Evaluating and extending the Informed Consent Ontology for representing permissions from the clinical domain

The purpose of this study was to evaluate, revise, and extend the Informed Consent Ontology (ICO) for expressing clinical permissions, including reuse of residual clinical biospecimens and health data. This study followed a formative evaluation design and used a bottom-up modeling approach. Data were collected from the literature on US federal regulations and a study of clinical consent forms. Eleven federal regulations and fifteen permission-sentences from clinical consent forms were iteratively modeled to identify entities and their relationships, followed by community reflection and negotiation based on a series of predetermined evaluation questions. ICO included fifty-two classes and twelve object properties necessary when modeling, demonstrating appropriateness of extending ICO for the clinical domain. Twenty-six additional classes were imported into ICO from other ontologies, and twelve new classes were recommended for development. This work addresses a critical gap in formally representing permissions clinical permissions, including reuse of residual clinical biospecimens and health data. It makes missing content available to the OBO Foundry, enabling use alongside other widely-adopted biomedical ontologies. ICO serves as a machine-interpretable and interoperable tool for responsible reuse of residual clinical biospecimens and health data at scale.

Real-Time Automatic Investigation of Indian Roadway Animals by 3D Reconstruction Detection Using Deep Learning for R-3D-YOLOV3 Image Classification and Filtering

Statistical reports say that, from 2011 to 2021, more than 11,915 stray animals, such as cats, dogs, goats, cows, etc., and wild animals were wounded in road accidents. Most of the accidents occurred due to negligence and doziness of drivers. These issues can be handled brilliantly using stray and wild animals-vehicle interaction and the pedestrians’ awareness. This paper briefs a detailed forum on GPU-based embedded systems and ODT real-time applications. ML trains machines to recognize images more accurately than humans. This provides a unique and real-time solution using deep-learning real 3D motion-based YOLOv3 (DL-R-3D-YOLOv3) ODT of images on mobility. Besides, it discovers methods for multiple views of flexible objects using 3D reconstruction, especially for stray and wild animals. Computer vision-based IoT devices are also besieged by this DL-R-3D-YOLOv3 model. It seeks solutions by forecasting image filters to find object properties and semantics for object recognition methods leading to closed-loop ODT.

Using Immersive Virtual Reality to Examine How Visual and Tactile Cues Drive the Material-Weight Illusion

The material-weight illusion (MWI) demonstrates how our past experience with material and weight can create expectations that influence the perceived heaviness of an object. Here we used mixed-reality to place touch and vision in conflict, to investigate whether the modality through which materials are presented to a lifter could influence the top-down perceptual processes driving the MWI. University students lifted equally-weighted polystyrene, cork and granite cubes whilst viewing computer-generated images of the cubes in virtual reality (VR). This allowed the visual and tactile material cues to be altered, whilst all other object properties were kept constant. Representation of the objects’ material in VR was manipulated to create four sensory conditions: visual-tactile matched, visual-tactile mismatched, visual differences only and tactile differences only. A robust MWI was induced across all sensory conditions, whereby the polystyrene object felt heavier than the granite object. The strength of the MWI differed across conditions, with tactile material cues having a stronger influence on perceived heaviness than visual material cues. We discuss how these results suggest a mechanism whereby multisensory integration directly impacts how top-down processes shape perception.

Identification of Misconceptions in Science Learning During the Covid-19 Pandemic Using the CRI (Certainty of Response Index) Method for Primary school Students

This research aims to identify the science misconceptions of elementary school students using CRI (Certainty of Response Index), as well as to describe the factors of students' misconceptions at Primary school Muhammadiyah 8 Tulangan. This research uses the descriptive qualitative method. The subjects used in this study were class V, totaling 26 students. Data collection techniques using the test, interviews, and documentation. Based on the results obtained from the research as a whole, the highest misconception is about the concept of the effect of temperature on changes in the shape of objects by 44.70%, the concept of temperature and heat by 39.42%, and the lowest misconception is about the concept of object properties by 32.04%. From the results of research using the CRI (Certainty of Response Index) method, several factors cause misconceptions, namely from the students themselves who come from the initial concept and students wrong intuition, then misconceptions from the teacher, as well as incomplete book explanations. So it can be concluded that science learning during the covid-19 pandemic caused misconceptions for students of Primary school Muhammadiyah 8 Tulangan

