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

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

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

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