Bringing a construction site into the lab:a context-relevant lab-based evaluation of a multimodal mobile application

Mobile technology has not yet achieved widespread acceptance in the Architectural, Engineering, and Construction (AEC) industry. This paper presents work that is part of an ongoing research project focusing on the development of multimodal mobile applications for use in the AEC industry. This paper focuses specifically on a context-relevant lab-based evaluation of two input modalities – stylus and soft-keyboard v. speech-based input – for use with a mobile data collection application for concrete test technicians. The manner in which the evaluation was conducted as well as the results obtained are discussed in detail.

Unsupervised KDD to creatively support managers' decision making with fuzzy association rules:a distribution channel application

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis. © 2013 Elsevier Inc.

Emerging of human factor in risk analysis of home care service

Risk management in healthcare represents a group of various complex actions, implemented to improve the quality of healthcare services and guarantee the patients safety. Risks cannot be eliminated, but it can be controlled with different risk assessment methods derived from industrial applications and among these the Failure Mode Effect and Criticality Analysis (FMECA) is a largely used methodology. The main purpose of this work is the analysis of failure modes of the Home Care (HC) service provided by local healthcare unit of Naples (ASL NA1) to focus attention on human and non human factors according to the organization framework selected by WHO. © Springer International Publishing Switzerland 2014.

Architectural aspects of self-aware and self-expressive computing systems:from psychology to engineering

Work on human self-Awareness is the basis for a framework to develop computational systems that can adaptively manage complex dynamic tradeoffs at runtime. An architectural case study in cloud computing illustrates the framework's potential benefits.

Incorporating Water Sensitive Urban Design (WSUD) practices into the planning context:the conceptual case for lot-scale developments

In recent decades, a number of sustainable strategies and polices have been created to protect and preserve our water environments from the impacts of growing communities. The Australian approach, Water Sensitive Urban Design (WSUD), defined as the integration of urban planning and design with the urban water cycle management, has made considerable advances on design guidelines since 2000. WSUD stormwater management systems (e.g. wetlands, bioretentions, porous pavement etc), also known as Best Management Practices (BMPs) or Low Impact Development (LID), are slowly gaining popularity across Australia, the USA and Europe. There have also been significant improvements in how to model the performance of the WSUD technologies (e.g. MUSIC software). However, the implementation issues of these WSUD practices are mainly related to ongoing institutional capacity. Some of the key problems are associated with a limited awareness of urban planners and designers; in general, they have very little knowledge of these systems and their benefits to the urban environments. At the same time, hydrological engineers should have a better understanding of building codes and master plans. The land use regulations are equally as important as the physical site conditions for determining opportunities and constraints for implementing WSUD techniques. There is a need for procedures that can make a better linkage between urban planners and WSUD engineering practices. Thus, this paper aims to present the development of a general framework for incorporating WSUD technologies into the site planning process. The study was applied to lot-scale in the Melbourne region, Australia. Results show the potential space available for fitting WSUD elements, according to building requirements and different types of housing densities. © 2011 WIT Press.

Self-adaptive volunteered services composition through stimulus-and time-awareness

Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. Selection and composition decisions tend to encounter numerous uncertainties: service consumers and applications have little control of these services and tend to be uncertain about their level of support for the desired functionalities and non-functionalities. In this paper, we contribute to a self-awareness framework that implements two levels of awareness, Stimulus-awareness and Time-awareness. The former responds to basic changes in the environment while the latter takes into consideration the historical performance of the services. We have used volunteer service computing as an example to demonstrate the benefits that self-awareness can introduce to self-adaptation. We have compared the Stimulus-and Time-awareness approaches with a recent Ranking approach from the literature. The results show that the Time-awareness level has the advantage of satisfying higher number of requests with lower time cost.

Mobile bluetooth-based multi-player game development in ubiquitous computing

This paper describes the use of Bluetooth and Java-Based technologies in developing a multi-player mobile game in ubiquitous computing, which strongly depends on automatic contextual reconfiguration and context-triggered actions. Our investigation focuses on an extended form of ubiquitous computing which game software developers utilize to develop games for players. We have developed an experimental ubiquitous computing application that provides context-aware services to game server and game players in a mobile distributed computing system. Obviously, contextual services provide useful information in a context-aware system. However, designing a context-aware game is still a daunting task and much theoretical and practical research remains to be done to reach the ubiquitous computing era. In this paper, we present the overall architecture and discuss, in detail, the implementation steps taken to create a Bluetooth and Java based context-aware game. We develop a multi-player game server and prepare the client and server codes in ubiquitous computing, providing adaptive routines to handle connection information requests, logging and context formatting and delivery for automatic contextual reconfiguration and context-triggered actions. © 2010 Binary Information Press.

Using knowledge management to give context to analytics and big data and reduce strategic risk

At the moment, the phrases “big data” and “analytics” are often being used as if they were magic incantations that will solve all an organization’s problems at a stroke. The reality is that data on its own, even with the application of analytics, will not solve any problems. The resources that analytics and big data can consume represent a significant strategic risk if applied ineffectively. Any analysis of data needs to be guided, and to lead to action. So while analytics may lead to knowledge and intelligence (in the military sense of that term), it also needs the input of knowledge and intelligence (in the human sense of that term). And somebody then has to do something new or different as a result of the new insights, or it won’t have been done to any purpose. Using an analytics example concerning accounts payable in the public sector in Canada, this paper reviews thinking from the domains of analytics, risk management and knowledge management, to show some of the pitfalls, and to present a holistic picture of how knowledge management might help tackle the challenges of big data and analytics.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.