Contextual Categorization Research Articles

Developing a semantic framework for categorizing IoT agriculture sensor data: A machine learning and web semantics approach

This study introduces a Semantic Framework for Categorizing IoT Agriculture Sensor Data, leveraging Machine Learning and Web Semantics. IoT sensors in agriculture generate vast real-time data on crucial factors like soil conditions and weather, promising optimization in resource use and crop yields. While machine learning aids data categorization, semantic aspects often remain unexplored. By combining machine learning with web semantics (RDF and OWL), this research establishes a structured framework that not only categorizes data but also links it to actionable farming recommendations. Methodologically, it involves data collection, preprocessing, machine learning, and semantic integration. Performance evaluation through metrics and visualizations reveals the framework's effectiveness, aiding decision-making in precision agriculture. This study contributes to IoT-based precision agriculture by bridging the gap between raw sensor data and actionable insights, empowering a semantic framework for contextual categorization and recommendation generation. The fusion of machine learning and web semantics holds transformative potential for agriculture, enhancing data management and decision-making processes.

Algorithms for Social Curation: Designing and evaluating an embodied and subjectively situated visual art Interpretation and navigation system (VAINS)

VAINS, the <em>visual art Interpretation and navigation system</em>, is an interdisciplinary work in progress, an interactive website which offers repository, search and content recommendation tools for art content in an online environment. This paper will put the VAINS system into a historical curatorial context as well as explaining and analysing the VAINS project and its methodology, outlining our reasons for placing the body at the forefront of its navigation systems. The paper will also present a narrative of interaction with the tools we have created, showing their impact on a range of users and illustrating the value of embodiment in the context of online curation. VAINS, <em>visual art Interpretation</em> <em>and navigation system</em>, is an interdisciplinary work in progress, an interactive website which offers repository, search and content recommendation tools adjusted to art content in an online environment, drawing upon the Computer Fine Arts database of digital artworks. The site is a response to the expected changes in content consumption as part of the movement towards a more complex web 3.0 generation, offering a customizable and personalized art viewing experience. The VAINS system aims to be in part a text free environment, where visual experiences are interpreted through their contextual categorization and through the use of other sensual means, such as icons, sounds and textures. VAINS also deploys the embodied and situated nature of human users as core resources in its underlying computational structures, drawing upon enactivism and embodiment as core computational principals. The paper will put the VAINS system into a historical curatorial context as well as explaining and analysing the VAINS project and its methodology, outlining the reasons for placing the body at the forefront of its navigation systems. The paper will also present a narrative of interaction with the tools we have created, showing their impact on a range of users and illustrating the value of embodiment in the context of online art curation.

Processing scene context: Fast categorization and object interference

The extent to which object identification is influenced by the background of the scene is still controversial. On the one hand, the global context of a scene might be considered as an ultimate representation, suggesting that object processing is performed almost systematically before scene context analysis. Alternatively, the gist of a scene could be extracted sufficiently early to be able to influence object categorization. It is thus essential to assess the processing time of scene context. In the present study, we used a go/no-go rapid visual categorization task in which subjects had to respond as fast as possible when they saw a “man-made environment”, or a “natural environment”, that was flashed for only 26ms. “Man-made” and “natural” scenes were categorized with very high accuracy (both around 96%) and very short reaction times (median RT both around 390ms). Compared with previous results from our group, these data demonstrate that global context categorization is remarkably fast: (1) it is as fast as object categorization [Fabre-Thorpe, M., Delorme, A., Marlot, C., & Thorpe, S. (2001). A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes. Journal of Cognitive Neuroscience, 13(2), 171–180]; (2) it is faster than contextual categorization at more detailed levels such as sea, mountain, indoor or urban contexts [Rousselet, G. A., Joubert, O. R., & Fabre-Thorpe, M. (2005). How long to get to the “gist” of real-world natural scenes? Visual Cognition, 12(6), 852–877]. Further analysis showed that the efficiency of contextual categorization was impaired by the presence of a salient object in the scene especially when the object was incongruent with the context. Processing of natural scenes might thus involve in parallel the extraction of the global gist of the scene and the concurrent object processing leading to categorization. These data also suggest early interactions between scene and object representations compatible with contextual influences on object categorization in a parallel network.

