A comparative survey of integrated learning systems

This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework

Wild Westworld: Section 230 of the CDA and Social Networks’ Use of Machine-Learning Algorithms

This Note argues that Facebook’s services—specifically the personalization of content through machine-learning algorithms—constitute the “development” of content and as such do not qualify for § 230 immunity. This Note analyzes the evolution of § 230 jurisprudence to help inform the development of a revised framework. This framework is guided by congressional and public policy goals and creates brighter lines for technological immunity. It tailors immunity to account for user data mined by ISPs and the pervasive effect that the use of that data has on users—two issues that courts have yet to confront. This Note concludes that under the revised framework, machine-learning algorithms’ content organization— made effective through the collection of individualized data—make ISPs codevelopers of content and thus bar them from immunity

Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

Toward a Systematic Evidence-Base for Science in Out-of-School Time: The Role of Assessment

Analyzes the tools used in assessments of afterschool and summer science programs, explores the need for comprehensive tools for comparisons across programs, and discusses the most effective structure and format for such a tool. Includes recommendations

PASS: a simple classifier system for data analysis

Let x be a vector of predictors and y a scalar response associated with it. Consider the regression problem of inferring the relantionship between predictors and response on the basis of a sample of observed pairs (x,y). This is a familiar problem for which a variety of methods are available. This paper describes a new method based on the classifier system approach to problem solving. Classifier systems provide a rich framework for learning and induction, and they have been suc:cessfully applied in the artificial intelligence literature for some time. The present method emiches the simplest classifier system architecture with some new heuristic and explores its potential in a purely inferential context. A prototype called PASS (Predictive Adaptative Sequential System) has been built to test these ideas empirically. Preliminary Monte Carlo experiments indicate that PASS is able to discover the structure imposed on the data in a wide array of cases

MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation

An architectural approach to self-adaptive systems involves runtime change of system configuration (i.e., the system's components, their bindings and operational parameters) and behaviour update (i.e., component orchestration). Thus, dynamic reconfiguration and discrete event control theory are at the heart of architectural adaptation. Although controlling configuration and behaviour at runtime has been discussed and applied to architectural adaptation, architectures for self-adaptive systems often compound these two aspects reducing the potential for adaptability. In this paper we propose a reference architecture that allows for coordinated yet transparent and independent adaptation of system configuration and behaviour

Explainable methods for knowledge graph refinement and exploration via symbolic reasoning

