Mapas de competencias genéricas del bachillerato

En el marco general del diseño e implementación de un entorno virtual inteligente para dar seguimiento al desarrollo de competencias por los estudiantes, se presentan los resultados del análisis de las competencias genéricas establecidas para el Sistema Nacional de Bachillerato de México y la codificación de los resultados en la forma de mapas de competencias usando un lenguaje susceptible de ser procesado automáticamente por computadoras. Específicamente, se presentan una revisión de literatura sobre la representación computacional de competencias, un mapa de competencias genéricas con relaciones de subsume/es-un e incluye/parte-de y la aplicación del mapa para identificar la presencia de competencias genéricas en el diseño de cursos de bachillerato en el Sistema de Universidad Virtual de la Universidad de Guadalajara. In the general framework of designing and implementing an intelligent virtual environment to monitor the development of competences by students, we present the results of an analysis of the generic competences established for the National High School System of Mexico, and the coding of the results in the form of competence maps using a language that can be automatically processed by computers. Specifically, we present a literature review on the computational representation of competencies, a map of generic competencies with subsume/is-a and includes/part-of relationships, and the application of the competences map to identify the presence of generic competences in the design of high school courses in the Virtual University System of the University of Guadalajara.

COMPUTATIONAL MODEL OF INTERSECTIONS REPRESENTATION AS APPARATUS OF CONTENTS MULTI-SCALAR, RETROSPECTIVE AND RESILIENT FOR THE RESEARCH OF CONNOTATIONS, SIMULATIONS AND SUSTAINABILITY IN THE LANDSCAPE ASSETS

The research project’s objective, based on defining a “Computational Model of Intersections Simulation/Representation” as complex digital cultural infrastructure and computational knowledge, acquires the identity characteristics of the Computational Representation (phenomenological, geometry, visual, resilience, scalability analytical-interpretative knowledge) to promote for landscape and Cultural Heritage (CH): a) of systematize environmental information and protective by intersections of resilience’s categories in the “thematic systems of families and types” of the landscape; b) a model of Smart CH for Industry 4.0 – 2021. The same time favors: a) thematic and retrospective intersections between formal and interpretative layers for the return of information from a spatial and functional; b) innovation by introduction of professional figures of the contemporary digital. The methodology is articulated on following intersection models: Connotations, Composition, Development (combination of complex formal, structural and geometric factors); Morphogenesis (innovative concepts of transformations form), Sustainability. From the point of view the results and conclusion the study allowed to highlight: a) “network of multi-scalar discrete relationships” with sequences of digital models for the relative tessellation of the surfaces; b) “resilience measure” by “building a digital model of morphogenesis”; c) systematize environmental information and protective by intersections of resilience’s categories in the “thematic systems of families and types” of the landscape; d) “operative and phenomenological reading” of the changing complexity of environmental reality with connections between the behavioural and physical structures of the place.

When Computational Representation Meets Neuroscience: A Survey on Brain Encoding and Decoding

Real human language mechanisms and the artificial intelligent language processing methods are two independent systems. Exploring the relationship between the two can help develop human-like language models and is also beneficial to reveal the neuroscience of the reading brain. The flourishing research in this interdisciplinal research field calls for surveys to systemically study and analyze the recent successes. However, such a comprehensive review still cannot be found, which motivates our work. This article first briefly introduces the interdisciplinal research progress, then systematically discusses the task of brain decoding from the perspective of simple concepts and complete sentences, and also describes main limitations in this field and put forward with possible solutions. Finally, we conclude this survey with certain open research questions that will stimulate further studies.