Aesthetic Liking Is Not Only Driven by Object Properties, but Also by Your Expectations

Aesthetic liking has traditionally been thought to be caused by specific object properties: symmetry, curvature, etc. One of the great insights of neuroaesthetics is the realization that expectations play almost as great a role in shaping liking responses. For example, by prefacing exposure to an artwork with information about its provenance it is possible to enhance or decrease liking. The article under discussion summarizes the results from a functional magnetic resonance imaging study where people were scanned as they rated abstract art they either believed belonged to a prestigious art gallery or to have been created by the experimenters.

Semantic Reference Model for Individualization of Information Processes in IoT Heterogeneous Environment

The individualization of information processes based on artificial intelligence (AI), especially in the context of industrial tasks, requires new, hybrid approaches to process modeling that take into account the novel methods and technologies both in the field of semantic representation of knowledge and machine learning. The combination of both AI techniques imposes several requirements and restrictions on the types of data and object properties and the structure of ontologies for data and knowledge representation about processes. The conceptual reference model for effective individualization of information processes (IIP CRM) proposed in this work considers these requirements and restrictions. This model is based on such well-known standard upper ontologies as BFO, GFO and MASON. Evaluation of the proposed model is done on a practical use case in the field of precise agriculture where IoT-enabled processes are widely used. It is shown that IIP CRM allows the construction of a knowledge graph about processes that are surrounded by unstructured data in soft and heterogeneous domains. CRM also provides the ability to answer specific questions in the domain using queries written with the CRM vocabulary, which makes it easier to develop applications based on knowledge graphs.

Applying Machine Learning and Data Fusion to the "Missing Person" Problem

We present a system for integrating multiple sources of data for finding missing persons. This system can assist authorities in finding children during amber alerts, mentally challenged persons who have wandered off, or person-of-interests in an investigation. Authorities search for the person in question by reaching out to acquaintances, checking video feeds, or by looking into the previous histories relevant to the investigation. In the absence of any leads, authorities lean on public help from sources such as tweets or tip lines. A missing person investigation requires information from multiple modalities and heterogeneous data sources to be combined.<div>Existing cross-modal fusion models use separate information models for each data modality and lack the compatibility to utilize pre-existing object properties in an application domain. A framework for multimodal information retrieval, called Find-Them is developed. It includes extracting features from different modalities and mapping them into a standard schema for context-based data fusion. Find-Them can integrate application domains with previously derived object properties and can deliver data relevant for the mission objective based on the context and needs of the user. Measurements on a novel open-world cross-media dataset show the efficacy of our model. The objective of this work is to assist authorities in finding uses of Find-Them in missing person investigation.</div>

Applying Machine Learning and Data Fusion to the "Missing Person" Problem

We present a system for integrating multiple sources of data for finding missing persons. This system can assist authorities in finding children during amber alerts, mentally challenged persons who have wandered off, or person-of-interests in an investigation. Authorities search for the person in question by reaching out to acquaintances, checking video feeds, or by looking into the previous histories relevant to the investigation. In the absence of any leads, authorities lean on public help from sources such as tweets or tip lines. A missing person investigation requires information from multiple modalities and heterogeneous data sources to be combined.<div>Existing cross-modal fusion models use separate information models for each data modality and lack the compatibility to utilize pre-existing object properties in an application domain. A framework for multimodal information retrieval, called Find-Them is developed. It includes extracting features from different modalities and mapping them into a standard schema for context-based data fusion. Find-Them can integrate application domains with previously derived object properties and can deliver data relevant for the mission objective based on the context and needs of the user. Measurements on a novel open-world cross-media dataset show the efficacy of our model. The objective of this work is to assist authorities in finding uses of Find-Them in missing person investigation.</div>