Representation Independent Analytics Over Structured Data

Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties observed over particular representations do not necessarily hold for alternative structures. Thus, there is no guarantee that current database analytics algorithms will still provide the correct insights, no matter what structures are chosen to organize the database. Because these algorithms tend to be highly effective over some choices of structure, such as that of the databases used to validate them, but not so effective with others, database analytics has largely remained the province of experts who can find the desired forms for these algorithms. We argue that in order to make database analytics usable, we should use or develop algorithms that are effective over a wide range of choices of structural organizations. We introduce the notion of representation independence, study its fundamental properties for a wide range of data analytics algorithms, and empirically analyze the amount of representation independence of some popular database analytics algorithms. Our results indicate that most algorithms are not generally representation independent and find the characteristics of more representation independent heuristics under certain representational shifts

Expert system technology

The expert system is a computer program which attempts to reproduce the problem-solving behavior of an expert, who is able to view problems from a broad perspective and arrive at conclusions rapidly, using intuition, shortcuts, and analogies to previous situations. Expert systems are a departure from the usual artificial intelligence approach to problem solving. Researchers have traditionally tried to develop general modes of human intelligence that could be applied to many different situations. Expert systems, on the other hand, tend to rely on large quantities of domain specific knowledge, much of it heuristic. The reasoning component of the system is relatively simple and straightforward. For this reason, expert systems are often called knowledge based systems. The report expands on the foregoing. Section 1 discusses the architecture of a typical expert system. Section 2 deals with the characteristics that make a problem a suitable candidate for expert system solution. Section 3 surveys current technology, describing some of the software aids available for expert system development. Section 4 discusses the limitations of the latter. The concluding section makes predictions of future trends

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

From GenderMag to InclusiveMag: An Inclusive Design Meta-Method

How can software practitioners assess whether their software supports diverse users? Although there are empirical processes that can be used to find “inclusivity bugs” piecemeal, what is often needed is a systematic inspection method to assess software’s support for diverse populations. To help fill this gap, this paper introduces InclusiveMag, a generalization of GenderMag that can be used to generate systematic inclusiveness methods for a particular dimension of diversity. We then present a multicase study covering eight diversity dimensions, of eight teams’ experiences applying InclusiveMag to eight under-served populations and their “mainstream” counterparts

Three Heuristics for Writing and Revising Qualitative Research Articles in English Education

In this essay, I present three heuristics for writing and revising qualitative research articles in English education: “PAGE” (Purpose, Audience, Genre, Engagement), “Problem Posing, Problem Addressing, Problem Posing,” and “The Three INs” (INtroduction, INsertion, INterpretation). In so doing, I describe the rhetorical functions and conventional structure of all of the major sections of qualitative research articles, and show how the problem for study brings the rhetorical “jobs” of each section into purposive relationship with those of the other sections. Together, the three curricular resources that I offer in this essay prompt writers to connect general rhetorical concerns with specific writing moves and to approach qualitative research writing as a strategic art

Bio-inspired Factories of the Future

The biological transformation of added value is seen as one of the key aspects in applied research. Bio- inspired methods and technologies will affect factories of the future and enable them to cope with changing boundary conditions and the rising necessity of sustainability. This results in a higher demand for flexibility and transformation ability of the comprised production systems. To elaborate topics like these, Fraunhofer initiated strategic collaborative research projects. The current project aims at developing aspects of the biological transformation, whereof organic bio-inspired factories is one. Different research focal points were identified as enabling technologies on different levels of the well-established automation pyramid. The paper highlights the aspects “facility layout planning”, “behavioral modeling of production systems” and “skill-based controller programming” as enabling technologies. Solution approaches for the addressed aspects are discussed and future steps towards a flexible and sustainable production are shown

Automatically Produced Algorithms for the Generalized Minimum Spanning Tree Problem

The generalized minimum spanning tree problem consists of finding a minimum cost spanning tree in an undirected graph for which the vertices are divided into clusters. Such spanning tree includes only one vertex from each cluster. Despite the diverse practical applications for this problem, the NP-hardness continues to be a computational challenge. Good quality solutions for some instances of the problem have been found by combining specific heuristics or by including them within a metaheuristic. However studied combinations correspond to a subset of all possible combinations. In this study a technique based on a genotype-phenotype genetic algorithm to automatically construct new algorithms for the problem, which contain combinations of heuristics, is presented. The produced algorithms are competitive in terms of the quality of the solution obtained. This emerges from the comparison of the performance with problem-specific heuristics and with metaheuristic approaches