Achieving a Sequenced, Relational Query Language with Log-Segmented Timestamps

In a relational temporal database, typically each row of each table has a period timestamp to indicate the lifetime of that row. In order to evaluate a query in a temporal database, sequenced semantics comes into play. The semantics stipulates that the query must be evaluated simultaneously in each time instant using the data rows available at that point of time. Existing researches have proposed changes in the query evaluation engine to achieve sequenced semantics. In this paper we show a way to support sequenced semantics without modifying the query engine. We propose a noble construction log-segmented label to represent the lifetime and replace the period timestamp from each row with a log-segmented label that signifies when the tuple is alive. Then we translate a sequenced query to a non-temporal query by utilizing the properties of log-segmented label. The translated query has only operations already available in a typical relational database making the query readily executable in an unaltered installation of the database. Thus the sequenced query inevitably runs and retrieve data without changing query evaluation engine. Finally our implementation using Java language, ANTLR parser generator and PostgreSQL database demonstrates the feasibility of the proposed mechanism, which, to the best of our knowledge, has not been previously shown

Variational recurrent sequence-to-sequence retrieval for stepwise illustration

We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query. This new task extends the traditional cross-modal retrieval, where each image-text pair is treated independently ignoring broader context. We propose a novel variational recurrent seq2seq (VRSS) retrieval model for this seq2seq task. Unlike most cross-modal methods, we generate an image vector corresponding to the latent topic obtained from combining the text semantics and context. This synthetic image embedding point associated with every text embedding point can then be employed for either image generation or image retrieval as desired. We evaluate the model for the application of stepwise illustration of recipes, where a sequence of relevant images are retrieved to best match the steps described in the text. To this end, we build and release a new Stepwise Recipe dataset for research purposes, containing 10K recipes (sequences of image-text pairs) having a total of 67K image-text pairs. To our knowledge, it is the first publicly available dataset to offer rich semantic descriptions in a focused category such as food or recipes. Our model is shown to outperform several competitive and relevant baselines in the experiments. We also provide qualitative analysis of how semantically meaningful the results produced by our model are through human evaluation and comparison with relevant existing methods

Choreography automata

Automata models are well-established in many areas of computer science and are supported by a wealth of theoretical results including a wide range of algorithms and techniques to specify and analyse systems. We introduce choreography automata for the choreographic modelling of communicating systems. The projection of a choreography automaton yields a system of communicating finite-state machines. We consider both the standard asynchronous semantics of communicating systems and a synchronous variant of it. For both, the projections of well-formed automata are proved to be live as well as lock- and deadlock-free

Interval-based temporal functional dependencies: specification and verification

In the temporal database literature, every fact stored in a database may beequipped with two temporal dimensions: the valid time, which describes the time whenthe fact is true in the modeled reality, and the transaction time, which describes the timewhen the fact is current in the database and can be retrieved. Temporal functional dependencies(TFDs) add valid time to classical functional dependencies (FDs) in order to expressdatabase integrity constraints over the flow of time. Currently, proposals dealing with TFDsadopt a point-based approach, where tuples hold at specific time points, to express integrityconstraints such as \u201cfor each month, the salary of an employee depends only on his role\u201d. Tothe best of our knowledge, there are no proposals dealing with interval-based temporal functionaldependencies (ITFDs), where the associated valid time is represented by an intervaland there is the need of representing both point-based and interval-based data dependencies.In this paper, we propose ITFDs based on Allen\u2019s interval relations and discuss theirexpressive power with respect to other TFDs proposed in the literature: ITFDs allow us toexpress interval-based data dependencies, which cannot be expressed through the existingpoint-based TFDs. ITFDs allow one to express constraints such as \u201cemployees starting towork the same day with the same role get the same salary\u201d or \u201cemployees with a given roleworking on a project cannot start to work with the same role on another project that willend before the first one\u201d. Furthermore, we propose new algorithms based on B-trees to efficientlyverify the satisfaction of ITFDs in a temporal database. These algorithms guaranteethat, starting from a relation satisfying a set of ITFDs, the updated relation still satisfies thegiven ITFDs

Translating Temporal SQL to Nested SQL

Sequenced and nonsequenced semantics are the two previously researched semantics for the evaluation of an operation in a temporal database such as a query or data modification. Sequenced semantics evaluates an operation in each time instant using only the data alive at that time. Nonsequenced semantics, in contrast, means that an operation explicitly references and manipulates the timestamps in the data. In this thesis we propose a new framework that shows both semantics are variants of a general temporal semantics. We present the general semantics and show how additional semantics, such as preceding semantics can be realized. The semantics are specified using annotations. The primary contribution of this theses is the translation from temporal SQL to nested SQL. We focus on SQL\u27s SELECT statement, which is used to query data. Temporal SQL is SQL annotated with temporal semantics. Nested SQL is SQL for non-1NF data, with additional operations, such as COGROUP and FLATTEN to create and un-nest, respectively, bags of tuples (non-1NF data). This thesis develops a denotational semantics for translating from temporal to nested SQL. We implemented the denotational semantics for an SQLite ANTLR grammar, and the thesis also reports on the implementation

Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis

Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI) and Autism Spectrum Disorder (ASD). Using metaanalysis, we examined statistical learning in SLI (14 studies, 15 comparisons) and ASD (13 studies, 20 comparisons) to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, and Probabilistic Classification). Individuals with SLI showed deficits in statistical learning relative to age-matched controls. In contrast, statistical learning was intact in individuals with ASD relative to controls. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping socialcommunicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman and Pierpont, 2005), impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD