A taxonomy of cognitive artifacts: Function, information, and categories

The goal of this paper is to develop a systematic taxonomy of cognitive artifacts, i.e., human-made, physical objects that functionally contribute to performing a cognitive task. First, I identify the target domain by conceptualizing the category of cognitive artifacts as a functional kind: a kind of artifact that is defined purely by its function. Next, on the basis of their informational properties, I develop a set of related subcategories in which cognitive artifacts with similar properties can be grouped. In this taxonomy, I distinguish between three taxa, those of family, genus, and species. The family includes all cognitive artifacts without further specifying their informational properties. Two genera are then distinguished: representational and non-representational (or ecological) cognitive artifacts. These genera are further divided into species. In case of representational artifacts, these species are iconic, indexical, or symbolic. In case of ecological artifacts, these species are spatial or structural. Within species, token artifacts are identified. The proposed taxonomy is an important first step towards a better understanding of the range and variety of cognitive artifacts and is a helpful point of departure, both for conceptualizing how different artifacts augment or impair cognitive performance and how they transform and are integrated into our cognitive system and practices.17 page(s

A taxonomy of asymmetric requirements aspects

The early aspects community has received increasing attention among researchers and practitioners, and has grown a set of meaningful terminology and concepts in recent years, including the notion of requirements aspects. Aspects at the requirements level present stakeholder concerns that crosscut the problem domain, with the potential for a broad impact on questions of scoping, prioritization, and architectural design. Although many existing requirements engineering approaches advocate and advertise an integral support of early aspects analysis, one challenge is that the notion of a requirements aspect is not yet well established to efficaciously serve the community. Instead of defining the term once and for all in a normally arduous and unproductive conceptual unification stage, we present a preliminary taxonomy based on the literature survey to show the different features of an asymmetric requirements aspect. Existing approaches that handle requirements aspects are compared and classified according to the proposed taxonomy. In addition,we study crosscutting security requirements to exemplify the taxonomy's use, substantiate its value, and explore its future directions

Business Grid Services

Grid services have come to represent the synthesis of web services and grid computing paradigms. Web services provide the means to modularize software, enabling loosely coupled and novel synthesis. Grid computing removes the binding between functional software components and specific hosting hardware, enabling software to be deployed dynamically over a network (e.g. intra-, extra- or inter-net). Applying the constructs of grid computing to the service orientation of enterprise software will allow business service networks to utilize more specialized services. An upper service ontology that enables business grid services to be described and then related to the grid hosting platform is presented. Explicit knowledge is required for enterprise software, hosting servers and the domain that can then be utilized by both SLA and reservation systems. The ontology presented is derived from and validated using a collection of web services taken from leading investment banks

Transparency in Complex Computational Systems

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s..

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

The Space Object Ontology

Achieving space domain awareness requires the identification, characterization, and tracking of space objects. Storing and leveraging associated space object data for purposes such as hostile threat assessment, object identification, and collision prediction and avoidance present further challenges. Space objects are characterized according to a variety of parameters including their identifiers, design specifications, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, processes, operational statuses, and associated persons, organizations, or nations. The Space Object Ontology provides a consensus-based realist framework for formulating such characterizations in a computable fashion. Space object data are aligned with classes and relations in the Space Object Ontology and stored in a dynamically updated Resource Description Framework triple store, which can be queried to support space domain awareness and the needs of spacecraft operators. This paper presents the core of the Space Object Ontology, discusses its advantages over other approaches to space object classification, and demonstrates its ability to combine diverse sets of data from multiple sources within an expandable framework. Finally, we show how the ontology provides benefits for enhancing and maintaining longterm space domain awareness

Philosophical Signposts for Artificial Moral Agent Frameworks

This article focuses on a particular issue under machine ethics—that is, the nature of Artificial Moral Agents. Machine ethics is a branch of artificial intelligence that looks into the moral status of artificial agents. Artificial moral agents, on the other hand, are artificial autonomous agents that possess moral value, as well as certain rights and responsibilities. This paper demonstrates that attempts to fully develop a theory that could possibly account for the nature of Artificial Moral Agents may consider certain philosophical ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency. At the very least, the said philosophical concepts may be treated as signposts for further research on how to truly account for the nature of Artificial Moral Agents

The Foundational Model of Anatomy Ontology

Anatomy is the structure of biological organisms. The term also denotes the scientific discipline devoted to the study of anatomical entities and the structural and developmental relations that obtain among these entities during the lifespan of an organism. Anatomical entities are the independent continuants of biomedical reality on which physiological and disease processes depend, and which, in response to etiological agents, can transform themselves into pathological entities. For these reasons, hard copy and in silico information resources in virtually all fields of biology and medicine, as a rule, make extensive reference to anatomical entities. Because of the lack of a generalizable, computable representation of anatomy, developers of computable terminologies and ontologies in clinical medicine and biomedical research represented anatomy from their own more or less divergent viewpoints. The resulting heterogeneity presents a formidable impediment to correlating human anatomy not only across computational resources but also with the anatomy of model organisms used in biomedical experimentation. The Foundational Model of Anatomy (FMA) is being developed to fill the need for a generalizable anatomy ontology, which can be used and adapted by any computer-based application that requires anatomical information. Moreover it is evolving into a standard reference for divergent views of anatomy and a template for representing the anatomy of animals. A distinction is made between the FMA ontology as a theory of anatomy and the implementation of this theory as the FMA artifact. In either sense of the term, the FMA is a spatial-structural ontology of the entities and relations which together form the phenotypic structure of the human organism at all biologically salient levels of granularity. Making use of explicit ontological principles and sound methods, it is designed to be understandable by human beings and navigable by computers. The FMA’s ontological structure provides for machine-based inference, enabling powerful computational tools of the future to reason with biomedical data