In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment. The underlying idea is that such systems must have self-x properties in order to adapt their behavior to external disturbances with a high-degree of autonomy.
TOWARD ORGANIC COMPUTING APPROACH FOR CYBERNETIC RESPONSIVE ENVIRONMENT

Existing methods for dealing with knowledge updates differ greatly depending on the underlying knowledge representation formalism. When Classical Logic is used, updates are typically performed by manipulating the knowledge base on the model-theoretic level. On the opposite side of the spectrum stand the semantics for updating Answer-Set Programs that need to rely on rule syntax. Yet, a unifying perspective that could embrace both these branches of research is of great importance as it enables a deeper understanding of all involved methods and principles and creates room for their cross-fertilisation, ripening and further development. Furthermore, from a more pragmatic viewpoint, such a unification is a necessary step in addressing updates of hybrid knowledge bases consisting of both a classical and a rule component. This paper bridges the seemingly irreconcilable approaches to updates. It introduces a novel monotonic characterisation of rules, dubbed RE-models, and shows it to be a more suitable semantic foundation for rule updates than SE-models. Then it proposes a generic scheme for specifying semantic rule update operators, based on the idea of viewing a program as the set of sets of RE-models of its rules; updates are performed by introducing additional interpretations exceptions to the sets of RE-models of rules in the original program. The introduced scheme is then used to define particular rule update operators that are closely related to both classical update principles and traditional approaches to rules updates, enjoying a range of plausible syntactic as well as semantic properties. In addition, these operators serve as a basis for a solution to the long-standing problem of state condensing for two of the foundational rule update semantics, showing how they can be equivalently defined as binary operators on some class of logic programs. Finally, the essence of these ideas is extracted to define an abstract framework for exception-based update operators, viewing a knowledge base as the set of sets of models of its elements. It is shown that the framework can capture a wide range of both modeland formula-based classical update operators, and thus serves as the first firm formal ground connecting classical and rule updates.
Exception-Based Knowledge Updates

Chemical-chemical interaction (CCI) plays a key role in predicting candidate drugs, toxicity, therapeutic e'ects, and biological functions. CCI was created from text mining, experiments, similarities, and databases; to date, no learning-based CCI prediction method exist. In chemical analyses, computational approaches are required. e recent remarkable growth and outstanding performance of deep learning have aSracted considerable research aSention. However, even in state-of-the-art drug analyses, deep learning continues to be used only as a classier. Nevertheless, its purpose includes not only simple classication, but also automated feature extraction. In this paper, we propose the rst end-to-end learning method for CCI, named DeepCCI. Hidden features are derived from a simplied molecular input line entry system (SMILES), which is a string notation representing the chemical structure, instead of learning from craed features. To discover hidden representations for the SMILES strings, we use convolutional neural networks (CNNs). To guarantee the commutative property for homogeneous interaction, we apply model sharing and hidden representation merging techniques. e performance of DeepCCI was compared with a plain deep classier and conventional machine learning methods. e proposed DeepCCI showed the best performance in all seven evaluation metrics used. In addition, the commutative property was experimentally validated. e automatically extracted features through end-to-end SMILES learning alleviates the signicant efforts required for manual feature engineering. It is expected to improve prediction performance, in drug analyses.
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction