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26 results for “intelligent design”
The Perception of "Intelligent" Design in Visual Structure
<p>Datasets relative to the following publication:</p> <p>Schmidt, F. (2022). The Perception of “Intelligent” Design in Visual Structure. <em>i-Perception, 13(2)</em>, 1-5. https://doi.org/10.1177/20416695221080184</p> <p>Each excel file contains the data relative to (1) the label task and (2) the two ranking tasks, respectively:</p> <p>(1) LabelData.xlsx contains the average frequencies of verbal labels per category across 3 raters, based on accumululation of participants' free naming responses (for experimental details see publication).</p> <p>(2) RankingData.xlsx contains the individual rankings of all experimental stimuli on the continuum from "animate origin" to "inanimate origin", as well as on the continuum from "Straight lines and right angles" to "No straight lines and no right angles" (for experimental details see publication). </p>
Figure 8. Artificial Intelligence Algorithm-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>This module receives information from Artificial Intelligent unit. Total functions about<br> Robot Behavior such as stability motors actions, robot path planning, turn camera, walking,<br> shooting, dribbling; motion and etc are controlled in this section.</p>
Figure 2. Some screenshots from the software system-Design and Development of a Software System for Swarm Intelligence Based Research Studies
<p>All of the mentioned operations can be performed easily by using the provided controls over<br> the related interfaces – windows of each algorithm. It is also important that each algorithm interface<br> is supported by visual controls to view obtained results with typical iteration-based graphics or<br> problem oriented visual elements. For instance, resulting graph structures are automatically shown<br> by the algorithm interfaces after solving some specific, popular problems like Travelling Salesman<br> Problem (TSP), Vehicle Routing Problem (VCP)…etc. Visually improved using features and<br> functions of the software system are critical aspects to provide more effective and useful platform to<br> perform SI based research studies better.<br> Related to the designed and developed software system, some screenshots from the software<br> system [interfaces of two algorithms (IWDs and ABC)] are represented in Fig. 2.</p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 5. Emotional flow after 3 hours of graphical editing
<p>To resume, we started with a product interface, found a way to determine two opposite states, than used that way to map user interaction with the product and determine the emotional answer to that interface. The results can then be used to improve product design, to elicit certain emotional responses, etc. For example, in this case, due to the rapid movement (anger) patterns in the aligning phase, a layout that minimizes this can be developed, using a shortcut menu or dynamically appearing guidelines. Furthermore, the user interface can be imagined to be able to learn working patterns and shift the shortcut menu from an aligning menu, if it detects anger patterns, into a color/shape picking menu, if it detects relaxation patterns, therefore assessing the emotional impact and improving the design of the interface in the same time.</p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 2. Detailed view of the individual response
<p>The application that measures the way the user interacts with the PC mouse simulates a simple strategy game. The goal is to choose between different objects in random pattern a particular one. The learning curve of the game is designed to be very fast in order for the subject to quickly understand all the principles and thus the results collected during the test to not be influenced by accommodation to the mechanics. Each 77 individual response were evaluated using the mouse acceleration (reaction) and the number of mouse clicks as indicators for one’s emotional state. Simply plotting acceleration over time provides little information; reaction seems to be very chaotic, and somewhat similar between the two states (Figure 2). </p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 4. Mouse trajectory – images taken from http://iographica.com 4 hours in Photoshop (left) vs. 4 hours in Eclipse (right)
<p>Studies from different fields (Arroyo & Wei, 2006), (Mäkiaho & Poranen, 2012), (Lockton, Harrison, Cain, Stanton, & Jennings, 2013), (Seelye, et al., 2015), (Hehman, Stolier, & Freeman, 2014) have shown that mouse trajectory and clicks are environment (application, device) and user specific, especially if mapped over a long working session (Figure 4). </p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 1. Science branches involved in translating product features to human needs
<p>Approach notwithstanding, designing product features involves basically a two-step process: (a) detecting and recognizing emotional information and (b) exhibiting a suitable reaction to the previously considered input. Since needs are reflected in the emotional impact, a method to measure the emotions is necessary (Abraham & Michie, 2008) to correctly assess the impact. Referring strictly to a software product, in order to implement the capability to sense the users’ emotional state, the first step should be developing an affective database (Tao & Tan, 2005), to allow correct identification of the affective status. This results in a translation between the affective status of the user and the computer, therefore allowing the program to process the user emotion just like any other input, successfully “digitizing” emotion. </p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 3. Average values in acceleration and clicks
<p>However, when looking at the median values of acceleration and clicks, a clear difference appears between the states: more clicks for relaxation, higher acceleration for stress (Figure 3). </p>
Designing Data Governance Policies for the Inclusive use of Artificial Intelligence in Africa
<p>Discussion: Designing Data Governance Policies for the Inclusive use of Artificial Intelligence in Africa.<br> Panel: Towards an Inclusive Data Governance Policy for the Use of AI in Africa<br> <br> </p>
Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence
<p>Dataset for paper: Z. Qin, L. Wu, H. Sun, S. Huo, T. Ma, E. Lim, P.-Y. Chen, B. Marelli, M.J. Buehler, Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence, Extreme Mechanics Letters, Vol. 36, 100652, 2020. <a href="https://doi.org/10.1016/j.eml.2020.100652">https://doi.org/10.1016/j.eml.2020.100652</a>.</p> <p>Code: https://github.com/lamm-mit/MNNN/ </p>
Level of play and coach-rated game intelligence are related to performance on design fluency in elite soccer players
<p>Executive brain functions are innate mechanisms for regulating behavior. While the impact of suboptimal executive functions has been characterized in patients, their contribution to individual success has not been elucidated. We set out to understand how executive functions relate to successful human behavior by examining their relation to game intelligence in sport - the ability to read a game and quickly adapt the behavior. In elite soccer players (n = 51), those playing in national teams (national team players) significantly outperformed those only playing at premier league level (premier league players) in Design Fluency (DF), a complex visuo-spatial executive function test that includes measures of creativity and cognitive flexibility. Their result showed a moderate correlation with coach rated game intelligence, also when correcting for low level cognitive capacity and was most evident when considering cognitive flexibility. DF capacity also correlated with number of assists made during the season but not with number of made goals during the same period, linking the fast planning of several steps in DF to fast planning of several steps in the soccer game. Altogether, our data suggests that DF capacity relates to success in soccer both on a subjective and on an objective level.</p>
Design of Personalised SupplemenTs Based on the Gut MicRobiota Through Artificial Intelligence for Alzheimer's Patients
ClinicalTrials.gov study NCT06199193. IPD Sharing: NO. Countries: 1. Publications: 1.
