90,893 research outputs found

    Towards a lean model for production management of refurbishment projects, VTT Technology: 94

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    This is the Stage 3 Report for the ApRemodel project, which aims at improving processes for multi-occupancy retrofit by generating a lean model for project delivery. In this respect, a process-driven approach has been adopted to investigate what can be done to improve the way that retrofits projects are delivered. An initial literature review, focused on the management of refurbishment works, revealed that the research on this matter is scarce. There are plenty of studies related to the broad refurbishment area, however only a small number refer to the way that those construction projects are delivered. According to the literature, construction organisations have predominantly used traditional methods for managing the production of refurbishment projects. The problem is that those tools and techniques are not often appropriate to cope with the complex characteristics inherent to construction projects, especially in the case of refurbishments. Moreover, they have often not been based on a clear theoretical foundation. As a result, numerous types of waste have been identified in refurbishment projects such as waiting time, disruptions in performing tasks on site, rework, among others. This has led to unsatisfactory project performance in terms of low productivity, project delays, and cost overrun. The first step towards better production management in refurbishment projects is recognising the complexity of the sector in order to adopt the correct approach to cope with this specific scenario. In this respect, lean construction is identified as an appropriate way to deal with the complexity and uncertainty inherent in refurbishment projects, given that this management philosophy fully integrates the conversion, flow, and value views. This document builds on the findings from the literature review as well as evidence from case studies. Managerial practices based on lean construction principles have presented successful results in the management of complex projects. Case studies available in the literature report the feasibility and usefulness of this theoretical foundation. Moreover, the evidence from these studies show considerable potential for improving the management of refurbishment works. A list of methods, tools, and techniques are identified. This report may be used by construction refurbishment organisations and housing associations as a starting point for improving the efficiency in managing production of refurbishment projects. To this end, partnerships between industry and academia are strongly recommended. 4 Although the usefulness of lean principles in complex projects is already proved, further work is needed to check what practices are best for the respective refurbishment context, as well as identifying enablers and barriers for practical adoption. Furthermore, additional studies would be also necessary to better understand the extent to which the implementation of lean philosophy might influence performance of refurbishment projects. This report should be seen as work in progress with much more to learn, as detailed research work around the sustainable retrofit process in a lean way is further developed

    A Survey on Bayesian Deep Learning

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    A comprehensive artificial intelligence system needs to not only perceive the environment with different `senses' (e.g., seeing and hearing) but also infer the world's conditional (or even causal) relations and corresponding uncertainty. The past decade has seen major advances in many perception tasks such as visual object recognition and speech recognition using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. In recent years, Bayesian deep learning has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models. In this general framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in turn, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, control, etc. Besides, we also discuss the relationship and differences between Bayesian deep learning and other related topics such as Bayesian treatment of neural networks.Comment: To appear in ACM Computing Surveys (CSUR) 202

    Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

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    Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

    Detecting Functional Requirements Inconsistencies within Multi-teams Projects Framed into a Model-based Web Methodology

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    One of the most essential processes within the software project life cycle is the REP (Requirements Engineering Process) because it allows specifying the software product requirements. This specification should be as consistent as possible because it allows estimating in a suitable manner the effort required to obtain the final product. REP is complex in itself, but this complexity is greatly increased in big, distributed and heterogeneous projects with multiple analyst teams and high integration between functional modules. This paper presents an approach for the systematic conciliation of functional requirements in big projects dealing with a web model-based approach and how this approach may be implemented in the context of the NDT (Navigational Development Techniques): a web methodology. This paper also describes the empirical evaluation in the CALIPSOneo project by analyzing the improvements obtained with our approach.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2015-71938-RED

    Learning and Transferring IDs Representation in E-commerce

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    Many machine intelligence techniques are developed in E-commerce and one of the most essential components is the representation of IDs, including user ID, item ID, product ID, store ID, brand ID, category ID etc. The classical encoding based methods (like one-hot encoding) are inefficient in that it suffers sparsity problems due to its high dimension, and it cannot reflect the relationships among IDs, either homogeneous or heterogeneous ones. In this paper, we propose an embedding based framework to learn and transfer the representation of IDs. As the implicit feedbacks of users, a tremendous amount of item ID sequences can be easily collected from the interactive sessions. By jointly using these informative sequences and the structural connections among IDs, all types of IDs can be embedded into one low-dimensional semantic space. Subsequently, the learned representations are utilized and transferred in four scenarios: (i) measuring the similarity between items, (ii) transferring from seen items to unseen items, (iii) transferring across different domains, (iv) transferring across different tasks. We deploy and evaluate the proposed approach in Hema App and the results validate its effectiveness.Comment: KDD'18, 9 page

    Finding the right answer: an information retrieval approach supporting knowledge sharing

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    Knowledge Management can be defined as the effective strategies to get the right piece of knowledge to the right person in the right time. Having the main purpose of providing users with information items of their interest, recommender systems seem to be quite valuable for organizational knowledge management environments. Here we present KARe (Knowledgeable Agent for Recommendations), a multiagent recommender system that supports users sharing knowledge in a peer-to-peer environment. Central to this work is the assumption that social interaction is essential for the creation and dissemination of new knowledge. Supporting social interaction, KARe allows users to share knowledge through questions and answers. This paper describes KARe�s agent-oriented architecture and presents its recommendation algorithm
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