Data Set for Risk-Based Authentication

Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

Overview

The data set contains the following features related to each login attempt on the SSO:

    Feature
    Data Type
    Description
    Range or Example




    IP Address
    String
    IP address belonging to the login attempt
    0.0.0.0 - 255.255.255.255


    Country
    String
    Country derived from the IP address
    US


    Region
    String
    Region derived from the IP address
    New York


    City
    String
    City derived from the IP address
    Rochester


    ASN
    Integer
    Autonomous system number derived from the IP address
    0 - 600000


    User Agent String
    String
    User agent string submitted by the client
    Mozilla/5.0 (Windows NT 10.0; Win64; ...


    OS Name and Version
    String
    Operating system name and version derived from the user agent string
    Windows 10


    Browser Name and Version
    String
    Browser name and version derived from the user agent string
    Chrome 70.0.3538


    Device Type
    String
    Device type derived from the user agent string
    (mobile, desktop, tablet, bot, unknown)1


    User ID
    Integer
    Idenfication number related to the affected user account
    [Random pseudonym]


    Login Timestamp
    Integer
    Timestamp related to the login attempt
    [64 Bit timestamp]


    Round-Trip Time (RTT) [ms]
    Integer
    Server-side measured latency between client and server
    1 - 8600000


    Login Successful
    Boolean
    True: Login was successful, False: Login failed
    (true, false)


    Is Attack IP
    Boolean
    IP address was found in known attacker data set
    (true, false)


    Is Account Takeover
    Boolean
    Login attempt was identified as account takeover by incident response team of the online service
    (true, false)

Data Creation

As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

Regarding the Data Values

Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

You can recognize them by the following values:

ASNs with values >= 500.000

IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

Study Reproduction

Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

See RESULTS.md for more details.

Ethics

By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

Publication

You can find more details on our conducted study in the following journal article:

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022) Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono. ACM Transactions on Privacy and Security

Bibtex

@article{Wiefling_Pump_2022, author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi}, title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}}, journal = {{ACM} {Transactions} on {Privacy} and {Security}}, doi = {10.1145/3546069}, publisher = {ACM}, year = {2022} }

License

This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

This dataset contains two significant mobile gestures for brain-mobile phone interfaces (BMPIs: (i) motor imagery of tapping on the screen of a mobile device and (ii) motor imagery of swiping down with a thumb on the screen of a mobile device. The raw EEG signals were recorded using the Emotiv EPOC Flex (Model 1.0) headset with saline-based sensors and Emotiv Pro (2.5.1.227) software. The sampling rate is 128 Hz. Each epoch contains 3.5 s signals. The first 1 s signal is recorded before the MI task starts (5 s to 6 s interval in the timing plan), and the next 2.5 s signal is recorded during the MI execution (6 s to 8.5 s interval in the timing plan). Please refer to the reference study below for details.

The file names are constructed as follows. For example, taking "D01_s1" and "D01" in the file name refers to subject "01", and "s1" refers to session 1 ("s2" refers to session 2). The label data is given in a separate folder in Matlab format.

The data is provided in two different forms for use (the desired is preferable):

The set_files folder contains the data prepared for import in EEGLAB. EEGLAB must be installed, and the set files must be imported to access the data. The data is in epoched format in 3D (channels, sample_points, trials). With the EEGLAB interface, all the data can be accessed, and EEGLAB functions can be executed. Also, the EEG variable, which is built after importing the *.set file, contains all the information about the experiment. With the EEG.data variable, epoched data in the dimensions (channels, sample_points, trials) can be accessed.

The mat_files folder contains data in mat file format. In these files, epoched data is stored in a 3-D array of size (channels, sample_points, trials). You can access the data as follows. For example, all data from the first session of subject D01 can be retrieved as follows. Load the mat file with the load('D01_s1.mat') code, and access the data using the EEG variable in the workspace. For instance, 13x448 x101 sized epoched data (channels, sample_points, trials) can be retrieved with the command EEG.data. Other information about the experiments and subjects is also included in the fields of the EEG variable.

This research was supported by the Turkish Scientific and Research Council (TUBITAK) under project number 119E397.

The following article can be cited in academic studies as follow.

Yilmaz, C.M., Yilmaz, B.H. & Kose, C. MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM. Neural Comput & Applic 37, 4679–4696 (2025). https://doi.org/10.1007/s00521-024-10917-5

Permission must be obtained for use in commercial studies.

