14,146 research outputs found
From ACT-ONE to Miranda, a Translation Experiment
No full textIt is now almost universally acknowledged that the data language ACT-ONE associated with the formal description technique LOTOS is inappropriate for the purpose of OSI formal description. In response to this the LOTOS restandardisation activity plans to replace ACT-ONE with a functional language. Thus, compatibility between ACT-ONE and the replacement data language becomes an issue. In response to this, we present an experimental investigation of backward compatibility between ACT-ONE and the new LOTOS data language. Specifically, we investigate translating ACT-ONE data types into the functional language Miranda. Miranda has been chosen as it is a widely used functional programming language and it is close in form to the anticipated new data language. This work serves as a ``verification of concept'' for translating ACT-ONE to the E-LOTOS data language. It identifies the bounds on embedding ACT-ONE in a functional data language. In particular, it indicates what can be translated and what cannot be translated. In addition, the paper reveals pertinent issues which can inform the E-LOTOS work. For example, which constructs are needed in E-LOTOS in order to support the class of data type specifications typically made in the LOTOS setting? We conclude with a number of specific recommendations for the E-LOTOS data language
Miranda in Isabelle
No full textThis paper describes our experience in formalising arguments about the Miranda functional programming language in Isabelle. After explaining some of the problems of reasoning about Miranda, we explain our two different approaches to encoding Miranda in Isabelle. We conclude by discussing some shorter examples and a case study of reasoning about hardware. Miranda 1 [Turner, 1990, Thompson, 1995b] is a modern functional programming language, allowing type polymorphism and higher-order functions in a similar way to ML[Milner et al., 1990]. It differs from ML in being lazy --- arguments to functions are only evaluated when and to the extent that they are needed --- and in being side-effect free. It has long been an article of faith in the functional programming community that languages like this are ideal candidates for program verification because of their `declarative' nature. This is clearly true for idealised languages, but real languages like Miranda bring their own complexities wh..
Metadata Representations for Queryable ML Model Zoos
No full textMachine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc
A Manifesto of Nodalism
No full textThis paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music
Optimizing ML Inference Queries Under Constraints
No full textThe proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
Structure-activity relationship study of the antimicrobial fragment 33-61 of bovine hemoglobin
No full textHigher-order + Polymorphic = Reusable
No full textThis paper explores how certain ideas in object oriented languages have their correspondents in functional languages. In particular we look at the analogue of the iterators of the C++ standard template library. We also give an example of the use of constructor classes which feature in Haskell 1.3 and Gofer
Building a generalisable ML pipeline at ING
No full textAdvances in data science have caused an increase in the use of Artificial Intelligence (AI), specifically Machine Learning (ML), throughout various fields. Not only in research but in the industry as well, has ML been receiving increasing amounts of interest. Many companies rely on ML models to increase the efficiency of existing processes or offer new services and products. The industry, however, is facing several additional challenges compared to the academic context. One of those challenges is applying the Development Operations (DevOps) model to an ML application, also referred to as MLOps. This thesis sets out to find the specific challenges that practitioners encounter while operationalising ML models. To do so, we perform a single-case case study on an ML pipeline built by the Trade & Communication Surveillance team at the ING bank. This case study consists of conducting a set of interviews and performing a manual code inspection of the pipeline. The team faces challenges ranging from having insufficient time for operationalising each ML project individually to operating in the highlyregulated fintech context. Their pipeline is able to deploy a single ML model but it does not generalise well to other projects. We present the first version of an application that mitigates these challenges. The application is able to deploy ML models to the development environment at ING and can be operated by data scientists to reduce the effort of operationalising an ML model. Computer Science | Software Technolog
Sampling Chemical Space: Activity Cliffs, Extended Similarity, and ML Performance
No full textThe presence of Activity Cliffs (ACs) has been known to represent a challenge for QSAR modeling. With its data high dependency, Machine Learning QSAR models will be highly influenced by the activity landscape of the data. We propose several extended similarity and extended SALI methods to study the implications of ACs distribution on the training and test sets on the model’s errors. Non-uniform ACs and chemical space distribution tends to lead to worse models than the proposed uniform methods. ML modeling on AC-rich sets needs to be analyzed case-by-case. Proposed methods can be used as a tool to study the dataset, with random and uniform splitting being the better overall data splitting alternatives
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