A Unified Format for Language Documents

We have analyzed a substantial number of language documentation artifacts, including language standards, language specifications, language reference manuals, as well as internal documents of standardization bodies. We have reverse-engineered their intended internal structure, and compared the results. The Language Document Format (LDF), was developed to specifically support the documentation domain. We have also integrated LDF into an engineering discipline for language documents including tool support, for example, for rendering language documents, extracting grammars and samples, and migrating existing documents into LDF. The definition of LDF, tool support for LDF, and LDF applications are freely available through SourceForge

LEMP : a language engineering model-driven process

In this paper, we propose LEMP as a model-driven process to develop a language endowed with a set of derived artifacts (syntax, interchange format, APIs, ...) and with a well defined formal semantics. The process exploits the Model Driven Engineering principles of metamodeling, model transformation and automatic generation of language processing tools. We describe the requirements to fulfill and the development steps of this language engineering life cycle, including the validation activities regarding the syntactic and semantic aspects. As a proof-of-concepts, we apply LEMP to the Finite State Machines and we report our experience in developing a language for the Abstract State Machine formal method

On the Limitations of Simulating Active Learning

Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects informative unlabeled data for human annotation, aiming to improve over random sampling. However, performing AL experiments with human annotations on-the-fly is a laborious and expensive process, thus unrealistic for academic research. An easy fix to this impediment is to simulate AL, by treating an already labeled and publicly available dataset as the pool of unlabeled data. In this position paper, we first survey recent literature and highlight the challenges across all different steps within the AL loop. We further unveil neglected caveats in the experimental setup that can significantly affect the quality of AL research. We continue with an exploration of how the simulation setting can govern empirical findings, arguing that it might be one of the answers behind the ever posed question ``why do active learning algorithms sometimes fail to outperform random sampling?''. We argue that evaluating AL algorithms on available labeled datasets might provide a lower bound as to their effectiveness in real data. We believe it is essential to collectively shape the best practices for AL research, particularly as engineering advancements in LLMs push the research focus towards data-driven approaches (e.g., data efficiency, alignment, fairness). In light of this, we have developed guidelines for future work. Our aim is to draw attention to these limitations within the community, in the hope of finding ways to address them.Comment: To appear at Findings of ACL 202

Application of design science research to design a modelling approach for procurement of infrastructure systems

© 2019 IEEE. Model-driven approaches are widely used in managing the complex domains such as infrastructure systems or disaster management. The foundation of conducting a systematic research is designing a methodology that pertinently covers the steps of research from problem definition to solution proposal and then identifying or tailoring a method for developing and validating the solution. This paper explains the application of Design Science for conducting a research which aims at providing a model-driven approach for addressing the complexities of infrastructure procurement projects. So firstly the design science artefacts are adopted for designing the method for this research. Then the steps of this method are explained briefly along with description of how each step is applied in this research. The core of this method is proposing a process for developing and validating the metamodels which is designed based on combination of other metamodeling processes

Preliminaries of a Space Situational Awareness Ontology

Space situational awareness (SSA) is vital for international safety and security, and for the future of space travel. The sharing of SSA data and information should improve the state of global SSA for planetary defense and spaceflight safety. I take steps toward a Space Situational Awareness (SSA) Ontology, and outline some central objectives, requirements and desiderata in the ontology development process for this domain. The purpose of this ontological system is to explore the potential for the ontology research topic to (i) represent SSA general knowledge, data, and entities/objects, (ii) clearly express the meaning of SSA data, and (iii) foster SSA data-sharing. The overall goal and motivation is to (iv) improve our capacity for planetary defense, e.g., from near- or deep-space objects and phenomena, and (v) facilitate safer and peaceful space access, navigation and travel, by improving global SSA. This research is thereby intended only for peaceful space-domain applications and uses, with particular interests in orbital debris. There is little application of ontology to the space domain as compared with other disciplines and little if any ontological development of SSA and related domains. In this respect, this paper offers novel concepts