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Multi‐adjoint relation equations are defined by means of a sup‐composition operator involving different conjunctions. Former works reveal that the resolution of a multi‐adjoint relation equation is closely related to such conjunctions. This paper presents a first approach on the study of the influence of the selection of a sup‐composition operator in different aspects of multi‐adjoint relation equations, as their solvability or the extremal elements of their solution set. The obtained results are illustrated by an example concerning multi‐adjoint logic programming. Last but not least, a procedure for reducing the computation of the solution set when only the sup‐composition operator is modified is also introduced. [ABSTRACT FROM AUTHOR]

Artificial temporal signaling systems, which mimic living out‐of‐equilibrium conditions, have made large progress. However, systems programmed by enzymatic reaction networks in multicomponent and unknown environments, and using biocompatible components remain a challenge. Herein, we demonstrate an approach to program temporal pH signals by enzymatic logic gates. They are realized by an enzymatic disaccharide‐to‐monosaccharide‐to‐sugar acid reaction cascade catalyzed by two metabolic chains: invertase‐glucose oxidase and β‐galactosidase‐glucose oxidase, respectively. Lifetimes of the transient pH signal can be programmed from less than 15 min to more than 1 day. We study enzymatic kinetics of the reaction cascades and reveal the underlying regulatory mechanisms. Operating with all‐food grade chemicals and coupling to self‐regulating hydrogel, our system is quite robust to work in a complicated medium with unknown components and in a biocompatible fashion. [ABSTRACT FROM AUTHOR]

Artificial temporal signaling systems, which mimic living out‐of‐equilibrium conditions, have made large progress. However, systems programmed by enzymatic reaction networks in multicomponent and unknown environments, and using biocompatible components remain a challenge. Herein, we demonstrate an approach to program temporal pH signals by enzymatic logic gates. They are realized by an enzymatic disaccharide‐to‐monosaccharide‐to‐sugar acid reaction cascade catalyzed by two metabolic chains: invertase‐glucose oxidase and β‐galactosidase‐glucose oxidase, respectively. Lifetimes of the transient pH signal can be programmed from less than 15 min to more than 1 day. We study enzymatic kinetics of the reaction cascades and reveal the underlying regulatory mechanisms. Operating with all‐food grade chemicals and coupling to self‐regulating hydrogel, our system is quite robust to work in a complicated medium with unknown components and in a biocompatible fashion. [ABSTRACT FROM AUTHOR]

Constructionism, long before it had a name, was intimately tied to the field of Artificial Intelligence. Soon after the birth of Logo at BBN, Seymour Papert set up the Logo Group as part of the MIT AI Lab. Logo was based upon Lisp, the first prominent AI programming language. Many early Logo activities involved natural language processing, robotics, artificial game players, and generating poetry, art, and music. In the 1970s researchers explored enhancements to Logo to support AI programming by children. In the 1980s the Prolog community, inspired by Logo's successes, began exploring how to adapt logic programming for use by school children. While there have been over 40 years of active AI research in creating intelligent tutoring systems, there was little AI‐flavoured constructionism after the 1980s until about 2017 when suddenly a great deal of activity started. Amongst those activities were attempts to enhance Scratch, Snap!, and MIT App Inventor with new blocks for speech synthesis, speech recognition, image recognition, and the use of pre‐trained deep learning models. The Snap! enhancements also include support for word embeddings, as well as blocks to enable learners to create, train, and use deep neural networks. Student and teacher project‐oriented resources highlighting these new AI programming components appeared at the same time. In this paper, we review this history, providing a unique perspective on AI developments—both social and technical—from a constructionist perspective. Reflecting on these, we close with speculations about possible futures for AI and constructionism. Practitioner notesWhat is already known about this topicThere exist excellent broad surveys of the current status of teaching machine learning in schools, for example Marques et al. (2020). There are historical collections of AI and education research papers that include descriptions of constructionist activities, for example Yazdani (1984).What this paper addsThis paper adds an in‐depth focus on historical and current efforts on AI and education that support constructionist teaching. This focus enables us to delve deeper than a broad survey. Uniquely, we provide a 50‐year historical perspective on constructionist AI tools, trials, and research. Grounded in this history and our survey of current tools and projects, we provide speculations about future directions.Implications for practice and/or policyWe hope our descriptions of current AI programming tools for non‐experts placed in a broad historical context will be of use to teachers wishing to introduce AI to their students in a constructionist manner, as well as to developers and researchers aiming to support such teaching. [ABSTRACT FROM AUTHOR]

Inductive logic programming (ILP) induces concepts from a set of positive examples, a set of negative examples, and background knowledge. ILP has been applied on tasks such as natural language processing, finite element mesh design, network mining, robotics, and drug discovery. These data sets usually contain numerical and multivalued categorical attributes; however, only a few relational learning systems are capable of handling them in an efficient way. In this paper, we present an evolutionary approach, called Grouping and Discretization for Enriching the Background Knowledge (GDEBaK), to deal with numerical and multivalued categorical attributes in ILP. This method uses evolutionary operators to create and test numerical splits and subsets of categorical values in accordance with a fitness function. The best subintervals and subsets are added to the background knowledge before constructing candidate hypotheses. We implemented GDEBaK embedded in Aleph and compared it to lazy discretization in Aleph and discretization in Top‐down Induction of Logical Decision Trees (TILDE) systems. The results obtained showed that our method improves accuracy and reduces the number of rules in most cases. Finally, we discuss these results and possible lines for future work. [ABSTRACT FROM AUTHOR]

