12,293 research outputs found
The KB paradigm and its application to interactive configuration
The knowledge base paradigm aims to express domain knowledge in a rich formal
language, and to use this domain knowledge as a knowledge base to solve various
problems and tasks that arise in the domain by applying multiple forms of
inference. As such, the paradigm applies a strict separation of concerns
between information and problem solving. In this paper, we analyze the
principles and feasibility of the knowledge base paradigm in the context of an
important class of applications: interactive configuration problems. In
interactive configuration problems, a configuration of interrelated objects
under constraints is searched, where the system assists the user in reaching an
intended configuration. It is widely recognized in industry that good software
solutions for these problems are very difficult to develop. We investigate such
problems from the perspective of the KB paradigm. We show that multiple
functionalities in this domain can be achieved by applying different forms of
logical inferences on a formal specification of the configuration domain. We
report on a proof of concept of this approach in a real-life application with a
banking company. To appear in Theory and Practice of Logic Programming (TPLP).Comment: To appear in Theory and Practice of Logic Programming (TPLP
Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert
A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs' monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient's condition on the most suited device within the physician's reach
Recommendation and weaving of reusable mashup model patterns for assisted development
With this article, we give an answer to one of the open problems of mashup development that users may face when operating a model-driven mashup tool, namely the lack of modeling expertise. Although commonly considered simple applications, mashups can also be complex software artifacts depending on the number and types of Web resources (the components) they integrate. Mashup tools have undoubtedly simplified mashup development, yet the problem is still generally nontrivial and requires intimate knowledge of the components provided by the mashup tool, its underlying mashup paradigm, and of how to apply such to the integration of the components. This knowledge is generally neither intuitive nor standardized across different mashup tools and the consequent lack of modeling expertise affects both skilled programmers and end-user programmers alike. In this article, we show how to effectively assist the users of mashup tools with contextual, interactive recommendations of composition knowledge in the form of reusable mashup model patterns. We design and study three different recommendation algorithms and describe a pattern weaving approach for the one-click reuse of composition knowledge. We report on the implementation of three pattern recommender plugins for different mashup tools and demonstrate via user studies that recommending and weaving contextual mashup model patterns significantly reduces development times in all three cases
Ami-deu : un cadre sémantique pour des applications adaptables dans des environnements intelligents
Cette thĂšse vise Ă Ă©tendre lâutilisation de l'Internet des objets (IdO) en facilitant le dĂ©veloppement dâapplications par des personnes non experts en dĂ©veloppement logiciel. La thĂšse propose une nouvelle approche pour augmenter la sĂ©mantique des applications dâIdO et lâimplication des experts du domaine dans le dĂ©veloppement dâapplications sensibles au contexte. Notre approche permet de gĂ©rer le contexte changeant de lâenvironnement et de gĂ©nĂ©rer des applications qui sâexĂ©cutent dans plusieurs environnements intelligents pour fournir des actions requises dans divers contextes. Notre approche est mise en Ćuvre dans un cadriciel (AmI-DEU) qui inclut les composants pour le dĂ©veloppement dâapplications IdO. AmI-DEU intĂšgre les services dâenvironnement, favorise lâinteraction de lâutilisateur et fournit les moyens de reprĂ©senter le domaine dâapplication, le profil de lâutilisateur et les intentions de lâutilisateur. Le cadriciel permet la dĂ©finition dâapplications IoT avec une intention dâactivitĂ© autodĂ©crite qui contient les connaissances requises pour rĂ©aliser lâactivitĂ©. Ensuite, le cadriciel gĂ©nĂšre Intention as a Context (IaaC), qui comprend une intention dâactivitĂ© autodĂ©crite avec des connaissances colligĂ©es Ă Ă©valuer pour une meilleure adaptation dans des environnements intelligents.
La sĂ©mantique de lâAmI-DEU est basĂ©e sur celle du ContextAA (Context-Aware Agents) â une plateforme pour fournir une connaissance du contexte dans plusieurs environnements. Le cadriciel effectue une compilation des connaissances par des rĂšgles et l'appariement sĂ©mantique pour produire des applications IdO autonomes capables de sâexĂ©cuter en ContextAA. AmI- DEU inclut Ă©galement un outil de dĂ©veloppement visuel pour le dĂ©veloppement et le dĂ©ploiement rapide d'applications sur ContextAA. L'interface graphique dâAmI-DEU adopte la mĂ©taphore du flux avec des aides visuelles pour simplifier le dĂ©veloppement d'applications en permettant des dĂ©finitions de rĂšgles Ă©tape par Ă©tape. Dans le cadre de lâexpĂ©rimentation, AmI-DEU comprend un banc dâessai pour le dĂ©veloppement dâapplications IdO. Les rĂ©sultats expĂ©rimentaux montrent une optimisation sĂ©mantique potentielle des ressources pour les applications IoT dynamiques dans les maisons intelligentes et les villes intelligentes.
