57,383 research outputs found
A requirements engineering framework for integrated systems development for the construction industry
Computer Integrated Construction (CIC) systems are computer environments through which
collaborative working can be undertaken. Although many CIC systems have been developed to demonstrate the
communication and collaboration within the construction projects, the uptake of CICs by the industry is still
inadequate. This is mainly due to the fact that research methodologies of the CIC development projects are
incomplete to bridge the technology transfer gap. Therefore, defining comprehensive methodologies for the
development of these systems and their effective implementation on real construction projects is vital.
Requirements Engineering (RE) can contribute to the effective uptake of these systems because it drives the
systems development for the targeted audience. This paper proposes a requirements engineering approach for
industry driven CIC systems development. While some CIC systems are investigated to build a broad and deep
contextual knowledge in the area, the EU funded research project, DIVERCITY (Distributed Virtual Workspace
for Enhancing Communication within the Construction Industry), is analysed as the main case study project
because its requirements engineering approach has the potential to determine a framework for the adaptation of
requirements engineering in order to contribute towards the uptake of CIC systems
Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences
This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering
Intrusion Detection Systems Using Adaptive Regression Splines
Past few years have witnessed a growing recognition of intelligent techniques
for the construction of efficient and reliable intrusion detection systems. Due
to increasing incidents of cyber attacks, building effective intrusion
detection systems (IDS) are essential for protecting information systems
security, and yet it remains an elusive goal and a great challenge. In this
paper, we report a performance analysis between Multivariate Adaptive
Regression Splines (MARS), neural networks and support vector machines. The
MARS procedure builds flexible regression models by fitting separate splines to
distinct intervals of the predictor variables. A brief comparison of different
neural network learning algorithms is also given
Personalized Ambience: An Integration of Learning Model and Intelligent Lighting Control
The number of households and offices adopting automation system is on the rise. Although devices and actuators can be controlled through wireless transmission, they are mostly static with preset schedules, or at different times it requires human intervention. This paper presents a smart ambience system that analyzes the userās lighting habits, taking into account different environmental context variables and user needs in order to automatically learn about the userās preferences and automate the room ambience dynamically. Context information is obtained from Yahoo Weather and environmental data pertaining to the room is collected via Cubesensors to study the userās lighting habits. We employs a learning model known as the Reduced Error Prune Tree (REPTree) to analyze the usersā preferences, and subsequently predicts the preferred lighting condition to be actuated in real time through Philips Hue. The system is able to ensure the userās comfort at all time by performing a closed feedback control loop which checks and maintains a suitable lighting ambience at optimal level
Real-time human action recognition on an embedded, reconfigurable video processing architecture
Copyright @ 2008 Springer-Verlag.In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine (SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. āmotion history imageā) class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfiured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments.DTI and Broadcom Ltd
VECTORS: Video communication through opportunistic relays and scalable video coding
Crowd-sourced video distribution is frequently of interest in the local
vicinity. In this paper, we propose a novel design to transfer such content
over opportunistic networks with adaptive quality encoding to achieve
reasonable delay bounds. The video segments are transmitted between source and
destination in a delay tolerant manner using the Nearby Connections Android
library. This implementation can be applied to multiple domains, including farm
monitoring, wildlife, and environmental tracking, disaster response scenarios,
etc. In this work, we present the design of an opportunistic contact based
system, and we discuss basic results for the trial runs within our institute.Comment: 13 pages, 6 figures, and under 3000 words for submission to the
SoftwareX journa
FPGA implementation of real-time human motion recognition on a reconfigurable video processing architecture
In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine(SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. ``motion history image") class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfigured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
Bridging the Gap Between Research and Practice: The Agile Research Network
We report an action research-oriented approach to investigating agile project management methods which aims to bridge the gap between academic research and agile practice. We have set up a research network of academics from two universities, through which we run focussed project-based research into agile methods. Organisations are invited to suggest an āagile challengeā and we work closely with them to investigate how challenge affects them. Our approach is both academic and practical. We use appropriate research methods such as interviews, observation and discussion to clarify and explore the nature of the challenge. We then undertake a detailed literature review to identify practical approaches that may be appropriate for adoption, and report our findings. If the organisation introduces new practices or approaches as a result of our work, we conduct an academic evaluation. Alternatively, if we uncover an under-researched area, we propose undertaking some basic research. As befits the topic, we work iteratively and incrementally and produce regular outputs.
In this paper we introduce our approach, overview research methods used in the agile research literature, describe our research model, outline a case study, and discuss the advantages and disadvantages of our approach. We discuss the importance of producing outputs that are accessible to practitioners as well as researchers. Findings suggest that by investigating the challenges that organisations propose, we uncover problems that are of real relevance to the agile community and obtain rich insights into the facilitators and barriers that organisations face when using agile methods. Additionally, we find that practitioners are interested in research results as long as publications are relevant to their needs and are written accessibly. We are satisfied with the basic structure of our approach, but we anticipate that the method will evolve as we continue to work with collaborators
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