Artificial Intelligence Based Autonomous Socket Proposal Program: Socket Design Experiences
ClinicalTrials.gov study NCT05341674. IPD Sharing: NO. Countries: 1. Publications: 4.
Artificial Intelligence Designed Single Tooth Dental Prostheses
ClinicalTrials.gov study NCT05056948. IPD Sharing: NO. Countries: 1. Publications: 4.
Data from: The design of an intelligent fault-tolerant control for floating offshore wind turbine with blade faults
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Level of play and coach-rated game intelligence are related to performance on design fluency in elite soccer players
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DigFrag as a digital fragmentation method used for artificial intelligence-based drug design
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A Free Verbalization Method of Evaluating Sound Design: The Effectiveness of Artificially Intelligent Natural Language Processing Methods and Tools
<p>"Robot" voice sound files. Seventeen sound files were recorded in four formats; raw human voiceover (VO), and three types of robot voice: vocoded voice 1 (“robo”), vocoded voice 2 with music (“kbd”), and a “beep” voice. Each was recorded as 44.1kHz, 24-bit wav files in a professional recording studio. VO was recorded by professional voice actor DB Cooper, who has been the robot voice for several video games, as well as the voice of the DEE BMW internal car AI voice system. Cooper recorded three versions of the emotes on a Sennheiser MKH-416. Professional sound designer pdx Drescher, an expert in robot<br> and interface sound design, created three sets of robot voices from<br> the original voice files. With guidance from one of the authors,<br> pdx was tasked with trying different approaches to turning the VO<br> samples into three different types of robot voice while attempting<br> to maintain the meaning of the original sounds as described in the<br> list above through preserving the prosody/melodic contour of the<br> original. The first set, robo, used some clips from one of pdx’s prior<br> robot voice projects and integrated them to approximate the emo-<br> tional intention of the VO. Clips were re-pitched, manipulated, and<br> modulated using ProTools plugins. For the kbd takes, VO sounds<br> were played into a Shure SM58 microphone. Vocoder patches mod-<br> ified the signal by voice formants, and the pitch was determined<br> by MIDI notes and pitch-bend controllers. Output of the synthe-<br> sizer was then edited with additional synth patches and effects (EQ,<br> modulation, etc.). We made particular use of a plugin called Envy<br> by Cargo Cult, which takes the volume, pitch, and EQ envelopes of<br> one sound (the original VO) and apply them to another sound. This<br> helped make the synth resemble the prosody of the original sound<br> to some degree. The beep sounds underwent a similar development<br> process as the robo takes, but with interface “bleeps and bloops”<br> derived from various sound effects libraries, including the Star Trek<br> LCARS soundset.</p> <p>The following sounds were recorded: 1. Warning calm (“Uh-oh”) 2. Warning alarm (“ah!”) 3. Wrong/<br> error (“rrrrr”) 4. Correct/good (“yay”) 5. Surprise (neutral) (“Oh!”) 6.<br> Surprise (good) (“Oh!”) 7. Surprise (bad) “(ohhh”) 8. Love/adoration<br> (“awww”) 9. Disgust (“ew”) 10. Contempt (“ech”) 11. Guilt (“hmmm”)<br> 12. Confused (“huh?”) 13. Laugh (“ha ha”) 14. Calculating (“hmmm”)<br> 15. Sigh 16. Giggle 17. Pain (“ow ")</p>
The Role of Artificial Intelligence in The Architectural Design Process
<p>This research explores the integration of artificial intelligence (AI) in architectural design education, focusing on its impact on student creativity and design processes. Architects have increasingly adopted AI, with many acknowledging its potential to enhance efficiency. However, challenges remain, particularly in balancing AI with human creativity and intuition. The study, conducted in a third-year architecture studio course, employs Quantitative Content Analysis (QCA) to examine how AI influences design outcomes. By analyzing student reports, the research investigates patterns in AI usage and its role in achieving educational goals, aiming to inform AI's integration into architecture pedagogy.</p>
Artificial Intelligence in Smile Designing
ClinicalTrials.gov study NCT06666907. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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.