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The transition from a requirements document to a formal specification in Event-B is usually manual and ad-hoc. In order to bridge this gap, we propose a method based on Behavior-Driven Development, an agile approach, and that uses a structured natural language conformant to the formalism of the Semantics of Business Vocabulary and Business Rules (SBVR) standard. This method will successively refine...
Software start-ups are a new and relatively unexplored field for software engineering researchers. However, conducting empirical studies with start-ups is difficult. Start-ups produce very little "hard" evidence, thus data collection methods are limited to interviews and surveys. These methods come with their limitations, namely interview studies are not scalable to a large number of companies,...
Failing to identify multi-word expression (MWE) may cause serious problems for many Natural Language Processing (NLP) tasks. Previous approaches heavily depend on language specific knowledge and pre-existing natural language processing (NLP) tools. However, many languages (including Chinese language) have less such resources and tools compared to English. An automatically learn effective features...
In this paper we propose several novel approaches for incorporating forgetting mechanisms into sequential prediction based machine learning algorithms. The broad premise of our work, supported and motivated in part by recent findings stemming from neurology research on the development of human brains, is that knowledge acquisition and forgetting are complementary processes, and that learning can (perhaps...
The main goal of an industrial microgrid during grid-connected operation is maximal cost saving for the microgrid owner. Many industrial companies do not only pay for the amount of electrical energy, but also for the maximum electrical power, which they have drawn from the distribution grid within the billing period. Under these conditions two basic options of cost saving exists utilizing the local...
The platform we're proposing will be the main actor of the upcoming paradigm shift from representative governance to self governance, the architecture, functionality and interface of the platform was modelled after the human anatomy. Human decision making efficiency is dependent upon the governance system of the deciding agents and the roles they play. Aligning the context in which a decision impacting...
The paper aims to identify the elements that make possible the appearance of emergent properties in organizations structured as social networks, considered as adaptive multi-agent complex systems. The dynamics of these communities (referring primarily to the increasing number of members who interact) was analyzed by the specific rules of scale-free networks growth and the specific principles of multi-agent...
Down Syndrome is a common disorder which causes intellectual disability among other symptoms. To date, no treatment exists for the learning difficulties associated with Down Syndrome. However, the pharmaceutical drug memantine has been shown to improve learning ability in a Down Syndrome model of mice (Ts65Dn) exposed to Context Fear Conditioning (CFC), an existing technique used in determining the...
Multi-source clustering is common data mining task the aim of which is to use several clustering algorithms to analyze different aspects of the same data. Well known applications of multi-source clustering include horizontal collaborative clustering and multi-view clustering, where several algorithms combine their strengths by exchanging information about their finding on local structures with a goal...
Semi-supervised clustering has been widely explored in the last years. In this paper, we present HCAC-ML (Hierarchical Confidence-based Active Clustering with Metric Learning), an innovative approach for this task which employs distance metric learning through cluster-level constraints. HCAC-ML is based on the HCAC algorithm, an state-of-the-art algorithm for hierarchical semi-supervised clustering...
Sequence to sequence (seq2seq) prediction is a key to many tasks of machine learning. Personal computer software sequence, as one of these tasks, was regarded as stochastic and unpredictable in the past. However, the deep neural networks (DNNs) have achieved excellent performance recently in sequence to sequence tasks, especially in the field of natural language process (NLP) such as language model,...
Data representation is a fundamental task in machine learning, which affects the performance of the whole machine learning system. In the past few years, with the rapid development of deep learning, the models for word embedding based on neural networks have brought new inspiration to the research of natural language processing. In this paper, two kinds of schemes for improving the Continuous Bag-of-Words...
Provides an abstract for each of the tutorial presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.
Traditional code search engines often do not perform well with natural language queries since they mostly apply keyword matching. These engines thus require carefully designed queries containing information about programming APIs for code search. Unfortunately, existing studies suggest that preparing an effective query for code search is both challenging and time consuming for the developers. In this...
Sexual harassment at workplace has been a criticalchallenge for women, especially in the service sector due to oddworking hours. Companies and Government on their part havetaken up measures to protect women employees but theproblem seems persistent. To address this, we have designed aregulatory solution based on operant conditioning. Operantconditioning argues that people's behaviors are primarilycontrolled...
Context: A key issue when dealing with the generalization threat of software engineering experiments is to use different subject types. Objective: In this paper, we aim to investigate which subject types are used in experiments and their impact on results. Method: We have performed a systematic mapping study by manually searching experiments published from January 2014 to June 2016 in six leading...
The relevance of data created in or about the IoT has a strong reliance on the context, especially spatiotemporal context, of the device and application perceiving it. To ensure that applications perceive data items that are relevant to the current context, it is necessary to restrict when each item is available. To control an application's perceptions of data availability, data items are often put...
Decision support systems increasingly support experts' work, but errors caused by them may have severe consequences, especially in the medical domain. To better understand how these inaccuracies affect experts' behavior and decisions, a quality of context model was constructed. Based on the model a controlled online experiment was conducted in which physicians had to treat hypothetical cases while...
This technical briefing provides an overview of how quantitative empirical research methods can be combined with qualitative ones generating the family of empirical software engineering approaches known as mixed-methods. The ultimate aim of such mixed-methods is supporting cause-effect claims combining multiple data types, sources and analyses that provide software practitioners and academicians solid...
Product Line (PL) configuration practices have been employed by industries as a mass customization process. However, due to the NP-hard nature of the process, performance concerns start to be an issue when facing large-scale configuration spaces. The aim of my doctoral research is therefore to propose an efficient collaborative-based recommender system that provides accurate and scalable solutions...
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