Knowledge Graphs (KGs) have applications in many domains such as Finance, Manufacturing, and Healthcare. While recent efforts have created large KGs, their content is far from complete and sometimes includes invalid statements. Therefore, it is crucial to refine the constructed KGs to enhance their coverage and accuracy via KG completion and KG validation. It is also vital to provide human-comprehensible explanations for such refinements, so that humans have trust in the KG quality. Enabling KG exploration, by search and browsing, is also essential for users to understand the KG value and limitations towards down-stream applications. However, the large size of KGs makes KG exploration very challenging. While the type taxonomy of KGs is a useful asset along these lines, it remains insufficient for deep exploration. In this dissertation we tackle the aforementioned challenges of KG refinement and KG exploration by combining logical reasoning over the KG with other techniques such as KG embedding models and text mining. Through such combination, we introduce methods that provide human-understandable output. Concretely, we introduce methods to tackle KG incompleteness by learning exception-aware rules over the existing KG. Learned rules are then used in inferring missing links in the KG accurately. Furthermore, we propose a framework for constructing human-comprehensible explanations for candidate facts from both KG and text. Extracted explanations are used to insure the validity of KG facts. Finally, to facilitate KG exploration, we introduce a method that combines KG embeddings with rule mining to compute informative entity clusters with explanations.Wissensgraphen haben viele Anwendungen in verschiedenen Bereichen, beispielsweise im Finanz- und Gesundheitswesen. Wissensgraphen sind jedoch unvollständig und enthalten auch ungültige Daten. Hohe Abdeckung und Korrektheit erfordern neue Methoden zur Wissensgraph-Erweiterung und Wissensgraph-Validierung. Beide Aufgaben zusammen werden als Wissensgraph-Verfeinerung bezeichnet. Ein wichtiger Aspekt dabei ist die Erklärbarkeit und Verständlichkeit von Wissensgraphinhalten für Nutzer. In Anwendungen ist darüber hinaus die nutzerseitige Exploration von Wissensgraphen von besonderer Bedeutung. Suchen und Navigieren im Graph hilft dem Anwender, die Wissensinhalte und ihre Limitationen besser zu verstehen. Aufgrund der riesigen Menge an vorhandenen Entitäten und Fakten ist die Wissensgraphen-Exploration eine Herausforderung. Taxonomische Typsystem helfen dabei, sind jedoch für tiefergehende Exploration nicht ausreichend. Diese Dissertation adressiert die Herausforderungen der Wissensgraph-Verfeinerung und der Wissensgraph-Exploration durch algorithmische Inferenz über dem Wissensgraph. Sie erweitert logisches Schlussfolgern und kombiniert es mit anderen Methoden, insbesondere mit neuronalen Wissensgraph-Einbettungen und mit Text-Mining. Diese neuen Methoden liefern Ausgaben mit Erklärungen für Nutzer. Die Dissertation umfasst folgende Beiträge: Insbesondere leistet die Dissertation folgende Beiträge: • Zur Wissensgraph-Erweiterung präsentieren wir ExRuL, eine Methode zur Revision von Horn-Regeln durch Hinzufügen von Ausnahmebedingungen zum Rumpf der Regeln. Die erweiterten Regeln können neue Fakten inferieren und somit Lücken im Wissensgraphen schließen. Experimente mit großen Wissensgraphen zeigen, dass diese Methode Fehler in abgeleiteten Fakten erheblich reduziert und nutzerfreundliche Erklärungen liefert. • Mit RuLES stellen wir eine Methode zum Lernen von Regeln vor, die auf probabilistischen Repräsentationen für fehlende Fakten basiert. Das Verfahren erweitert iterativ die aus einem Wissensgraphen induzierten Regeln, indem es neuronale Wissensgraph-Einbettungen mit Informationen aus Textkorpora kombiniert. Bei der Regelgenerierung werden neue Metriken für die Regelqualität verwendet. Experimente zeigen, dass RuLES die Qualität der gelernten Regeln und ihrer Vorhersagen erheblich verbessert. • Zur Unterstützung der Wissensgraph-Validierung wird ExFaKT vorgestellt, ein Framework zur Konstruktion von Erklärungen für Faktkandidaten. Die Methode transformiert Kandidaten mit Hilfe von Regeln in eine Menge von Aussagen, die leichter zu finden und zu validieren oder widerlegen sind. Die Ausgabe von ExFaKT ist eine Menge semantischer Evidenzen für Faktkandidaten, die aus Textkorpora und dem Wissensgraph extrahiert werden. Experimente zeigen, dass die Transformationen die Ausbeute und Qualität der entdeckten Erklärungen deutlich verbessert. Die generierten unterstützen Erklärungen unterstütze sowohl die manuelle Wissensgraph- Validierung durch Kuratoren als auch die automatische Validierung. • Zur Unterstützung der Wissensgraph-Exploration wird ExCut vorgestellt, eine Methode zur Erzeugung von informativen Entitäts-Clustern mit Erklärungen unter Verwendung von Wissensgraph-Einbettungen und automatisch induzierten Regeln. Eine Cluster-Erklärung besteht aus einer Kombination von Relationen zwischen den Entitäten, die den Cluster identifizieren. ExCut verbessert gleichzeitig die Cluster- Qualität und die Cluster-Erklärbarkeit durch iteratives Verschränken des Lernens von Einbettungen und Regeln. Experimente zeigen, dass ExCut Cluster von hoher Qualität berechnet und dass die Cluster-Erklärungen für Nutzer informativ sind

Biotechnology business in Norway: Peripheral advantage, or just periphery?

Research on biotechnology related firms has long been associated with the agglomeration of “dedicated biotechnology firms” and partners such as large corporations, research institutes and venture capital firms. At the same time it is acknowledged that such agglomeration trends may be most closely associated with medical biotechnology. This paper shows that there are more than 130 firms which may be classified as biotechnology related firms in Norway. Furthermore they are spread out throughout the country, albeit with more than half being located in the Eastern part. While there is indeed a great preoccupation with medical biotechnology also in Norway, the survey shows that two other distinct traits are present: a concentration of firms being focused on diagnostics and drug delivery rather than on therapeutics, and a focus on marine biotechnology (more than one third of the firms) within nutrition related products rather than or in addition to medical products. In addition to contributing these descriptive findings the paper thus opens up the agglomeration discussion, and suggests that foci on such niches as those which are prevalent in Norway may function with geographical distribution patterns different from those prevalent within the current medical biotechnology focused literature.

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl