Logical relations for coherence of effect subtyping

A coercion semantics of a programming language with subtyping is typically defined on typing derivations rather than on typing judgments. To avoid semantic ambiguity, such a semantics is expected to be coherent, i.e., independent of the typing derivation for a given typing judgment. In this article we present heterogeneous, biorthogonal, step-indexed logical relations for establishing the coherence of coercion semantics of programming languages with subtyping. To illustrate the effectiveness of the proof method, we develop a proof of coherence of a type-directed, selective CPS translation from a typed call-by-value lambda calculus with delimited continuations and control-effect subtyping. The article is accompanied by a Coq formalization that relies on a novel shallow embedding of a logic for reasoning about step-indexing

Conditional Random Field Autoencoders for Unsupervised Structured Prediction

We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observable data using a feature-rich conditional random field. Then a reconstruction of the input is (re)generated, conditional on the latent structure, using models for which maximum likelihood estimation has a closed-form. Our autoencoder formulation enables efficient learning without making unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate insightful connections to traditional autoencoders, posterior regularization and multi-view learning. We show competitive results with instantiations of the model for two canonical NLP tasks: part-of-speech induction and bitext word alignment, and show that training our model can be substantially more efficient than comparable feature-rich baselines

Evaluation of graphical control flow management approaches for Event-B modelling

Integrating graphical representations with formal methods can help bridge the gap between requirements and formal modelling. In this paper, we compare and evaluate two graphical approaches aiming at describing control flows and refinement in Event-B, and we use a fire dispatch system case study to perform this evaluation. The fire dispatch system case study provides a good example of a complex workflow through which we try to identify a process that facilitates defining the structural and the behavioural parts of the Event-B model. In our case study, we focus on building the dynamic part of the model to evaluate the two diagrammatic notations: UML Activity Diagrams and Atomicity Decomposition Diagrams. Based on our evaluation, we try to identify the advantages and limitations of both approaches. Finally, we try to compare how both graphical notations can affect the Event-B formal modelling of our case study

19the Analysis of Students\u27 Team Achievement Divisions (Stad) Used in Learning Practice of Translating and Interpreting

Due to the Motto of STKIP Siliwangi Bandung “ The Leader of Learning Innovation”, this research deals with The Analysis of Student Teams Achievement Division (STAD) used in Learning Practice of Translating and Interpreting. This research explores the implementation of Students\u27 Team Achievement Divisions (STAD) and find out the advantages and disadvantages of Students\u27 Team Achievement Divisions (STAD) used in learning Practice of Translating and Interpreting. The objective of the research was to motivate students and encourage them to be active in learning, to accelerate student achievement, to improve behavior in learning, and to find out the students\u27 ability with Student Teams-Achievement Divisions (STAD) method. Data collection technique focused on participant observation, interviews, and documentation. Student Team-Achievement Division (STAD) is one type of cooperative learning model using small groups with a number of members of each group of 4-5 students in heterogenic way. It begins by delivering the objectives of learning, delivering of material, group activities, quizzes and group rewards. Students\u27 Team Achievement Divisions (STAD) method also is an effective method of cooperative learning. As with other learning methods, STAD method also has advantages and disadvantages. In the learning process there are good interaction among students, good attitude, increased interpersonal skills. It\u27s effective in increasing student participation and can train students to be more focus, more concentrate in answering questions from the teacher. It can make students eager to learn. But if the chief of the group can not resolve conflicts that arise constructively, it will be less effective in a group work. And if the number of groups is not considered, that is less than four, it would tend to withdraw and less active during the discussion. And if the number of groups of more than five, then chances for them to be passive in task completio

On relating CTL to Datalog

CTL is the dominant temporal specification language in practice mainly due to the fact that it admits model checking in linear time. Logic programming and the database query language Datalog are often used as an implementation platform for logic languages. In this paper we present the exact relation between CTL and Datalog and moreover we build on this relation and known efficient algorithms for CTL to obtain efficient algorithms for fragments of stratified Datalog. The contributions of this paper are: a) We embed CTL into STD which is a proper fragment of stratified Datalog. Moreover we show that STD expresses exactly CTL -- we prove that by embedding STD into CTL. Both embeddings are linear. b) CTL can also be embedded to fragments of Datalog without negation. We define a fragment of Datalog with the successor build-in predicate that we call TDS and we embed CTL into TDS in linear time. We build on the above relations to answer open problems of stratified Datalog. We prove that query evaluation is linear and that containment and satisfiability problems are both decidable. The results presented in this paper are the first for fragments of stratified Datalog that are more general than those containing only unary EDBs.Comment: 34 pages, 1 figure (file .eps

Combining link and content-based information in a Bayesian inference model for entity search

An architectural model of a Bayesian inference network to support entity search in semantic knowledge bases is presented. The model supports the explicit combination of primitive data type and object-level semantics under a single computational framework. A flexible query model is supported capable to reason with the availability of simple semantics in querie

Evaluating Datalog via Tree Automata and Cycluits

We investigate parameterizations of both database instances and queries that make query evaluation fixed-parameter tractable in combined complexity. We show that clique-frontier-guarded Datalog with stratified negation (CFG-Datalog) enjoys bilinear-time evaluation on structures of bounded treewidth for programs of bounded rule size. Such programs capture in particular conjunctive queries with simplicial decompositions of bounded width, guarded negation fragment queries of bounded CQ-rank, or two-way regular path queries. Our result is shown by translating to alternating two-way automata, whose semantics is defined via cyclic provenance circuits (cycluits) that can be tractably evaluated.Comment: 56 pages, 63 references. Journal version of "Combined Tractability of Query Evaluation via Tree Automata and Cycluits (Extended Version)" at arXiv:1612.04203. Up to the stylesheet, page/environment numbering, and possible minor publisher-induced changes, this is the exact content of the journal paper that will appear in Theory of Computing Systems. Update wrt version 1: latest reviewer feedbac