This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

halfFace is a dataset that contains facial images of both masked and unmasked images of South Asian people (166 participants). This is a combination of 3 different datasets which include full face, upper face, and augmented upper face images so that it can be used for person identification from both masked and unmasked images and detecting unmasked person. The participants were fully informed about the usage of the dataset and an approval was taken from them for further usage of this data while collecting data from them.

Method Images of the dataset were captured in participant’s environment, and using participant’s smart phone camera. Hence, this dataset contains the diversity of devices. This images were then collected by the authors. Then, three different datasets were developed from the images. In the dataset-1, only facial part was kept using a pretrained CNN model, and it was not divided into train-test images. The facial images of masked and unmasked images can be easily identified by naming convention. In dataset-2, we have excluded the covered face portion by using YOLOV3. So, images of dataset-2 contains only upper part of the face (hair, forehead, eyes, and upper portion of nose), and it was further divided into training-testing parts. As models require a huge amount of data to train, we have increased the number of training images using augmentation method by adjusting brightness, blur, contrast, saturation, gaussian noise, and salt and pepper noise. This has made the dataset-3 even more diverse and noisy which is essential in building robust models.

Usage Notes Dataset-1 : Full face images Datset-2: Upper face without augmentation (divided into train-test) Dataset-3: Upper face with augmentation of training part, testing images were same as dataset-2 Naming convention: Folders: P_XXX where XXX is the identification of participants. Images of dataset-1 and dataset-2: pXXX_(0/1)(0/1/2)(m/f) Here, pXXX is the person identification number. First (0/1) means unmasked (0) or masked(1). (0/1/2) means the image is front facing(0)/ left facing (1), right facing (2). (m/f) denotes the gender of the participant. Images of dataset-3: pXXX_(0/1)(0/1/2)(m/f)_(b/blur/c/GN/s/SPN) Here, pXXX is the person identification number. First (0/1) means unmasked (0) or masked(1). (0/1/2) means the image is front facing(0)/ left facing (1), right facing (2). (m/f) denotes the gender of the participant. “b/blur/c/GN/s/SPN” means change of brightness (b), blur (blur), contrast (c), gaussian noise (GN), saturation (s), Salt and Pepper noise (SPN).

First-person video dataset recorded in daily life situations of 17 participants, annotated by themselves for privacy sensitivity. The dataset of Steil et al. contains more than 90 hours of data recorded continuously from 20 participants (six females, aged 22-31) over more than four hours each. Participants were students with different backgrounds and subjects with normal or corrected- to-normal vision. During the recordings, participants roamed a university campus and performed their everyday activities, such as meeting people, eating, or working as they normally would on any day at the university. To obtain some data from multiple, and thus also “privacy-sensitive”, places on the university campus, participants were asked to not stay in one place for more than 30 minutes. Participants were further asked to stop the recording after about one and a half hours so that the laptop’s battery packs could be changed and the eye tracker re-calibrated. This yielded three recordings of about 1.5 hours per participant. Participants regularly interacted with a mobile phone provided to them and were also encouraged to use their own laptop, desktop computer, or music player if desired. The dataset thus covers a rich set of representative real-world situations, including sensitive environments and tasks.

This dataset provides information about user subscriptions and related details. It includes the following columns:

User ID: A unique identifier for each user.

Serial Number: Another identifier related to the user or their subscription.

Subscription Type: The type of subscription the user has (Basic or Standard).

Monthly Revenue: The revenue generated from the user on a monthly basis.

Join Date: The date when the user joined the subscription service.

Last Payment Date: The date when the user last made a payment.

Country: The country where the user is located.

Age: The age of the user.

Gender: The gender of the user.

Device: The type of device the user typically uses (Tablet, Mobile, Desktop).

Plan Duration: The duration of the user's subscription plan.

Opinions about and use of information- and communication technology. Watching tv and use of other media / use of a bank guarantee card, mobile telephone, fax, telephone services / finding information / use of a computer at home / use of the Internet / e-mail / reasons for not having a computer at home / computer skills / characteristics of work and profession of respondent / characteristics of work and profession of partner of respondent / use of a computer at work / characteristics of social network / opinion concerning use of computers by children / background characteristics of parents / background characteristics of respondent like age, sex, marital status, education, income, religion, ethnicity, and political preference.

