6,692 research outputs found
Private James Dem(m)ing leave of absence
No full textDocument written by Colonel Hiram Du Puy granting Private James Dem(m)ing of the 8th Ohio Volunteer Infantry a leave of absence from Camp Dennison, June 1861
Author Co-Citation Analysis (ACA): a powerful tool for representing implicit knowledge of scholar knowledge workers
No full textIn the last decade, knowledge has emerged as one of the most important and valuable organizational assets. Gradually this importance caused to emergence of new discipline entitled ―knowledge management‖. However one of the major challenges of knowledge management is conversion implicit or tacit knowledge to explicit knowledge. Thus Making knowledge visible so that it can be better accessed, discussed, valued or generally managed is a long-standing objective in knowledge management. Accordingly in this paper author co- citation analysis (ACA) will be proposed as an efficient technique of knowledge visualization in academia (Scholar knowledge workers)
An encoding framework for binarized images using hyperdimensional computing
No full textThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Flemish Government under the "Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen" program
Anglicisms Ending in -ing in Russian and Slovak Languages
No full textThis article focuses on the functioning of Anglicisms ending in -ing in the Russian and Slovak languages, pseudo-Anglicisms, and the reasons for the active borrowing of such words at present, as well as the controversy between linguists, historians, and politicians about the role and place of borrowing in a historical context. This study aims to trace the dynamics of Anglicisms in the contemporary Russian and Slovak languages via the descriptive and comparative methods. The quantitative estimation method is also used in this work.
The study examines the main points of view on the issues of borrowing in Russian and Slovak linguistics: positive, negative and neutral. The focus is on the formal marker -ing and its word-building potential, which still causes controversy among linguists. One of the aims of the study is to identify the most frequent lexical groups of words with the suffix -ing. As a result of the study, the author makes a conclusion that the Russian and the Slovak languages codify words with this formal marker in different ways. Some words, recorded in the Slovak dictionaries, are found in Russian on the periphery of the lexicon. The author believes that the invasion of Anglicisms ending in -ing is just a passing trend. Most of these words are easily replaced by the native language analogues. On the other hand, some anglicisms describe this or that notion/process more accurately, significantly saving space. Certain research interest is aroused by pseudo-Anglicisms illustrating authored poetic works with marked expressive evaluation. It can be assumed that the number of Anglicisms with the suffix -ing might increase every year, since they reflect the intensification of socio-economic processes in society
Data-Driven Extract Method Recommendations: A Study at ING
No full textThe sound identification of refactoring opportunities is still an open problem in software engineering. Recent studies have shown the effectiveness of machine learning models in recommending methods that should undergo different refactoring operations. In this work, we experiment with such approaches to identify methods that should undergo an Extract Method refactoring, in the context of ING, a large financial organization. More specifically, we (i) compare the code metrics distributions, which are used as features by the models, between open-source and ING systems, (ii) measure the accuracy of different machine learning models in recommending Extract Method refactorings, (iii) compare the recommendations given by the models with the opinions of ING experts. Our results show that the feature distributions of ING systems and open-source systems are somewhat different, that machine learning models can recommend Extract Method refactorings with high accuracy, and that experts tend to agree with most of the recommendations of the model.Software Engineerin
Creating an automation tool for customer journey experts at ING
No full textING wants to offer their customers the best experience possible. To achieve this goal, ING’s Customer Journey Experts (CJEs) constantly map and analyze the way customers use ING services in a Customer Journey Map. These maps however, are hard to share and collaborate on. ING needs an online tool in which they can, together with multiple people, build and maintain Customer Journey Maps. During our research phase we visited many different squads and found out that no single solution fits all needs. That is why we made our tool as customizable as possible with features such as: colors, text decorations, highlighting and templates. We worked in bi-weekly sprints for which we selected work from a top 50 issues board that we ordered by importance and difficulty. The final product, Mapp , allows CJEs to define, share and collaborate on customer journeys. CJEs can illustrate their customer’s steps using text, images, emotions, checkboxes andtimelines. TosharetheirworktheycanexportasPDFandprintinanysize. Andfinallytocollaborate they can simply share their journey’s URL. The product was user validated during a large midterm and endterm test, as well as during short weekly tests. All of the chapter leads we talked to were super excited and are soon marketing the product in their teams!Customer Journey Tool Map
Releasing Fast and Slow: An Exploratory Case Study at ING
No full textThe appeal of delivering new features faster has led many software projects to adopt rapid releases. However, it is not well understood what the effects of this practice are. This paper presents an exploratory case study of rapid releases at ING, a large banking company that develops software solutions in-house, to characterize rapid releases. Since 2011, ING has shifted to a rapid release model. This switch has resulted in a mixed environment of 611 teams releasing relatively fast and slow. We followed a mixed-methods approach in which we conducted a survey with 461 participants and corroborated their perceptions with 2 years of code quality data and 1 year of release delay data. Our research shows that: rapid releases are more commonly delayed than their non-rapid counterparts, however, rapid releases have shorter delays; rapid releases can be beneficial in terms of reviewing and user-perceived quality; rapidly released software tends to have a higher code churn, a higher test coverage and a lower average complexity; challenges in rapid releases are related to managing dependencies and certain code aspects, e.g. design debt.Software EngineeringSoftware Technolog
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
Predicting Delays in Software Deliveries using Networked Classification at ING
No full textDelays in the delivery of software projects and the corresponding cost and schedule overruns have been common problems in the software industry for years. A challenge within software project management is to make accurate effort estimations during planning. Software projects are complex networks, with multiple dependencies between software tasks. This study aims to combine the field of effort estimation and networked classification to utilise network information for delay prediction in industry. We conducted a case study at ING, resulting in a number of insights with regards to networked classification in an industry setting. There is a difference in the organisational structure of open-source and industry projects. This constitutes to a difference in available information, but there is also an opportunity to leverage the organisational structure of ING to improve delay prediction performance.Using weights in networked classification has shown no improvement compared to not using them, but relational models do benefit from larger datasets as the used network contains more relational information.Based on the insights we recommend ING to: keep track of more information, improve data quality by educating their teams and create models for specific domains or teams to leverage their organisational structure.Computer Science | Data Science and Technolog
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