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23 results for “Intention Modeling”
Forced Continuance Intention Model of Distance Online Teaching during CoVID-19 outbreak at University of Maribor, Slovenia [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the first study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university teachers to the new situation. The project documentation provided for the Forced Online Distance Teaching (FODT) consist of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics, and</li> <li>SPSS dataset.</li> </ul>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
Brain Functors: A mathematical model of intentional perception and action-Figure 15: Mathematical butterfly diagram for a brain functor
<p>HomA(F(X),A) ≅ Het(X,A) ≅ HomX(X,G(A)). If the functor F also has a left adjoint H : A→X, then: HomX(H(A),X) ≅ Het(A,X) ≅ HomA(A,F(X)). Then taking the isomorphisms that do not involve G or H gives:<br> and Het(A,X) ≅ HomA(A,F(X)), i.e., F is a brain functor. Hence all functors that have both right and left adjoints are brain functors.<br> 16<br> HomA(F(X),A) ≅ Het(X,A)</p>
Figure 14: Adjunctive square diagram-Brain Functors: A mathematical model of intentional perception and action
<p>Finally, a brain functor is a functor F: X→A that is a left semiadjunction for Het(X, A) and a right semiadjunction for Het(A, X), i.e., HomA(F(X),A) ≅ Het(X,A)<br> and Het(A,X) ≅ HomA(A,F(X)).<br> For each d in Het(X, A), there is a unique hom f(d) in HomA(F(X), A) so that the upper triangular ‘wing’ in the butterfly diagram commutes. For each d' in Het(A, X), there is a unique hom g(d') in HomA(A, F(X)) so that the lower triangular ‘wing’ commutes.</p>
Figure 13: Composition of hets and homs-Brain Functors: A mathematical model of intentional perception and action
<p>The cross-category object-to-object hets d : X→A will be indicated by thin arrows (→) rather than thick arrows (⇒). The first question is how do heteromorphisms compose with one another? But that is not necessary. Chimera do not need to ‘mate’ with other chimera to form a ‘species’ or category; they only need to mate with the intra-category morphisms on each side to form other chimera.8 Given a het d : X→A from an object in a category X to an object in a category A, and homs h : X'⇒X in X and k : A⇒A' in A, the composition dh : X'⇒X→A is another het X'→A and the composition kd : X→A⇒A' is another het X→A'.</p>
Figure 12: Language faculty as two-way determination through a universal-Brain Functors: A mathematical model of intentional perception and action
<p>A brain functor, broadly put, is any universal mechanism of determination that can factor determination either way through a universal–rather than an adjunction that factors one way determination through two (receiving and sending) universals. In some contexts in the life sciences, determination is strictly one way so one might expect to find a semiadjunction but not a two-way system like a brain functor. An application of the scheme for a brain functor in the cognitive sciences is to model the language faculty where there is two way determination between vocal stimuli and internal representations. The previous semiadjunctions for language understanding and language action can be merged to arrive at the brain-like function of the language faculty.</p>
Figure 11: Coding and decoding Cartesian coordinates of geometrical points-Brain Functors: A mathematical model of intentional perception and action
<p>The simplest form of a brain "functor" is just a two-way representation or coding system that constructs and implements a set of codes. Given some set of objects, it is encoded using some isomorphic set of representations or codes for the objects, and then given an instance of the code, it is decoded to determine the object. Coordinatizing is a form of coding. The geometrical plane is a collection of points, and the Cartesian coordinate system represents each point P by a pair (xP, yP) of coordinates. Given a point P , the "coordinate" function selects the coordinates (xP, yP) of the point which is the recognized or coded output, and given the coordinates or code for a point (xP, yP) as an input, the "plot" function designates the point.</p>
Figure 8: Language production through a sending universal-Brain Functors: A mathematical model of intentional perception and action
<p>The dual to "language understanding" is language production or linguistic action (e.g., "speech acts"). The role of the specific het is played by some auditory output such as utterances (Humboldt’s "vocal stimulus"). But the corresponding internal specific hom is the speech act (i.e., internal speech with intentionality) that through the language faculty produces the same outputs but as intentional speech.</p>
Figure 1: Het d: X→ A-Brain Functors: A mathematical model of intentional perception and action
<p>In the body of this paper, I will try to keep the mathematics at a minimal conceptual level– which the mathematical formulations restricted to the Appendix. Category theory lends itself to visualization in diagrams so that non-mathematical style of presentation is emphasized by an abundant use of diagrams. A category is intuitively a set of objects of the same type. Morphisms between objects should be thought of as a type of determining relation or cause-effect relation between the objects. When a morphism is between objects of the same category, it is called a homomorphism or hom, and when between objects of different categories it is a heteromorphism or het.4 One of the problems in the conventional treatment of category theory5 is that it tries to ignore heteromorphisms even though hets are a natural part of working mathematics. This leads to certain definitions being rather contrived (to avoid mentioning hets), the usual treatment of the universal mapping properties in adjunctions being the case in point. Adjunctions will be introduced informally and in the natural manner using hets. The general setting is how the objects in one category (e.g., the "environment" in a life sciences context), the "sending" category, will "affect" or "determine" objects in another category (e.g., "organisms"), the "receiving" category. We start with an object X in the sending category, an object A in the receiving category, and a specific het determination d : X → A from X to A.6</p>
