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opencc-by-4.0Apr 2024View details →

Empty plots layer screenshot of the Kunbaja online resource

Secondary information layer "empty plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format

opencc-by-4.0Apr 2024View details →

Magyar plots layer screenshot of the Kunbaja online resource with Ungar plots highlighted

Secondary information layer "magyar plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format with Ungar plots highlighted

Female plots layer screenshot of the Kunbaja online resource

Secondary information layer "female plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format

opencc-by-4.0Nov 2021View details →

UIS Log: Synthetic User Interface with Screenshots Log

These data correspond to the set of problems for evaluating the proposal detailed in Martínez-Rojas et al. 2022. The evaluation utilizes a set of synthetic problems that simulate realistic administrative use cases. Each problem includes a UI Log with a synthetic screenshot corresponding to each event, capturing 3 distinct processes (P) marked by varying complexity levels. These levels are defined by the number of activities, the process execution variants, and the visual features influencing decisions between these variants. The implementation of this proposal can be found in the tool available at <a href="https://github.com/RPA-US/screenrpa" target="_new">this GitHub repository</a>, which utilizes the logs of these 3 processes for validation. Here they are described: <ul> <li>P1 Client creation. A process with 5 activities and 2 variants. The single decision in this process is made based on the existence of an attachment in the reception email.</li> <li>P2 Client validation. A process with 7 activities and 2 variants. The decision is made based on the user’s response to a query.</li> <li>P3 Client deletion. A process with 7 activities and 4 variants. The decisions are made based on two conditions: (1) the existence of pending invoices and (2) the existence of an attachment to justify the payment of the invoices.</li> </ul> These processes all contain a single decision point, although the one in P3 is complex. All processes include <ol> <li>synthetic screen captures for their activities and</li> <li>a sample event log with a single instance for each variant.</li> </ol> To generate the objects for the valuation, we generate event logs of different sizes (|L|) for each of these processes by deriving events from the sample event log. We consider log sizes in the range of {10, 25, 50, 100} events. Note that we consider complete instances in the log and thus, we remove the last instance if it goes beyond |L|. Some of these logs are generated with a balanced number of instances, while others are unbalanced (B?) which present more than 20% of different frequency between the most frequent and less frequent variants. To average the result over a collection of problems, 30 instances are randomly generated for each tuple < P, |L|, B? >. In this dataset there are 3 zips, one for each family. Each family corresponds to a process: <ul> <li>Basic corresponds to P1</li> <li>Intermediate corresponds to P12</li> <li>Advanced corresponds to P3</li> </ul> Within these folders, we find 30 different scenarios (folder), in which the look and feel of the applications present in the screenshots have suffered little variations. Within each of these scenarios, variations are carried out respecting to the data entered in the forms and the images or attachments present in the user interface to generate log instances depending on the characteristics of each process. For each scenario, we find 8 folders with the concrete problem which is defined by Log_size (in {10,25,50,100}) and Balanced (in {Balanced, Unbalanced}). The name of these folders have this format: Family_LogSize_Balanced. Inside each problem folder the UI Log and the screen captures can be found. References Martínez-Rojas, A., Jiménez-Ramírez, A., Enríquez, J. G., & Reijers, H. A. (2022, September). Analyzing variable human actions for robotic process automation. In International Conference on Business Process Management (pp. 75-90). Cham: Springer International Publishing.

opencc-by-4.0Jun 2012View details →

Figure 2. Some screenshots from the software system-Design and Development of a Software System for Swarm Intelligence Based Research Studies

All of the mentioned operations can be performed easily by using the provided controls over the related interfaces – windows of each algorithm. It is also important that each algorithm interface is supported by visual controls to view obtained results with typical iteration-based graphics or problem oriented visual elements. For instance, resulting graph structures are automatically shown by the algorithm interfaces after solving some specific, popular problems like Travelling Salesman Problem (TSP), Vehicle Routing Problem (VCP)…etc. Visually improved using features and functions of the software system are critical aspects to provide more effective and useful platform to perform SI based research studies better. Related to the designed and developed software system, some screenshots from the software system [interfaces of two algorithms (IWDs and ABC)] are represented in Fig. 2.

opencc-by-4.0Aug 2015View details →

Figure 3. Screenshot of the MQTT broker, publisher, and two subscribers.-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology

As it was mentioned above, IoT needs the appropriate lightweight protocols to transmit the info because web-protocols (e.g. TCP) generate several times more traffic usually for IoT (e.g. remote connection to the Arduino weather station). MQTT (Message Queuing Telemetry Transport) and CoAP (Constrained Application Protocol) IoT protocols are mainly in use nowadays (http://postscapes.com/internet-of-things-protocols). In this activity,Arduino Ethernet Shield and C# console app are connected by MQTT Mosquitto open source software (http://mosquitto.org).Similar work presented against https://iotguys.wordpress.com/2014/11/13/arduino-with-mqtt/.The activity consists of the following steps: 1. Download and installation of Mosquitto software gainst http://mosquitto.org/download/. 2. Download and installation of the latest Arduino software against http://arduino.cc/en/main/software. 3. Development of the MQTT subscriber based on C programming language in Arduino IDE. 3. Development of the MQTT subscriber based on C# console app (laptop HP ProBook 650 G1 and Windows 10 are used) in Visual Studio. Screen shot of the software is shown in Fig. 3.