The Robot-at-Home dataset (Robot@Home, paper here) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms.

This dataset is unique in three aspects:

The provided data were captured with a rig of 4 RGB-D sensors with an overall field of view of 180°H. and 58°V., and with a 2D laser scanner.

It comprises diverse and numerous data: sequences of RGB-D images and laser scans from the rooms of five apartments (87,000+ observations were collected), topological information about the connectivity of these rooms, and 3D reconstructions and 2D geometric maps of the visited rooms.

The provided ground truth is dense, including per-point annotations of the categories of the objects and rooms appearing in the reconstructed scenarios, and per-pixel annotations of each RGB-D image within the recorded sequences

During the data collection, a total of 36 rooms were completely inspected, so the dataset is rich in contextual information of objects and rooms. This is a valuable feature, missing in most of the state-of-the-art datasets, which can be exploited by, for instance, semantic mapping systems that leverage relationships like pillows are usually on beds or ovens are not in bathrooms.

Robot@Home2

Robot@Home2, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. It consists of three main components. Firstly, a relational database that states the contextual information and data links, compatible with Standard Query Language. Secondly,a Python package for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of Jupyter notebooks, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics.

If you use Robot@Home2, please cite the following paper:

Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, The Robot@Home2 dataset: A new release with improved usability tools, in SoftwareX, Volume 23, 2023, 101490, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2023.101490.

@article{ambrosio2023robotathome2,title = {The Robot@Home2 dataset: A new release with improved usability tools},author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez},journal = {SoftwareX},volume = {23},pages = {101490},year = {2023},issn = {2352-7110},doi = {https://doi.org/10.1016/j.softx.2023.101490},url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863},keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab}}

Version historyv1.0.1 Fixed minor bugs.v1.0.2 Fixed some inconsistencies in some directory names. Fixes were necessary to automate the generation of the next version.v2.0.0 SQL based dataset. Robot@Home v1.0.2 has been packed into a sqlite database along with RGB-D and scene files which have been assembled into a hierarchical structured directory free of redundancies. Path tables are also provided to reference files in both v1.0.2 and v2.0.0 directory hierarchies. This version has been automatically generated from version 1.0.2 through the toolbox.v2.0.1 A forgotten foreign key pair have been added.v.2.0.2 The views have been consolidated as tables which allows a considerable improvement in access time.v.2.0.3 The previous version does not include the database. In this version the database has been uploaded.v.2.1.0 Depth images have been updated to 16-bit. Additionally, both the RGB images and the depth images are oriented in the original camera format, i.e. landscape.

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The presence of business in the digital space is a must now. Indeed, there’s hardly any company, be it a small startup or an international corporation, that wouldn’t be available online. For this, the company may use one of two options — to develop an app or a website, or both.

In the case of a limited budget, business owners often have to make a choice. Thus, considering that mobile traffic bypassed the desktop’s in 2016 and continues to grow, it becomes obvious that the business should become accessible and convenient for smartphone users. But what is better a responsive website or a mobile application?

Entrepreneurs often turn to development companies to ask this question. Lacking sufficient knowledge, they hope to get answers to their questions from people with experience in this field. So, we decided to compile a guide that will give you clear and understandable information.

Mobile app

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Let's look at the stats. It will help you understand why a mobile app may be the obvious choice for your client.

In 2019, smartphone users installed about 204 billion(!) applications on their devices. On average, this is more than 26 applications per inhabitant of the planet Earth. And if this is not enough evidence, here’s one more point. The expected revenue of mobile applications will be $189 billion in 2020.

It sounds impressive, but this does not mean that a mobile application is something indispensable for every business. Not at all. Let's go through the pros and cons of a mobile application and try to understand when it is needed.

Pros

• A new level of interaction. Mobile applications are a more convenient method of interaction. They load and process content faster. One more useful feature is notifications. Perhaps, applications are the best way to inform users about new updates, promotions, and other news (who will read long letters in the mail?).
• Personalized targeting. Mobile applications are ideal for products or services that need to be used on an ongoing basis. The options like creating accounts, entering profile information, etc., make applications more personalized than websites. All this allows the business to target their audience more accurately without wasting money.
• Offline usage. That’s another major advantage. Applications can provide users with access to content without an internet connection.

