Computing and computers are introduced in school as important examples of technology, sometimes as a subject matter of their own, and sometimes they are used as tools for other subjects. All in all, one might even say...
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Computing and computers are introduced in school as important examples of technology, sometimes as a subject matter of their own, and sometimes they are used as tools for other subjects. All in all, one might even say that learning about computing and computers is part of learning about technology. Lately, many countries have implemented programming in their curricula as a means to address society's dependence on, and need for programming knowledge and code. programming is a fairly new school subject without educational traditions and, due to the rapid technological development, in constant change. This means that most programming teachers must decide for themselves what and how to teach. In this study, programming teachers' teaching is studied. With the aim of exploring the connection/possible gap between teacher's intentions and the teacher's instructional practice, an expansion of the conceptual apparatus of phenomenography and variation theory is tested. In the article, phenomenography and variation theory and the suggested supplementary theoretical tool (Georg Henrik von Wright's model of logic of events) are briefly presented and then deployed upon one selected case. Findings reveal that teachers' intentions (reflected in their actions) include an emphasis (of teachers' side) on the importance of balancing theory and practice, using different learning strategies, encouraging learning by trial-and-error and fostering collaboration between students for a deeper understanding of concepts. In conclusion, logic of events interpretations proves to be useful as a complementary tool to the conceptual apparatus of phenomenography.
This paper describes an approach to the methodology of answer set programming that can facilitate the design of encodings that are easy to understand and provably correct. Under this approach, after appending a rule o...
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This paper describes an approach to the methodology of answer set programming that can facilitate the design of encodings that are easy to understand and provably correct. Under this approach, after appending a rule or a small group of rules to the emerging program, we include a comment that states what has been "achieved" so far. This strategy allows us to set out our understanding of the design of the program by describing the roles of small parts of the program in a mathematically precise way.
We present SLR, the first expressive program logic for reasoning about concurrent programs under a weak memory model addressing the out-of-thin-air problem. Our logic includes the standard features from existing logic...
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The LLL basis reduction algorithm was the first polynomial-time algorithm to compute a reduced basis of a given lattice, and hence also a short vector in the lattice. It thereby approximately solves an NP-hard problem...
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Answer set programming (ASP) is a well-established declarative paradigm. One of the successes of ASP is the availability of efficient systems. State-of-the-art systems are based on the ground+solve approach. In some a...
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Answer set programming (ASP) is a well-established declarative paradigm. One of the successes of ASP is the availability of efficient systems. State-of-the-art systems are based on the ground+solve approach. In some applications, this approach is infeasible because the grounding of one or a few constraints is expensive. In this paper, we systematically compare alternative strategies to avoid the instantiation of problematic constraints, which are based on custom extensions of the solver. Results on real and synthetic benchmarks highlight some strengths and weaknesses of the different strategies.
In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to corresp...
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In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. Therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant improvement over traditional approaches based on inductive logicprogramming. Under consideration for acceptance in TPLP.
Processing programs as data is one of the successes of functional and logicprogramming. Higher-order functions, as program-processing programs are called in functional programming, and meta-programs, as they are call...
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We present Ellora, a sound and relatively complete assertion-based program logic, and demonstrate its expressivity by verifying several classical examples of randomized algorithms using an implementation in the EasyCr...
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In this paper, we propose an extension of logicprogramming where each default literal derived from the well-founded model is associated to a justification represented as an algebraic expression. This expression conta...
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In this paper, we propose an extension of logicprogramming where each default literal derived from the well-founded model is associated to a justification represented as an algebraic expression. This expression contains both causal explanations (in the form of proof graphs built with rule labels) and terms under the scope of negation that stand for conditions that enable or disable the application of causal rules. Using some examples, we discuss how these new conditions, we respectively call enablers and inhibitors, are intimately related to default negation and have an essentially different nature from regular cause-effect relations. The most important result is a formal comparison to the recent algebraic approaches for justifications in logicprogramming: Why-not Provenance and Causal Graphs. We show that the current approach extends both Why-not Provenance and Causal Graphs justifications under the well-founded semantics and, as a byproduct, we also establish a formal relation between these two approaches.
M. Bezem defined an extensional semantics for positive higher-order logic programs. Recently, it was demonstrated by Rondogiannis and Symeonidou that Bezem's technique can be extended to higher-order logic program...
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M. Bezem defined an extensional semantics for positive higher-order logic programs. Recently, it was demonstrated by Rondogiannis and Symeonidou that Bezem's technique can be extended to higher-order logic programs with negation, retaining its extensional properties, provided that it is interpreted under a logic with an infinite number of truth values. Rondogiannis and Symeonidou also demonstrated that Bezem's technique, when extended under the stable model semantics, does not in general lead to extensional stable models. In this paper, we consider the problem of extending Bezem's technique under the well-founded semantics. We demonstrate that the well-founded extension fails to retain extensionality in the general case. On the positive side, we demonstrate that for stratified higher-order logic programs, extensionality is indeed achieved. We analyze the reasons of the failure of extensionality in the general case, arguing that a three-valued setting cannot distinguish between certain predicates that appear to have a different behaviour inside a program context, but which happen to be identical as three-valued relations.
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