On Yao's method of translation

Machine Translation, i.e., translating one kind of natural language to another kind of natural language by using a computer system, is a very important research branch in Artificial Intelligence. Yao developed a method of translation that he called ``Lexical-Semantic Driven". In his system he introduced 49 ``relation types" including case relations, event relations, semantic relations, and complex relations. The knowledge graph method is a new kind of method to represent an interlingua between natural languages. In this paper, we will give a comparison of these two methods. We will translate one Chinese sentence cited in Yao�s book by using these two methods. Finally, we will use the relations in knowledge graph theory to represent the ``relations" in Lexical-Semantic Driven, and partition the relations in Lexical-Semantic Driven into groups according to the relations in knowledge graph theory

Steps Towards a Method for the Formal Modeling of Dynamic Objects

Fragments of a method to formally specify object-oriented models of a universe of discourse are presented. The task of finding such models is divided into three subtasks, object classification, event specification, and the specification of the life cycle of an object. Each of these subtasks is further subdivided, and for each of the subtasks heuristics are given that can aid the analyst in deciding how to represent a particular aspect of the real world. The main sources of inspiration are Jackson System Development, algebraic specification of data- and object types, and algebraic specification of processes

A Dataflow Language for Decentralised Orchestration of Web Service Workflows

Orchestrating centralised service-oriented workflows presents significant scalability challenges that include: the consumption of network bandwidth, degradation of performance, and single points of failure. This paper presents a high-level dataflow specification language that attempts to address these scalability challenges. This language provides simple abstractions for orchestrating large-scale web service workflows, and separates between the workflow logic and its execution. It is based on a data-driven model that permits parallelism to improve the workflow performance. We provide a decentralised architecture that allows the computation logic to be moved "closer" to services involved in the workflow. This is achieved through partitioning the workflow specification into smaller fragments that may be sent to remote orchestration services for execution. The orchestration services rely on proxies that exploit connectivity to services in the workflow. These proxies perform service invocations and compositions on behalf of the orchestration services, and carry out data collection, retrieval, and mediation tasks. The evaluation of our architecture implementation concludes that our decentralised approach reduces the execution time of workflows, and scales accordingly with the increasing size of data sets.Comment: To appear in Proceedings of the IEEE 2013 7th International Workshop on Scientific Workflows, in conjunction with IEEE SERVICES 201

Doing-it-All with Bounded Work and Communication

We consider the Do-All problem, where $p$ cooperating processors need to complete $t$ similar and independent tasks in an adversarial setting. Here we deal with a synchronous message passing system with processors that are subject to crash failures. Efficiency of algorithms in this setting is measured in terms of work complexity (also known as total available processor steps) and communication complexity (total number of point-to-point messages). When work and communication are considered to be comparable resources, then the overall efficiency is meaningfully expressed in terms of effort defined as work + communication. We develop and analyze a constructive algorithm that has work $O( t + p \log p\, (\sqrt{p\log p}+\sqrt{t\log t}\, ) )$ and a nonconstructive algorithm that has work $O(t +p \log^2 p)$. The latter result is close to the lower bound $\Omega(t + p \log p/ \log \log p)$ on work. The effort of each of these algorithms is proportional to its work when the number of crashes is bounded above by $c\,p$, for some positive constant $c < 1$. We also present a nonconstructive algorithm that has effort $O(t + p ^{1.77})$