Cons

• Development costs. In order to reach the maximum audience with a mobile app, it is necessary to cover two main operating systems — iOS and Android. Development for each OS can be too expensive for small business owners and they will have to make difficult choices. The way out of this situation is cross-platform development. Why? Because there’s no need to guess which platform targets prefer using — iOS or Android. Instead, you create just one app that runs seamlessly on both platforms.

• Maintenance. The application is a technical product that needs constant support. Upgrades should be carried out in a timely manner. Often, users need to personally update applications by downloading a new version, which is annoying. Regular bug-fixing for various devices (smartphones, tablets) and different operating systems might be a real problem. Plus, any update should be confirmed by the store where the application is placed.

• Suitable for businesses that provide interactive and personalized content (refers to all lifestyle and healthcare solutions), require regular app usage (for instance, to-do lists), rely on visual interaction and so on. For games, like Angry Birds, creating an app is also a wise choice.

Website

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In order to be convenient for users of mobile devices, a website should be responsive. We want to make an emphasis on this since it is critically important. Most of the traffic on the Internet comes from mobile devices, so your website should be adaptable, or in other words, mobile-friendly. If a mobile user needs to zoom in all the necessary elements and text to see something, they will immediately quit your website.

On the other hand, a responsive website has the following benefits.

Pros

• Maintenance. Maintaining a website is less costly. When compared to applications where the user mu...

Workout Data

The dataset provided includes the logged data of my own strength workouts following the 5/3/1 BBB routine. While some insights were derived in an article I published recently, there is an opportunity for the community to benefit from the open sourcing of this data.

Most notably, I haven't found time to come up with a way of training and applying performance metrics against the data which I have labeled; and I'm hoping that the work I've spent to prepare a decent dataset can be picked up by someone looking to try out computer vision but on a dataset that has a clearer use case than some of the toy datasets that are currently open sourced.

The goal is to try to build an ML model that takes either phone images or scans of workout sheets, and automatically transfer them into the more structured Excel format for easier data gathering.

Content

There are 3 folders contained in the dataset, all files within the folder are datestamped by filename as DD-MM-YYYY: Excel Data This is considerable as the labeled data to a matching phone image or scanned image. There is an Excel file for each workout performed. Phone Images These are images of the filled out workout sheets as taken by my Android phone. More recently I have stopped taking phone images of my workout sheets, but about 85% of the Excel data has a matching phone image. While these images represent a harder challenge for computer vision, the ease of taking these images makes them much more practical as a future deployable mobile application. Scanned Images These are scans of the filled out workout sheets as scanned on my HP Deskjet printer. These scans are higher quality than the mobile images, however the lack of quick and easy access to scanners means that it is harder to gain a userbase as a potential future product.

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In this paper, an exhaustive video dataset categorized as fall and no-fall videos is presented, which was compiled for the specific purpose of fall detection research. The dataset comprises three fundamental classifications of falls, namely those originating from a standing position, bed, or chair. After being initially acquired in unprocessed form, these videos underwent subsequent processing to generate seminal videos, which were presented with and without a black backdrop. The dataset was obtained from voluntary participants through the use of handheld devices (e.g., digital cameras or mobile phones), which ensured ethical compliance and informed assent. The dataset provides a substantial asset for the progression of fall detection algorithms, serving as a resilient framework for the development and evaluation of such algorithms. The implementation of fall detection systems is critical, especially in situations involving elderly individuals who occur during medical emergencies that lead to falls and require immediate assistance, or when individuals are solitary and unable to restore their balance after falling. By utilizing this dataset, scientists have the opportunity to investigate a wide range of methodologies, such as deep learning and computer vision, in order to develop and enhance fall detection systems. This video dataset has the potential to contribute to the development of fall detection technology, thereby improving safety protocols for vulnerable populations, due to its availability to researchers.

Dataset consisting of feature vectors of 215 attributes extracted from 15,036 applications (5,560 malware apps from Drebin project and 9,476 benign apps). The dataset has been used to develop and evaluate multilevel classifier fusion approach for Android malware detection, published in the IEEE Transactions on Cybernetics paper 'DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection'. The supporting file contains further description of the feature vectors/attributes obtained via static code analysis of the Android apps.

This layer shows computer ownership and internet access by age and race. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of population age 18 to 64 in households with no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28005, B28003, B28009B, B28009C, B28009D, B28009E, B28009F, B28009G, B28009H, B28009I Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.