Neuf motifs de révision des textes procéduraux : l'apport de la catégorisation et des graphes contextuels à l'explication du savoir-faire

Know-how is made of knowledge on the actions intervening in the accomplishment of a task. Expressed verbally, this knowledge is itself a task. Studies on problem solving let us to identify, on the one hand, the prerequisite knowledge needed to understand and learn how to accomplish a task, and, on the other hand, the task structure in which the knowledge is embedded. Thus, this approach allows to associate know-how with understanding, learning and with verbal production revision. We point out how two context-based formalism for representing knowledge and reasoning, namely contextual categorization and contextual graphs, can be used for simulating the revision of verbal production of the know-how. The procedural explanation, which can be analyzed in terms of exposition, categorization and implication on the object side to justify the organization of actions in a structure of context-aware goals, is a didactical competence that is not really studied although this competence would help to improve the writing of instructions, and more widely, the teaching of practical knowledge. Thus, we list nine components of the process of revising the verbal production of know-how.

Neuf motifs de révision des textes procuraux : l'apport de la catégorisation contextuelle et des graphes contextuels à l'explication du savoir-faire

Charles TIJUS, Leslie GANET et Patrick BRÉZILLON: Neuf motifs de révision des textes procéduraux: l’apport de la catégorisation contextuelle et des graphes contextuels à l’explication du savoir-faire Know-how is made of knowledge on the actions intervening in the accomplishment of a task. Expressed verbally, this knowledge is itself a task. Studies on problem solving let us to identify, on the one hand, the prerequisite knowledge needed to understand and learn how to accomplish a task, and, on the other hand, the task structure in which the knowledge is embedded. Thus, this approach allows to associate know-how with understanding, learning and with verbal production revision. We point out how two context-based formalism for representing knowledge and reasoning, namely contextual categorization and contextual graphs, can be used for simulating the revision of verbal production of the know-how. The procedural explanation, which can be analyzed in terms of exposition, categorization and implication on the object side to justify the organization of actions in a structure of contextaware goals, is a didactical competence that is not really studied although this competence would help to improve the writing of instructions, and more widely, the teaching of practical knowledge. Thus, we list nine components of the process of revising the verbal production of know-how.

Forming contextual categories in infancy

The role of context in categorization was examined in four experiments with 3-month-olds. In all experiments infants learned to kick their feet to activate a mobile on 3 successive days. Infants were trained with a different mobile exemplar during each daily session. Categorization of a physically dissimilar novel object ( Butterfly) was assessed 1 to 14 days later. In Experiments 1A and 1B, infants were trained and tested in a highly distinctive context. They included Butterfly in the mobile category after 1 and 7, but not after 14, days (Experiment 1A). This latter result was due to a categorization failure because infants did respond to another novel exemplar of the original training series after 14 days (Experiment 1B). In Experiment 2A, infants who were passively exposed to Butterfly for 3 minutes in the distinctive context at the end of category training responded to Butterfly 1 day later when it was encountered “out of context.” This result was also obtained in Experiment 2B when the distinctive context was present only during the final training session. Taken together, these findings demonstrate that categorization of a novel object is influenced by the context present when the object is initially encountered and by previous encounters with that object in the category context. Clearly, infants are capable of contextual categorization very early in the first year of life.

Contextual Categorization by Lateral Inhibition

A mechanism is proposed whereby categorization of stimuli is performed on the basis of the context in which such stimuli occur. This mechanism depends on lateral inhibition, which has been experimentally shown to operate in a number of biological receptor systems. It is assumed that n areas in the brain correspond to n classes into which a stimulus might be classified, the category chosen corresponding to the area of maximal excitation. Establishment of mutually inhibitory connections between these areas, such that inhibition follows the equation for lateral inhibition and such that areas corresponding to "like" classes inhibit each other less than areas corresponding to "unlike" classes, leads to contextual categorization.