Notre approche favorise l'adoption de la technologie pour amĂ©liorer le bienĂȘtre et la qualitĂ© de vie des personnes. Cette thĂšse se termine par des orientations de recherche que le cadriciel AmI-DEU dĂ©voile pour rĂ©aliser des environnements intelligents omniprĂ©sents fournissant des adaptations appropriĂ©es pour soutenir les intentions des personnes.Abstract: This thesis aims at expanding the use of the Internet of Things (IoT) by facilitating the development of applications by people who are not experts in software development. The thesis proposes a new approach to augment IoT applicationsâ semantics and domain expert involvement in context-aware application development. Our approach enables us to manage the changing environment context and generate applications that run in multiple smart environments to provide required actions in diverse settings. Our approach is implemented in a framework (AmI-DEU) that includes the components for IoT application development. AmI- DEU integrates environment services, promotes end-user interaction, and provides the means to represent the application domain, end-user profile, and end-user intentions. The framework enables the definition of IoT applications with a self-described activity intention that contains the required knowledge to achieve the activity. Then, the framework generates Intention as a Context (IaaC), which includes a self-described activity intention with compiled knowledge to be assessed for augmented adaptations in smart environments. AmI-DEU framework semantics adopts ContextAA (Context-Aware Agents) â a platform to provide context-awareness in multiple environments. The framework performs a knowledge compilation by rules and semantic matching to produce autonomic IoT applications to run in ContextAA. AmI-DEU also includes a visual tool for quick application development and deployment to ContextAA. The AmI-DEU GUI adopts the flow metaphor with visual aids to simplify developing applications by allowing step-by-step rule definitions. As part of the experimentation, AmI-DEU includes a testbed for IoT application development. Experimental results show a potential semantic optimization for dynamic IoT applications in smart homes and smart cities. Our approach promotes technology adoption to improve peopleâs well-being and quality of life. This thesis concludes with research directions that the AmI-DEU framework uncovers to achieve pervasive smart environments providing suitable adaptations to support peopleâs intentions
Might-E Wheel
Electric bikes are proving to be an increasingly reliable source of transportation, but with large price tags and existing conversion kits proving too complicated and unapproachable for the average user, commuters are failing to consider electric bicycles as an option. A prototype of a wireless, rear-wheel replacement to convert a bicycle from manual to full-power electric was built. This motorized wheel is called Might-E Wheel. Might-E Wheel was designed as the most approachable and user-friendly way to convert a bicycle from manual to full-power electric. Might-E Wheel was able to achieve a top speed of 27.5 km/h, a range of 24.6 km, and run at 311 W of power. Range tests were inconclusive. The fully assembled system weighed 6.8 kg. The system was found to adequately meet the goals of the project. Battery failure limited the testing of Might-E Wheel, but the system was found to run smoothly before the failure, which was unrelated to the system design. In the future, further tests are planned with new batteries. Also, further development of the product is desired in order to lower the weight and reduce the size of the system. Ideally, the next prototype of the system would consist of a custom built motor and a fully-enclosed system within the hub of the wheel
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Solving mechanics problems using numerical methods requires comprehensive
intelligent capability of retrieving relevant knowledge and theory,
constructing and executing codes, analyzing the results, a task that has thus
far mainly been reserved for humans. While emerging AI methods can provide
effective approaches to solve end-to-end problems, for instance via the use of
deep surrogate models or various data analytics strategies, they often lack
physical intuition since knowledge is baked into the parametric complement
through training, offering less flexibility when it comes to incorporating
mathematical or physical insights. By leveraging diverse capabilities of
multiple dynamically interacting large language models (LLMs), we can overcome
the limitations of conventional approaches and develop a new class of
physics-inspired generative machine learning platform, here referred to as
MechAgents. A set of AI agents can solve mechanics tasks, here demonstrated for
elasticity problems, via autonomous collaborations. A two-agent team can
effectively write, execute and self-correct code, in order to apply finite
element methods to solve classical elasticity problems in various flavors
(different boundary conditions, domain geometries, meshes, small/finite
deformation and linear/hyper-elastic constitutive laws, and others). For more
complex tasks, we construct a larger group of agents with enhanced division of
labor among planning, formulating, coding, executing and criticizing the
process and results. The agents mutually correct each other to improve the
overall team-work performance in understanding, formulating and validating the
solution. Our framework shows the potential of synergizing the intelligence of
language models, the reliability of physics-based modeling, and the dynamic
collaborations among diverse agents, opening novel avenues for automation of
solving engineering problems
Human Resource Information Systems for Competitive Advantage: Interviews with Ten Leaders
[Excerpt] Increasingly, today\u27s organizations use computer technology to manage human resources (HR). Surveys confirm this trend (Richards-Carpenter, 1989; Grossman and Magnus, 1988; Human Resource Systems Professionals 1988; KPMGPeat Marwick, 1988). HR professionals and managers routinely have Personnel Computers (PCs) or computer terminals on their desks or in their departments. HR computer applications, once confined to payroll and benefit domains, now encompass incentive compensation, staffing, succession planning, and training. Five years ago, we had but a handful of PC-based software applications for HR management. Today, we find a burgeoning market of products spanning a broad spectrum of price, sophistication, and quality (Personnel Journal, 1990). Top universities now consider computer literacy a basic requirement for students of HR, and many consulting firms and universities offer classes designed to help seasoned HR professionals use computers in their work (Boudreau, 1990). Changes in computer technology offer expanding potential for HR management (Business Week, 1990; Laudon and Laudon, 1988)
Automating the Classification of Thematic Rasters for Weighted Overlay Analysis in GeoPlanner for ArcGIS
Esriâs GeoPlanner for ArcGIS application provides powerful analysis capabilities through the weighted overlay analysis modeler. This modeler consumes weighted overlay services composed of pre-processed raster layers. Creating custom weighted overlay services for GeoPlanner is a difficult and complex process that requires both domain-specific and GIS expertise. This challenge was addressed by simplifying the weighted overlay service creation workflow and developing two new custom Python tools that guide GeoPlanner users through the process of preparing input datasets and then classifying the raster datasets. Where possible these tools automate the required steps and where user input is needed, the tools provide default recommendations based on the input datasets properties and characteristics. As a result, the weighted overlay services creation workflow has been significantly improved and more GeoPlanner users can include their own data in weighted overlay analyses
- âŠ