Inaccurate Metacognitive Monitoring and its Effects on Metacognitive Control and Task Outcomes in Self-Regulated L2 Learning

Accurate metacognitive monitoring of one’s own knowledge or performance is a precondition for self-regulated learning; monitoring informs metacognitive control, which in turn affects task outcomes. Studies of monitoring accuracy and its connection to knowledge and performance are common in psychology and educational research but rare in instructed SLA. This paper describes two studies in which actual performance and self-evaluated performance were compared. In Study 1, 64 college-level ESL learners completed L2 vocabulary tasks that differed in complexity and familiarity. Wide discrepancies in monitoring accuracy were observed. In Study 2, the same sample was divided into two groups, and the more complex task from Study 1 was used as a pre- and post-test. One group was given strategy instruction to improve monitoring accuracy. Metacognitive control was operationalized as choice of dictionaries used during the task, and effects of control on task outcomes were operationalized as points achieved on the task that could be attributed to use of a particular dictionary type. Results showed that control decisions leading to poorer task outcomes were associated with lower monitoring accuracy

Towards native higher-order remote procedure calls

We present a new abstract machine, called DCESH, which mod-els the execution of higher-order programs running in distributed architectures. DCESH implements a native general remote higher-order function call across node boundaries. It is a modernised ver-sion of SECD enriched with specialised communication features required for implementing the remote procedure call mechanism. The key correctness result is that the termination behaviour of the remote procedure call is indistinguishable (bisimilar) to that of a local call. The correctness proofs and the requisite definitions for DCESH and other related abstract machines are formalised using Agda. We also formalise a generic transactional mechanism for transparently handling failure in DCESHs. We use the DCESH as a target architecture for compiling a conventional call-by-value functional language ("Floskel") whic

Fully Observable Non-deterministic Planning as Assumption-Based Reactive Synthesis

We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard “fairness” assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special “structural” properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.Fil: D'ippolito, Nicolás Roque. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Rodriguez, Natalia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Sardina, Sebastian. RMIT University; Australi

Towards secure distributed computations

Nowadays, there are plenty of networks that work in a cooperative way and form what we know as grids of computers. These grids are serving a lot of purposes, and they are used with good results for intensive calculation, because the joined computing power aids in solving complex functions. To cope with these new requirements and facilities, programming languages had to evolve to new paradigms, including facilities to do distributed computing in a straightforward way. Functional programming is a paradigm that treats computation as the evaluation of mathematical functions. Functional programming languages implement the concepts introduced by this paradigm. Usually they are modeled using I» calculus, but other variants exist. In this line we have languages like ML, Haskell and (Pure)Lisp. This work has its focus on ML-like languages. As part of the evolution in grid computing, some functional programming languages were adapted to handle these new requirements. To be used in distributed contexts, the calculi had to be extended with new paradigms. Theoretic support for concurrent and distributed programming was conceived. For concurrent programming the I_ calculus was created, and this formalism was extended for mobility on the Ambient calculus. From these approaches, new functional languages were created. Examples of concurrent programming languages are Pict, occam-pi and Concurrent Haskell. In the case of distributed programming languages, we can mention Nomadic Pict, Alice and Acute. After the creation and utilization of such languages, an aspect remaining to be introduced is the security properties of these computations. The security properties of languages that execute on a single machine are difficult to achieve. Increased precautions must be take into account when dealing with lots of hosts and complex networks.Distributed programming languages must achieve, among other properties, correctness in its own abstractions: they must satisfy type-safety and abstraction safety. This work is concerned with correctness and safety in distributed languages, with focus on ML-like languages and the properties they have. To this aim, we have focused on a language called Acute. This language was born for doing research in distributed programming, and was created as a joint effort of the University of Cambridge and INRIA Rocquencourt. In Acute we have modern primitives for interaction between cooperating programs. Two primitives, marshal and unmarshal, have been introduced with this in mind. Acute has powerful properties: type and abstraction safety are guaranteed along the distributed system. But this only happens when there are no entities that can tamper with data transmitted between hosts. If this situation occurs, safety can be no longer guaranteed. The Acute language typechecks values at unmarshal time, to ensure its correctness. This can be made with values of concrete types, but if we have values of abstract types the situation os different: we may have only partial information to check, or the representation could be not available at all. So, how can values of abstract types be secured, in the context of a distributed programming language? We propose the use of a novel technique, called Proof Carrying Results. This technique is based on Neculaâ_Ts proof carrying code. Basically, the result of some computation comes equipped with a certificate, or witness, that can be used with abstract types.If the value comes with a witness that the computation was performed correctly, the caller can verify this witness and know that the value was generated in a good way. Throughout this thesis work, we will show how to add the PCR technique to a distributed programming language. The supporting infrastructure for the technique is introduced along with it. For checking the values and associated witnesses produced by some host, we use the COQ proof checker for a precise and reliable verification

