Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.

Self-organization in multi agent systems: a middleware approach

Abstract: Self-organization in multi agent systems requires two main building blocks: adaptive and uncoupled interaction mechanisms and context-awareness. Here we show how the middleware TOTA (Tuples On The Air) supports self-organization by providing effective abstractions for the above two building-blocks. TOTA relies on spatially distributed tuples for both supporting adaptive and uncoupled interactions between agents, and context-awareness. Agents can inject these tuples in the network, to make available some kind of contextual information and to interact with other agents. Tuples are propagated by the middleware, on the basis of application specific patterns, defining sorts of "computational fields", and their intended shape is maintained despite network dynamics, such as topological reconfigurations. Agents can locally "sense" these fields and rely on them for both acquiring contextual information and carrying on distributed self-organizing coordination activities. Several application examples in different scenarios show the effectiveness of our approach.

A Dataset for Tracking Entities in Open Domain Procedural Text

Abstract: We present the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. For example, in a text describing fog removal using potatoes, a car window may transition between being foggy, sticky, opaque, and clear. Previous formulations of this task provide the text and entities involved, and ask how those entities change for just a small, pre-defined set of attributes (e.g., location), limiting their fidelity. Our solution is a new task formulation where given just a procedural text as input, the task is to generate a set of state change tuples (entity, attribute, before-state, after-state) for each step, where the entity, attribute, and state values must be predicted from an open vocabulary. Using crowdsourcing, we create OPENPI, a high-quality (91.5% coverage as judged by humans and completely vetted), and large-scale dataset comprising 29,928 state changes over 4,050 sentences from 810 procedural real-world paragraphs from WikiHow.com. A current state-of-the-art generation model on this task achieves 16.1% F1 based on BLEU metric, leaving enough room for novel model architectures.

A novel keyword search paradigm in relational databases: Object summaries

Abstract: This paper introduces a novel keyword search paradigm in relational databases, where the result of a search is an Object Summary (OS). An OS summarizes all data held about a particular Data Subject (DS) in a database. More precisely, it is a tree with a tuple containing the keyword(s) as a root and neighboring tuples as children. In contrast to traditional relational keyword search, an OS comprises a more complete and therefore semantically meaningful set of information about the enquired DS. The proposed paradigm introduces the concept of Affinity in order to automatically generate OSs. More precisely, it investigates and quantifies the Affinity of relations (i.e. Affinity) and their attributes (i.e. Attribute Affinity) in order to decide which tuples and attributes to include in the OS. Experimental evaluation on the TPC-H and Northwind databases verifies the searching quality of the proposed paradigm on both large and small databases; precision, recall, f-score, CPU and space measures are presented.

The Role of "Condition": A Novel Scientific Knowledge Graph Representation and Construction Model

Abstract: Conditions play an essential role in scientific observations, hypotheses, and statements. Unfortunately, existing scientific knowledge graphs (SciKGs) represent factual knowledge as a flat relational network of concepts, as same as the KGs in general domain, without considering the conditions of the facts being valid, which loses important contexts for inference and exploration. In this work, we propose a novel representation of SciKG, which has three layers. The first layer has concept nodes, attribute nodes, as well as the attaching links from attribute to concept. The second layer represents both fact tuples and condition tuples. Each tuple is a node of the relation name, connecting to the subject and object that are concept or attribute nodes in the first layer. The third layer has nodes of statement sentences traceable to the original paper and authors. Each statement node connects to a set of fact tuples and/or condition tuples in the second layer. We design a semi-supervised Multi-Input Multi-Output sequence labeling model that learns complex dependencies between the sequence tags from multiple signals and generates output sequences for fact and condition tuples. It has a self-training module of multiple strategies to leverage the massive scientific data for better performance when manual annotation is limited. Experiments on a data set of 141M sentences show that our model outperforms existing methods and the SciKGs we constructed provide a good understanding of the scientific statements.

Universal test generation using fault tuples

Abstract: A test generation tool for combinational circuits called FATGEN has been developed based on the notion of fault tuples. FATGEN can be used to simultaneously generate tests for many types of misbehavior that occur in digital systems. Individual experiments involving SSL, transistor stuck-open, path delay and bridging faults for the ISCAS85 benchmark circuits reveal an average speedup of nearly 32% and test set compaction of 60% when faults of all types are analyzed simultaneously. In addition, there is an average reduction of approximately 34% in the number of aborted faults.