opencc-by-4.0Aug 2015View details →

Figure 2. Screenshot of the Google Earth web-site's prototype on the visualization of heat/cold waves-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology-

A non-anticipative analog method consists of four main steps: 1. Generation of the prediction rules. 2. Analysis of the prediction rules. The rules with time slots, which are not concentrated at the same frame, are excluded. 3. Generation of possible extremes. 4. Analysis of the generated possible extremes. The extremes with time slots, which do not correspond to the time slots of the appropriate rules, are excluded. The results of the heat/cold waves’ prediction from 2011 to 2014 at different locations (places are selected randomly) are presented in Table 2.

opencc-by-4.0Jul 2017View details →

BRAIN Journal-A New Challenge for Information Mining-Figure 2. A ScreenShot of L4All Portal

Simple selection or complex selection operations, with boolean operators, are possible. Each widget shows the value of the attributes for the current state of the dataset with different visualization. The current set of objects is shown on a “canvas” (see Figure 2 - right side of the interface). Thanks to advanced Human-Computer Interaction mechanisms, the portal can support sophisticated exploration activities in the cycle . Based on L4All, a number of scientific investigations by different research groups took place: on the relation between different forms of group-work and inclusion, on digital storytelling and related benefits, etc. (Di Blas, Paolini, 2013; Falcinelli, 2012; Falcinelli, Laici, 2012).

Tohoku Tsunami, March 11 2011: GoogleEarth-Screenshot of Saip tide gauge station at 10:08 hours CET

GoogleEarth-Screenshot of Saip tide gauge station on March 11 at 10:08 hours CET. Tide gauge station live data provided from Marine Obs by Program - National Data Buoy Center - NOAA (<a href="https://deref-gmx.net/mail/client/_W-lmx-gCeY/dereferrer/?redirectUrl=http%3A%2F%2Fwww.ndbc.noaa.gov%2Fkml%2Fmarineobs_by_pgm.kml">www.ndbc.noaa.gov/kml/marineobs_by_pgm.kml</a>).

Tohoku Tsunami, March 11 2011: GoogleEarth-Screenshot of Wake tide gauge station at 10:07 hours CET

GoogleEarth-Screenshot of Wake tide gauge station  on March 11 at 10:07 hours CET. Tide gauge station live data provided from Marine Obs by Program - National Data Buoy Center - NOAA (<a href="https://deref-gmx.net/mail/client/_W-lmx-gCeY/dereferrer/?redirectUrl=http%3A%2F%2Fwww.ndbc.noaa.gov%2Fkml%2Fmarineobs_by_pgm.kml">www.ndbc.noaa.gov/kml/marineobs_by_pgm.kml</a>).

Tohoku Tsunami, March 11 2011: GoogleEarth-Screenshot of Hansaki tide gauge station at 09:56 hours CET

GoogleEarth-Screenshot of Hanasaki tide gauge station  on March 11 at 09:56 hours CET. Tide gauge station live data provided from Marine Obs by Program - National Data Buoy Center - NOAA (<a href="https://deref-gmx.net/mail/client/_W-lmx-gCeY/dereferrer/?redirectUrl=http%3A%2F%2Fwww.ndbc.noaa.gov%2Fkml%2Fmarineobs_by_pgm.kml">www.ndbc.noaa.gov/kml/marineobs_by_pgm.kml</a>).

A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 3. Screenshots of the new VC app

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.

A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 2. Screenshots of the Virtual Campus mobile application (d, e)

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.

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)

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.

opencc-by-4.0Jan 2019View details →

The ReDraw Dataset: A Set of Android Screenshots, GUI Metadata, and Labeled Images of GUI Components

This is the dataset used to train and evaluate the CNN and KNN machine learning techniques for the ReDraw paper, published in IEEE Transactions on Software Engineering in 2018. Link to ReDraw Paper: https://arxiv.org/abs/1802.02312

opencc-by-4.0Dec 2022View details →

gaitXplorer Screenshot

This upload contains open-access screenshots of the gaitXplorer prototype. gaitXplorer was originally presented at the 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX). Preprint: A Rind, D Slijepčević, M Zeppelzauer, F Unglaube, A Kranzl, B Horsak (2022). Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy. arXiv:2208.05232

opencc-by-4.0Jun 2023View details →

Mapping User Attention: Filtering and Visualizing Relevant UI Components in Screenshots based on Gaze Fixations

These data correspond to the set of problems used for the evaluation of the proposal What Are You Gazing At? An Approach to Use Eye-tracking for Robotic Process Automation. Each problem consists of a set of 10 screenshots with the same look and feel but different data values for those values that can be entered/modify by the user. Each problem has its associated gaze fixation data. In each of the problems there is a key UI element that primarily attracts the attention of the user. The evaluation is based on a set of images which resemble realistic screenshots of activities in the administrative domain. More precisely, 5 different set of screenshots (S) are generated, each of them with a different level of complexity. Complexity is measured in terms of the number of UI elements per screenshot. The sets are: <ul> <li>S1 Mockup-based email view. Represents the activity of viewing an email to check if it contains an attachment. In this case, the key UI element that receives the attention is the attachment inside the email.</li> <li>S2 Mockup-based CRM user details. Represents a user's detail viewing activity within a Client Relationship Management (CRM) platform. The key UI element is the checkbox that indicates if the user has all his invoices paid.</li> <li>S3 Real screenshot email view. Analogous to S1 but with real screenshots. It represents the activity of viewing an e-mail to check if it contains an attachment. In this case, the key UI element to which attention is paid is the attachment contained in the e-mail.</li> <li>S4 Real screenshot CRM user details. Analogous to S2 but with real screenshots. It represents a user's detail viewing activity within a CRM platform. The key UI element is the checkbox indicating whether the user has all their invoices paid.</li> <li>S5 Real screenshot CRM user details. Represents the split-screen display of two applications. On the left side a pdf viewer, showing a covid vaccination certificate. And on the right side a human resources management system (basic recreation of real system for privacy reasons). In this one the detail of the employee to whom the certificate of the left side corresponds is visualized. These screenshots, having two applications, have two key UI elements. In the pdf viewer it is the name of the certificate holder and in the human resources management system it is the name of the employee whose detail view is being displayed. The activity being carried out is the verification that the covid certificate received corresponds to that of an employee.</li> </ul> Two types of filters based on the gaze fixation data are applied to these sets of screenshots: Pre-filtering and Post-filtering, corresponding to applying the filtering before and after detecting UI components in the screenshots, respectively. The structure of the data packages is divided in two folders input and output. The input folder is organized as follows: input/ <ul> <li>screenshots/: corresponds to the screenshots. The sets of screenshots are easily identifiable, they are named following the pattern: SX_screenshot_DDDD.jpeg. Where X indicates to which of the set of screenshots described in the previous list it belongs, and DDDD represents a unique identifier for each screenshot. Each group consists of 10 screenshots, being 50 in total.</li> <li>fixation.json: It is a JSON file that contains a key associated with each of the screenshots. For each screenshot, it contains a "fixation_points" key where information about the fixations that have occurred on the screenshot is stored. Here's an example: <pre><code> "S5_screenshot_0050.jpeg": { "fixation_points": { "334.25#497.166666666667": { "#events": 6, "start_index": 33224, "ms_start": 553962.1467, "ms_end": 554061.9899, "duration": 99.8432000001194, "imotions_dispersion": 0.300325967868111, "last_index": 33229, "dispersion": 14.044275227531914 }, "1258.80769230769#507.576923076923": { "#events": 13, "start_index": 33234, "ms_start": 554128.5427, "ms_end": 554345.3595, ...</code></pre> </li> </ul> The output folder is organized in three subfolders, the first one containing the information of the non-filtered screenshots (i.e. without having applied to them any filtering or processing), and the next two with the information resulting from pre-filtering and post-filtering. output/ <ul> <li>non-filter/ <ul> <li>borders/: screenshots with highlighted borders of all UI components detected in it.</li> <li>components_json/: a collection of JSON files with the same name as the screenshot, containing the "img_shape" key with a list of the screen resolution and the number of layers the image has: [1080, 1920, 3], and the "compos" key with a list of all UI components representing the Screen Object Model.  </li> </ul> </li> <li>pre-filter/ and post-filter/ <ul> <li>borders/: screenshots with the borders of the relevant UI components. In the case of prefiltering, the detection of components is only performed on the parts of the screenshot that have received attention. In postfiltering, the complete screenshot is shown, with only the borders of the relevant UI components highlighted.</li> <li>components_json/: a collection of JSON files with the same name as the screenshot is included, containing the following keys: <ul> <li>"img_shape": A list representing the screen resolution and the number of layers in the image, e.g., [1080, 1920, 3].</li> <li>"compos": A list of all UI components representing the Screen Object Model (SOM). During post-filtering, each UI component is augmented with an additional property called "relevant." If this property is set to true, it indicates that the respective UI component has received attention.</li> </ul> </li> <li>(pre)/(post)filter_attention_maps/: represent the attention maps. In the case of prefiltering, any surface of the screen that has not received attention will be shown in black. In the case of postfiltering, the areas of attention will be shown as red circles, and the UI components whose area intersects with the areas of attention by more than 25% will be shown in yellow.</li> </ul> </li> </ul> In conclusion, the described data package consists of sets of screenshots, accompanied by prefiltering and postfiltering filters using gaze fixation data, enabling the identification of relevant UI components. The organized data packages include input and output folders, where the output folder offers processed screenshots, UI component information, and attention maps. This resource provides valuable insights into user attention and interaction with UI elements on different types of scenarios.

Village sections layer screenshot of the Kunbaja online resource

Information layer "village sections" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format

Road names layer screenshot of the Kunbaja online resource

Information layer "road names" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format

Hydro features layer screenshot of the Kunbaja online resource

Information layer "hydro features" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format