The Semantic Web ontologies can contain vague axioms, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption if the necessary information is incomplete (there is an ignorance about information). An axiom description can be very exact (crisp axiom) or exact (fuzzy axiom) if its knowledge is complete, otherwise it is inexact (vague axiom) if its knowledge is incomplete. Here, the author proposes vagueness description with meta‐level logic programming to describe vague ontologies. These vagueness descriptions are inputs to vagueness reasoning procedure implemented at meta‐level, which is based on extended tableau algorithm. The extended tableau algorithm is intended to answer queries even with the presence of imprecise information. [ABSTRACT FROM AUTHOR]

Here, the authors present a study on a contactless interface between a fully integrated wireless power receiver (WPR) and a dipole antenna. The authors analyse the impedance matching between the antenna and the inductive link formed by the contactless interface for providing an appropriate design methodology. Furthermore, the authors developed a measurement strategy to verify the entire system comprised by a pair of dipole antennas, the inductive link, and the CMOS WPR. The results show that the CMOS die is well coupled to the antenna, the complete system operates correctly with a source of 26.6 dBm at 987 MHz when the distance between the dipole antennas is 58 cm. [ABSTRACT FROM AUTHOR]

Abstract: Nonvolatile optoelectronic memories integrated with the functions of sensing, data storage, and data processing are promising for the potential Internet of things (IoT) applications. To meet the requirements of IoT devices, multifunctional memory devices with low power consumption and secure data storage are highly desirable. This study demonstrates an optoelectronic resistive switching memory integrated with sensing and logic operations by adopting organic–inorganic hybrid CH3NH3PbI3−xClx perovskites, which possess unusual defect physics and excellent light absorption. The CH3NH3PbI3−xClx cell exhibits low operation voltage of 0.1 V with the assistance of light illumination, long‐term retention property, and multiple resistance states. Its unique optoelectronic characteristics enable to perform logic operation for inputting one electrical pulse and one optical signal, and detect the coincidence of electrical and optical signal as well. This design provides possibilities for smart sensor in IoT application. [ABSTRACT FROM AUTHOR]

Abstract: Logic Programming (LP) follows the declarative programming paradigm, which novice students often find hard to grasp. The limited availability of visual teaching aids for LP can lead to low motivation for learning. In this paper, we present a platform for teaching and learning Prolog in Virtual Worlds, which enables the visual interpretation and verification of program results in a straightforward fashion and requires students to adopt a collaborative problem‐solving approach. The results of the pilot application and student‐centered evaluation of the platform are encouraging regarding group learning performance and user experience. The paper contributes to current practice of teaching and learning LP by proposing a metaphor and a system that can empower the educational process with toy world examples visualized in a shared 3D environment. [ABSTRACT FROM AUTHOR]

The existing approaches that map the given explicit preferences into standard assumption-based argumentation (ABA) frameworks reveal some difficulties such as generating a huge number of rules. To overcome them, we present an assumption-based argumentation framework equipped with preferences (p_ABA). It increases the expressive power of ABA by incorporating preferences between sentences into the framework. The semantics of p_ABA is given by [ABSTRACT FROM AUTHOR]

An effective means to analyze mRNA expression data is to take advantage of established knowledge from pathway databases, using methods such as pathway-enrichment analyses. However, pathway databases are not case-specific and expression data could be used to infer gene-regulation patterns in the context of specific pathways. In addition, canonical pathways may not always describe the signaling mechanisms properly, because interactions can frequently occur between genes in different pathways. Relatively few methods have been proposed to date for generating and analyzing such networks, preserving the causality between gene interactions and reasoning over the qualitative logic of regulatory effects. We present an algorithm (MCWalk) integrated with a logic programming approach, to discover subgraphs in large-scale signaling networks by random walks in a fully automated pipeline. As an exemplary application, we uncover the signal transduction mechanisms in a gene interaction network describing hepatocyte growth factor-stimulated cell migration and proliferation from gene-expression measured with microarray and RT-qPCR using in-house perturbation experiments in a keratinocyte-fibroblast co-culture. The resulting subgraphs illustrate possible associations of hepatocyte growth factor receptor c-Met nodes, differentially expressed genes and cellular states. Using perturbation experiments and Answer Set programming, we are able to select those which are more consistent with the experimental data. We discover key regulator nodes by measuring the frequency with which they are traversed when connecting signaling between receptors and significantly regulated genes and predict their expression-shift consistently with the measured data. The Java implementation of MCWalk is publicly available under the MIT license at: . [ABSTRACT FROM AUTHOR]