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Öğe Adaptable Runtime Monitoring for Intermittent Systems(Association for Computing Machinery, Inc, 2024) Yildiz E.; Akhunov K.; Riva L.A.; Goknil A.; Kurtev I.; Yildirim K.S.Batteryless energy harvesting devices compute intermittently due to power failures that frequently interrupt the computational activity and lead to charging delays. To ensure functional correctness in intermittent computing, applications must exhibit several unique properties, such as guarantees for computational progress despite power failures and prevention of stale operations caused by charging delays. We observe that current software support for intermittent computing allows for checking only a fixed set of properties and leads to tightly coupled application and property-checking, thus hampering modularity, scalability, and maintainability. In this paper, we present ARTEMIS, the first framework designed to facilitate flexible property checking of intermittent programs at runtime. ARTEMIS is developed based on techniques from the area of runtime monitoring, offers a specification language for specifying an open set of properties, and provides automatic generation of monitors responsible for checking the properties. Our evaluation showed that ARTEMIS achieves comparable efficiency to state-of-the-art solutions while significantly preventing failure scenarios through its monitoring capabilities. © 2024 ACM.Öğe An AI pipeline for garment price projection using computer vision(Springer Science and Business Media Deutschland GmbH, 2024) Rico Gómez R.; Lorentz J.; Hartmann T.; Goknil A.; Pal Singh I.; Halaç T.G.; Boruzanlı Ekinci G.The fashion industry’s traditional price-setting methods, based on historical sales and Fashion Week trends, are inadequate in the digital era. Rapid changes in collections and consumer preferences necessitate advanced Artificial Intelligence (AI) techniques. These AI methods should analyze data from various sources, including social media and e-commerce, to predict future fashion trends and prices. In this paper, we propose, apply, and assess a data analytics approach, i.e., FashionXpert, employing several image processing and machine learning techniques in an AI pipeline for garment price prediction. It integrates various heterogeneous data sources (e.g., textual and image data from e-stores, brand websites, and social media) to obtain more consistent, accurate, and beneficial information. We evaluated its effectiveness with an industrial data set obtained by a fashion search tool from the electronic commerce sites of clothing brands. FashionXpert predicted garment prices with an average Mean Absolute Error (MAE) of 15.31 EUR on a data set that has a standard deviation of 72.99 EUR. © The Author(s) 2024.Öğe Model driven development of semantic web enabled multi-agent systems(2009) Kardas G.; Goknil A.; Dikenelli O.; Topaloglu N.Y.Semantic Web evolution brought a new vision into agent research. The interpretation of this second generation web will be realized by autonomous computational entities, called agents, to handle the semantic content on behalf of their human users. Surely, Semantic Web environment has specific architectural entities and a different semantic which must be considered to model a Multi-agent System (MAS) within this environment. Hence, in this study, we introduce a MAS development process which supports the Semantic Web environment. Our approach is based on Model Driven Development (MDD) which aims to change the focus of software development from code to models. We first define an architecture for Semantic Web enabled MASs and then provide a MAS metamodel which consists of the first class meta-entities derived from this architecture. We also define a model transformation process for MDD of such MASs. We present a complete transformation process in which the source and the target metamodels, entity mappings between models and the implementation of the transformation for two different real MAS frameworks by using a well-known model transformation language are all included. In addition to the model-to-model transformation, the implementation of the model-to-code transformation is given as the last step of the system development process. The evaluation of the proposed development process by considering its use within the scope of a real commercial software project is also discussed. © 2009 World Scientific Publishing Company.Öğe Ontological perspective in metamodeling for model transformations(2005) Goknil A.; Topaloglu Y.Model Driven Engineering (MDE) aims to facilitate building larger and more complex, reliable software systems by introducing a higher abstraction level than the code level. The technical space concept discusses how the basic MDE principles may be mapped onto modern platform support and several technical spaces are proposed to support MDE. In this paper, we propose to use the ontology technical space in model transformations to achieve the targets of MDE. Using the ontology technical space will enable us to model not only the meta concepts but also the semantic context which can be used in model inferencing. Within this context, we define meta models of object oriented models ontologicaly. © 2005 ACM.Öğe Ontology based model transformation infrastructure(2005) Goknil A.; Topaloglu N.Y.Using MDA in ontology development has been investigated in several works recently. The mappings and transformations between the UML constructs and the OWL elements to develop ontologies are the main concern of these research projects. We propose another approach in order to achieve the collaboration between MDA and ontology technologies. We propose an ontology based model transformation infrastructure to transform application models by using query statements, transformation rules and models defined as ontologies in OWL. Using this approach in model transformation infrastructure will enable us to use semantic web and ontology facilities in model driven architecture. This paper will discuss how these two technologies come together to provide automatization in model transformations.