Instance and Output Optimal Parallel Algorithms for Acyclic Joins

Massively parallel join algorithms have received much attention in recent years, while most prior work has focused on worst-optimal algorithms. However, the worst-case optimality of these join algorithms relies on hard instances having very large output sizes, which rarely appear in practice. A stronger notion of optimality is {\em output-optimal}, which requires an algorithm to be optimal within the class of all instances sharing the same input and output size. An even stronger optimality is {\em instance-optimal}, i.e., the algorithm is optimal on every single instance, but this may not always be achievable. In the traditional RAM model of computation, the classical Yannakakis algorithm is instance-optimal on any acyclic join. But in the massively parallel computation (MPC) model, the situation becomes much more complicated. We first show that for the class of r-hierarchical joins, instance-optimality can still be achieved in the MPC model. Then, we give a new MPC algorithm for an arbitrary acyclic join with load O ({\IN \over p} + {\sqrt{\IN \cdot \OUT} \over p}), where \IN,\OUT are the input and output sizes of the join, and $p$ is the number of servers in the MPC model. This improves the MPC version of the Yannakakis algorithm by an O (\sqrt{\OUT \over \IN} ) factor. Furthermore, we show that this is output-optimal when \OUT = O(p \cdot \IN), for every acyclic but non-r-hierarchical join. Finally, we give the first output-sensitive lower bound for the triangle join in the MPC model, showing that it is inherently more difficult than acyclic joins

Automating iterative tasks with programming by demonstration

Programming by demonstration is an end-user programming technique that allows people to create programs by showing the computer examples of what they want to do. Users do not need specialised programming skills. Instead, they instruct the computer by demonstrating examples, much as they might show another person how to do the task. Programming by demonstration empowers users to create programs that perform tedious and time-consuming computer chores. However, it is not in widespread use, and is instead confined to research applications that end users never see. This makes it difficult to evaluate programming by demonstration tools and techniques. This thesis claims that domain-independent programming by demonstration can be made available in existing applications and used to automate iterative tasks by end users. It is supported by Familiar, a domain-independent, AppleScript-based programming-by-demonstration tool embodying standard machine learning algorithms. Familiar is designed for end users, so works in the existing applications that they regularly use. The assertion that programming by demonstration can be made available in existing applications is validated by identifying the relevant platform requirements and a range of platforms that meet them. A detailed scrutiny of AppleScript highlights problems with the architecture and with many implementations, and yields a set of guidelines for designing applications that support programming-by-demonstration. An evaluation shows that end users are capable of using programming by demonstration to automate iterative tasks. However, the subjects tended to prefer other tools, choosing Familiar only when the alternatives were unsuitable or unavailable. Familiar's inferencing is evaluated on an extensive set of examples, highlighting the tasks it can perform and the functionality it requires

Set based failure diagnosis for concurrent constraint programming

Oz is a recent high-level programming language, based on an extension of the concurrent constraint model by higher-order procedures and state. Oz is a dynamically typed language like Prolog, Scheme, or Smalltalk. We investigate two approaches of making static type analysis available for Oz: Set-based failure diagnosis and strong typing. We define a new system of set constraints over feature trees that is appropriate for the analysis of record structures, and we investigate its satisfiability, emptiness, and entailment problem. We present a set-based diagnosis for constraint logic programming and concurrent constraint programming as first-order fragments of Oz, and we prove that it correctly detects inevitable run-time errors. We also propose an analysis for a larger sublanguage of Oz. Complementarily, we define an Oz-style language called Plain that allows an expressive strong type system. We present such a type system and prove its soundness.Oz ist eine anwendungsnahe Programmiersprache, deren Grundlage eine Erweiterung des Modells nebenläufiger Constraintprogrammierung um Prozeduren höherer Stufe und Zustand ist. Oz ist eine Sprache mit dynamischer Typüberprüfung wie Prolog, Scheme oder Smalltalk. Wir untersuchen zwei Ansätze, statische Typüberprüfung für Oz zu ermöglichen: Mengenbasierte Fehlerdiagnose und Starke Typisierung. Wir definieren ein neues System von Mengenconstraints über Featurebäumen, das für die Analyse von Recordstrukturen geeignet ist, und wir untersuchen das Erfüllbarkeits-, das Leerheits- und das Subsumtionsproblem für dieses Constraintsystem. Wir präsentieren eine mengenbasierte Diagnose für Constraint-Logikprogrammierung und für nebenläufige Constraintprogrammierung als Teilsprachen von Oz, und wir beweisen, daß diese unvermeidliche Laufzeitfehler erkennt. Wir schlagen auch eine mengenbasierte Analyse für eine grössere Teilsprache von Oz vor. Komplementär dazu definieren wir eine Oz-artige Sprache genannt Plain, die ein expressives starkes Typsystem erlaubt. Wir stellen ein solches Typsystem vor und beweisen seine Korrektheit