1,278,544 research outputs found
An analysis of the key factors affecting the success of a re-launched destination marketing website in the UK
Business Information Systems
e-Commerce/e-business
Computer Appl. in Social and Behavioral Sciences
Marketing
Information Systems Applications (incl. Internet)This paper presents a case study of the re-launch of a DMO website in the UK. It evaluates the perceived usability of the new website and identifies the key factors affecting customers’ intention to use the new website. A large-scale online survey was developed to understand a number of issues relating to usability (e.g. aesthetics, effectiveness) and psychological and behavioural indicators (e.g. perceived trustworthiness and intent to use). Both quantitative and qualitative data was analysed to understand users’ perceptions, behaviour and attitudes towards the re-launched website. A Structural Equation Model (SEM) was developed to identify the factors affecting their intention to use the new website. The SEM model identified the impact of a variety of factors on intention to use and the descriptive analysis, using both qualitative and quantitative data, highlights further areas of research
A retrieval-based dialogue system utilizing utterance and context embeddings
Finding semantically rich and computer-understandable representations for
textual dialogues, utterances and words is crucial for dialogue systems (or
conversational agents), as their performance mostly depends on understanding
the context of conversations. Recent research aims at finding distributed
vector representations (embeddings) for words, such that semantically similar
words are relatively close within the vector-space. Encoding the "meaning" of
text into vectors is a current trend, and text can range from words, phrases
and documents to actual human-to-human conversations. In recent research
approaches, responses have been generated utilizing a decoder architecture,
given the vector representation of the current conversation. In this paper, the
utilization of embeddings for answer retrieval is explored by using
Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor
(ANN) model, to find similar conversations in a corpus and rank possible
candidates. Experimental results on the well-known Ubuntu Corpus (in English)
and a customer service chat dataset (in Dutch) show that, in combination with a
candidate selection method, retrieval-based approaches outperform generative
ones and reveal promising future research directions towards the usability of
such a system.Comment: A shorter version is accepted at ICMLA2017 conference;
acknowledgement added; typos correcte
Insights from computational modelling and simulation towards promoting public health among African countries
One of the problems associated with some African countries is the increasing trend of
road mortality as a result of road fatalities. This has been a major concern. The negative
impacts of these on public health cannot be underestimated. An issue of concern is the
high record of casualties being recorded on an annual basis as a result of over-speeding,
overtaking at dangerous bends, alcohol influence and non-chalant attitude of drivers to
driving. The aim of this research is to explore and adapt the knowledge of finite state
algorithm, modeling and simulation to design and implement a novel prototype of an
advanced traffic light system towards promoting public health among African countries.
Here, we specify and built a model of an advanced wireless traffic control system, which
will help complement existing traffic control systems among African countries. This
prototype is named Advanced Wireless Traffic Control System (WPDTCS). We developed
this model using an event-driven programming approach. The technical details of the
model were based on knowledge adapted from the Finite State Automation Transition
algorithm. It is expected that the AWTCS will promote the evolution of teaching in
modeling, simulation, public safety by offering trainees an advanced pedagogical
product. It will also permit to strengthen the collaboration of knowledge from the fields
of Computer Science, Public health, and Electrical Engineering.
Keywords: public health, public safety, modelling , simulation, pr
Optimum Concentration Ratio Analysis Using Dynamic Thermal Model for Concentrated Photovoltaic System
Concentrated photovoltaic (PV) technology represents a growing market in the field of terrestrial solar energy production. As the demand for renewable energy technologies increases, further importance is placed upon the modeling, design, and simulation of these systems. Given the U.S. Air Force cultural shift towards energy awareness and conservation, several concentrated PV systems have been installed on Air Force installations across the country. However, there has been a dearth of research within the Air Force devoted to understanding these systems in order to possibly improve the existing technologies. This research presents a new model for a simple concentrated PV system. This model accurately determines the steady state operating temperature as a function of the concentration factor for the optical part of the concentrated PV system, in order to calculate the optimum concentration that maximizes power output and efficiency. The dynamic thermal model derived is validated experimentally using a commercial polysilicon solar cell, and is shown to accurately predict the steady state temperature and facilitates computer analysis and prediction of the ideal concentration factor
Final Report on MITRE Evaluations for the DARPA Big Mechanism Program
This report presents the evaluation approach developed for the DARPA Big
Mechanism program, which aimed at developing computer systems that will read
research papers, integrate the information into a computer model of cancer
mechanisms, and frame new hypotheses. We employed an iterative, incremental
approach to the evaluation of the three phases of the program. In Phase I, we
evaluated the ability of system and human teams ability to read-with-a-model to
capture mechanistic information from the biomedical literature, integrated with
information from expert curated biological databases. In Phase II we evaluated
the ability of systems to assemble fragments of information into a mechanistic
model. The Phase III evaluation focused on the ability of systems to provide
explanations of experimental observations based on models assembled (largely
automatically) by the Big Mechanism process. The evaluation for each phase
built on earlier evaluations and guided developers towards creating
capabilities for the new phase. The report describes our approach, including
innovations such as a reference set (a curated data set limited to major
findings of each paper) to assess the accuracy of systems in extracting
mechanistic findings in the absence of a gold standard, and a method to
evaluate model-based explanations of experimental data. Results of the
evaluation and supporting materials are included in the appendices.Comment: 46 pages, 8 figure
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