Individual variability of neural computations in the primate retina

Variation in the neural code between individuals contributes to making each person unique. Using ∼100 neural population recordings from major ganglion cell types in the macaque retina, we develop an interpretable computational representation of individual variability using machine learning. This representation preserves invariances, such as asymmetries between ON and OFF cells, while capturing individual variation and covariation in properties such as nonlinearity, temporal dynamics, and spatial receptive field size. The similarity of these properties across cell types was dependent on the similarity of their synaptic connections. Surprisingly, male retinas exhibited higher firing rates and faster temporal integration than female retinas. By exploiting data from previously recorded macaque retinas, a new macaque retina (and crucially, a human retina) could be efficiently characterized. Simulations indicated that combining a vast dataset of healthy macaque recordings with behavioral feedback could be used to identify the neural code and improve retinal implants for treating blindness.

Discovering the Value Creation System in IoT Ecosystems

Internet of Things (IoT) should not be seen only as a cost reduction mechanism for manufacturing companies; instead, it should be seen as the basis for transition to a new business model that monetizes the data from an intelligent ecosystem. In this regard, deciphering the operation of the value creation system and finding the balance between the digital strategy and the deployment of technological platforms, are the main motivations behind this research. To achieve the proposed objectives, systems theory has been adopted in the conceptualization stage, later, fuzzy logic has been used to structure a subsystem for the evaluation of input parameters. Subsequently, system dynamics have been used to build a computational representation and later, through dynamic simulation, the model has been adjusted according to iterations and the identified limits of the system. Finally, with the obtained set of results, different value creation and capture behaviors have been identified. The simulation model, based on the conceptualization of the system and the mathematical representation of the value function, allows to establish a frame of reference for the evaluation of the behaviour of IoT ecosystems in the context of the connected home.

Defining A Design Space of The Auto-Mobile Office: A Computational Abstraction Hierarchy Analysis

One advantage of highly automated vehicles is drivers can use commute time for non-driving tasks, such as work-related tasks. The potential for an auto-mobile office—a space where drivers work in automated vehicles—is a complex yet underexplored idea. This paper begins to define a design space of the auto- mobile office in SAE Level 3 automated vehicles by integrating the affinity diagram (AD) with a computational representation of the abstraction hierarchy (AH). The AD uses a bottom-up approach where researchers starting with individual findings aggregate and abstract those into higher-level concepts. The AH uses a top-down approach where researchers start with first principles to identify means-ends links between system goals and concrete forms of the system. Using the programming language R, the means-ends links of AH can be explored statistically. This computational approach to the AH provides a systematic means to define the design space of the auto-mobile office.

Lying, computers and self-awareness

From the initial analysis of John Morris in 1976 about if computers can lie, I have presented my own treatment of the problem using what can be called a computational lying procedure. One that uses two Turing Machines. From there, I have argued that such a procedure cannot be implemented in a Turing Machine alone. A fundamental difficulty arises, concerning the computational representation of the self-knowledge a machine should have about the fact that it is lying. Contrary to Morris’ claim, I have thus suggested that computers – as far as they are Turing Machines – cannot lie. Consequently, I have claimed that moral agency attribution to a robot or any other automated AI system, cannot be made, strictly grounded on imitating behaviors. Self-awareness as an ontological grounding for moral attribution must be evoked. This can pose a recognition problem from our part, should the sentient system be the only agent capable of acknowledging its own sentience.

Combining computational controls with natural text reveals new aspects of meaning composition

To study a core component of human intelligence—our ability to combine the meaning of words—neuroscientists look for neural correlates of meaning composition, such as brain activity proportional to the difficulty of understanding a sentence. However, little is known about the product of meaning composition—the combined meaning of words beyond their individual meaning. We term this product “supra-word meaning” and devise a computational representation for it by using recent neural network algorithms and a new technique to disentangle composed-from individual-word meaning. Using functional magnetic resonance imaging, we reveal that hubs that are thought to process lexical-level meaning also maintain supra-word meaning, suggesting a common substrate for lexical and combinatorial semantics. Surprisingly, we cannot detect supra-word meaning in magnetoencephalography, which suggests that composed meaning is maintained through a different neural mechanism than synchronized firing. This sensitivity difference has implications for past neuroimaging results and future wearable neurotechnology.