Figure 4: The Adjunctive Square Diagram-Brain Functors: A mathematical model of intentional perception and action
<p>Dually, we can define the above situation, given by the association of the sending universal G(A) with each object A in the receiving category along with the canonical isomorphism Het(X,A) ≅ Homsending(X,G(A)), as a right semiadjunction. Now we are prepared to define an adjunction essentially as:<br> adjunction = left semiadjunction + right semiadjunction Homreceiving(F(X),A) ≅ Het(X,A) ≅ Homsending(X,G(A)).</p>
Figure 7: -"Action" as determination through a sending universal-Brain Functors: A mathematical model of intentional perception and action
<p>Dual to the generic model of "perception" is the generic model of "action"–which is the determinative scheme given by a right semiadjunction. In the model of perception, there is the uninterpreted message as just a sensory input (the external het), and then there is the second level where the factorization (the internal hom) through the receiving universal recognizes the interpretation, meaning, or intentionality of the message. In the dual model of "action," the external het specifies the external behavior (which could be even a reflex behavior) while internal hom factoring through a sending universal that supplies the "intentionality" of the "action" (where an "action" is a "behavior" plus the second level of "intentionality"). In each case, we end up with a certain behavior but determined by two different means.</p>
Figure 6: "Perception" as determination through a receiving universal-Brain Functors: A mathematical model of intentional perception and action
<p>Before turning to right semiadjunctions, it might be useful to present a rather generic version of determination through a receiving universal as model of "recognition" or "perception" that captures many of the common features of the various examples. The determination through the receiving universal is the active internal process that supplies the "interpretation" or "intentionality" to the raw sense data. The red blotch is seen as a tomato; the sound "ya" is understood as indicating agreement, and so forth. In the passive/direct alternative, the raw sensory input supplies Lockean "perception" like writing on a blank slate or a stamp making an impression on wax.</p>
Figure 3: Scheme for determination by a sending universal G(A)-Brain Functors: A mathematical model of intentional perception and action
<p>The universal mapping property is: for every het d : X→A, there is a unique hom g(d) : X⇒G(A) in the sending category such that: eAg(d) = X⇒G(A)→A = X→A = d i.e., such that the determination through the universal sending het eA : G(A)→A preceded by the hom g(d) : X⇒G(A) is the same as the original het d : X→A.</p>
A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy (dataset)
<p>Data cleaned and used for the path analysis model estimated in the article</p> <h1>A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy</h1> <p>https://doi.org/10.3390/su13041879</p>
Limestone spheroid 3D models for: The limestone spheroids of 'Ubeidiya: Intentional imposition of symmetric geometry by early hominins?
<p><span>Spheroids are one of the least understood lithic items yet are one of the most enduring, spanning from the Oldowan to the Middle Palaeolithic. Why and how they were made remains highly debated. We seek to address whether spheroids represent unintentional by-products of percussive tasks or if they were intentionally knapped tools with specific manufacturing goals. We apply novel 3D analysis methods, including spherical harmonics and surface curvature, to 150 limestone spheroids from 'Ubeidiya (c.1.4Ma), presently the earliest Acheulean occurrence outside of Africa, to bring a new perspective to these enigmatic artefacts. We reconstruct the spheroid reduction sequence based on trends in their scar facets and geometry, finding that the spheroid makers at 'Ubeidiya followed a premeditated reduction strategy. During their manufacture, the spheroids do not become smoother, but they become markedly more spherical. They approach an ideal sphere, a feat that likely required a mental template and skilful knapping. Acheulean bifaces are currently thought to represent the earliest evidence of hominins imposing a premeditated, symmetrical shape on stone. With evidence of spheroids occurring before the Acheulean, the intentional production of a sphere-like object now represents the oldest evidence of hominins desiring and achieving intentional geometry and symmetry in stone. </span></p>
Limestone spheroid 3D models for: The limestone spheroids of ‘Ubeidiya: Intentional imposition of symmetric geometry by early hominins?
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Artifacts for paper "PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing" submitted to TOSEM
<p>The project includes the data and code used in the submitted TOSEM paper titled "PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing"</p>
Intentional Relationship Model in OT Students
ClinicalTrials.gov study NCT07133009. IPD Sharing: NO. Countries: 1. Publications: 2.
The Effect of a Transtheoretical Model-Based Educational Intervention on First-Year Nursing Students' HPV Knowledge, Health Beliefs, and Vaccination Behavior Intentions
ClinicalTrials.gov study NCT07248904. IPD Sharing: NO. Countries: 1. Publications: 0.
Creighton Model Effectiveness, Intentions and Behaviors Assessment (CEIBA)
ClinicalTrials.gov study NCT01012596. IPD Sharing: Not stated. Countries: 2. Publications: 0.
ScienceDex guides
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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.