Customer perception of switch-feel in luxury sports utility vehicles

Successful new product introduction requires that product characteristics relate to the customer on functional, emotional, aesthetic and cultural levels. As a part of research into automotive human machine interfaces (HMI), this paper describes holistic customer research carried out to investigate how the haptics of switches in luxury sports utility vehicles (SUVs) are perceived by customers. The application of these techniques, including an initial proposal for objective specifications, is addressed within the broader new product introduction context, and benefits described. One-hundred and one customers of SUVs assessed the feel of automotive push switches, completing the tasks both in, and out of vehicles to investigate the effect of context. Using the semantic differential technique, hedonic testing, and content analysis of customers’ verbatim comments, a holistic picture has been built up of what influences the haptic experience. It was found that customers were able to partially discriminate differences in switch-feel, alongside considerations of visual appearance, image, and usability. Three factors named ‘Affective’, ‘Robustness and Precision’, and ‘Silkiness’ explained 61% of the variance in a principle components analysis. Correlations of the factors with acceptance scores were 0.505, 0.371, and 0.168, respectively

Evaluation of Automatic Video Captioning Using Direct Assessment

We present Direct Assessment, a method for manually assessing the quality of automatically-generated captions for video. Evaluating the accuracy of video captions is particularly difficult because for any given video clip there is no definitive ground truth or correct answer against which to measure. Automatic metrics for comparing automatic video captions against a manual caption such as BLEU and METEOR, drawn from techniques used in evaluating machine translation, were used in the TRECVid video captioning task in 2016 but these are shown to have weaknesses. The work presented here brings human assessment into the evaluation by crowdsourcing how well a caption describes a video. We automatically degrade the quality of some sample captions which are assessed manually and from this we are able to rate the quality of the human assessors, a factor we take into account in the evaluation. Using data from the TRECVid video-to-text task in 2016, we show how our direct assessment method is replicable and robust and should scale to where there many caption-generation techniques to be evaluated.Comment: 26 pages, 8 figure

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models

Artificial Intelligence in the Context of Human Consciousness

Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications