[en] Incremental development and maintenance of large systems imply that control be clearly separated from knowledge. Finding efficient control for a given class of knowledge is itself a matter of expertise, to which knowledge-based methods may and should be applied. We present here two attempts at building root systems that may later be tuned by knowledge engineers, using the semantics of each particular application. These systems are given heuristics in a declarative manner, which they use to control the application of heuristics. Eventually, some heuristics may be used to compile others (or themselves) into efficient pieces of programmed code

[en] Structural optimization has many characteristics of Soft Design, and so, it is necessary to apply the experience of human experts to solving the uncertain and multidisciplinary optimization problems in large-scale and complex engineering systems. With the development of artificial intelligence (AI) and computational intelligence (CI), the theory of structural optimization is now developing into the direction of intelligent optimization. In this paper, a concept of Intelligent Structural Optimization (ISO) is proposed. And then, a design process model of ISO is put forward in which each design sub-process model are discussed. Finally, the design methods of ISO are presented

[en] It is suggested that the mind is able to go beyond the limitations of sound algorithmic deductions by virtue of the fact that it is not error free, and a proposal is made concerning how one might go about trying to construct an artificial mind. (author). 2 refs

[en] We propose an unsupervised machine learning algorithm for anomaly detection that exploits self-learnt features of monodimensional time series. A Variational Autoencoder, where convolution takes place of dot product, is trained to compress each input to a low-dimensional point from a normal distribution, detecting an anomaly as low probability and high density sequence. We validate our work on different public datasets, obtaining results that shed new light on Variational Autoencoders applied to anomaly detection.

[en] General Topology has become one of the fundamental parts of mathematics. Nowadays, as a consequence of an intensive research activity, this mathematical branch has been shown to be very useful in modeling several problems which arise in some branches of applied sciences as Economics, Artificial Intelligence and Computer Science.Due to this increasing interaction between applied and topological problems,we have promoted the creation of an annual workshop to encourage the collaboration between different national and international research groups in the area of General Topology and its Applications.

No abstract available

[en] Molecular similarity measures within the quantum concept of density functions are described and analyzed. It is intended to show how artificial intelligence techniques can be used within the framework of quantum theory, in order to study and classify the molecular structures and their properties. (A.C.A.S)

[en] Highlights: • Provides strategies adapted from established business models to ensure the success of radiology with the advent of Artificial Intelligence technology. • Explores future use cases for clinical radiology augmented with Artificial Intelligence technology. • Examines the legal and ethical issues within health policy applied to Artificial Intelligence systems. • Considers the impact on jobs and discusses mitigation of these risks. - Abstract: The rapid development of Artificial Intelligence/deep learning technology and its implementation into routine clinical imaging will cause a major transformation to the practice of radiology. Strategic positioning will ensure the successful transition of radiologists into their new roles as augmented clinicians. This paper describes an overall vision on how to achieve a smooth transition through the practice of augmented radiology where radiologists-in-the-loop ensure the safe implementation of Artificial Intelligence systems.

[en] After discussing the current status of the automation in signal interpretation from seismic networks, a new approach, based on artificial-intelligence tecniques, is proposed. The knowledge of the human expert analyst is examined, with emphasis on its objects, strategies and reasoning techniques. It is argued that knowledge-based systems (or expert systems) provide the most appropriate tools for designing an automatic system, modelled on the expert behaviour

[en] There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions. The experiments show that this method can improve the recognition rate and the time of feature extraction