Investigating Contextual Cues as Indicators for EMA Delivery

In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular Ecological Momentary Assessment (EMA) prompt. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant\u27s activity, conversation status, audio, and location, we can predict whether an EMA prompt triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.410. Using this knowledge, the researchers conducting field studies can efficiently schedule EMA prompts and achieve higher response rates

Driving whilst using in-vehicle information systems (IVIS): benchmarking the impairment to alcohol

Using the lane change task (LCT) a comparison of driving performance was made between normal (baseline) driving, driving whilst using an in-vehicle information system (IVIS) and driving while intoxicated at the UK blood alcohol level (80 mg per 100 ml). The results provided clear evidence for impaired performance of the LCT when performing an IVIS task in comparison to both baseline (LCT alone) and alcohol conditions. However, the LCT was found to be insensitive to the effects of alcohol in the absence of a secondary task. It is concluded that LCT performance can be impaired more when undertaking certain IVIS tasks than by having a blood alcohol level at the UK legal limit but the LCT requires further development before it can be used as a convincing proxy for the driving task

The 1990 Johnson Space Center bibliography of scientific and technical papers

Abstracts are presented of scientific and technical papers written and/or presented by L. B. Johnson Space Center (JSC) authors, including civil servants, contractors, and grantees, during the calendar year of 1990. Citations include conference and symposium presentations, papers published in proceedings or other collective works, seminars, and workshop results, NASA formal report series (including contractually required final reports), and articles published in professional journals

A model for adaptive multimodal mobile notification

Information is useless unless it is used whilst still applicable. Having a system that notifies the user of important messages using the most appropriate medium and device will benefit users that rely on time critical information. There are several existing systems and models for mobile notification as well as for adaptive mobile notification using context awareness. Current models and systems are typically designed for a specific set of mobile devices, modes and services. Communication however, can take place in many different modes, across many different devices and may originate from many different sources. The aim of this research was to develop a model for adaptive mobile notification using context awareness. An extensive literature study was performed into existing models for adaptive mobile notification systems using context awareness. The literature study identified several potential models but no way to evaluate and compare the models. A set of requirements to evaluate these models was developed and the models were evaluated against these criteria. The model satisfying the most requirements was adapted so as to satisfy the remaining criteria. The proposed model is extensible in terms of the modes, devices and notification sources supported. The proposed model determines the importance of a message, the appropriate device and mode (or modes) of communication based on the user‘s context, and alerts the user of the message using these modes. A prototype was developed as a proof-of-concept of the proposed model and evaluated by conducting an extensive field study. The field study highlighted the fact that most users did not choose the most suitable mode for the context during their initial subscription to the service. The field study also showed that more research needs to be done on an appropriate filtering mechanism for notifications. Users found that the notifications became intrusive and less useful the longer they used them

REAL ESTATE VALUE FORECASTING SYSTEM USING DATA MINING AND NEURAL NETWORK APPROACH (REVFOS)

Modern science and engineering are based on applying first-principle models in order to describe physical, biological and social systems. It is an approach starts with a basic scientific model such as Newton's Law of Motion or Maxwell's equations in electromagnetism and it leads to building various applications in mechanical and electrical engineering. However, in many domains that underlying first principle is unknown and unable to elaborate or the systems developed are too complex to be mathematically formalized. Thus, there is currently a paradigm shift from classical modeling and analyses based on first principles to developing models and the corresponding analyses directly from data. The necessitate to understand large, complex, information-rich data sets in widespread to virtually all fields of business, science and engineering as in the business world, corporate and customer data are treated as a strategic assets. The ability to extract useful knowledge hidden in these sets of data and to act on that knowledge is becoming increasingly important in today's competitive world. The entire process of applying computer-based methodologies including new techniques for discovering knowledge from raw data is known as data mining. Data mining is the process of identifying and analyzing data from diverse perspectives and summarizing it into constructive and useful information which it can be utilized as revenue increments, costs reductions and input productions. Technically, data mining application orsoftware is been treated as one ofanalytical tools for analyzing data and input gathered from various sources. Furthermore, it also allows users to scrutinize data from different dimensions and angels in order to categorize it before summarize the possible relationship identified. In addition, data mining is the process of identifying correlations orpatterns among dozens of fields in large relational databases

Interruptibility prediction for ubiquitous systems: conventions and new directions from a growing field

When should a machine attempt to communicate with a user? This is a historical problem that has been studied since the rise of personal computing. More recently, the emergence of pervasive technologies such as the smartphone have extended the problem to be ever-present in our daily lives, opening up new opportunities for context awareness through data collection and reasoning. Complementary to this there has been increasing interest in techniques to intelligently synchronise interruptions with human behaviour and cognition. However, it is increasingly challenging to categorise new developments, which are often scenario specific or scope a problem with particular unique features. In this paper we present a meta-analysis of this area, decomposing and comparing historical and recent works that seek to understand and predict how users will perceive and respond to interruptions. In doing so we identify research gaps, questions and opportunities that characterise this important emerging field for pervasive technology

The effect of selective spatial attention on peripheral discrimination thresholds

Experiments were conducted to investigate the role of attention in peripheral detection and discrimination. Advance spatial cues informed subjects about likely target positions; the task required to detect/discriminate plus localise a target briefly presented at cued or uncued locations, with accuracy as the dependent variable ("cost-benefit" analysis).Spatial cueing produced reliable advantages for cued over uncued locations, in single and in multiple element displays. However, costs plus benefits were less marked for single displays. Thus, advance knowledge of the likely target location enhances performance also when there are no competing stimuli present in the visual field. But costs plus benefits are smaller because single target onsets at uncued locations summon attention in the same "automatic" fashion as peripheral cues. Peripheral cues trigger a rapid facilitatory component (automatic), fading out within 300 msec after cue onset. Facilitation is then maintained by a less effective mechanism (controlled). Central cues initiate only this second component. Sustained, controlled, orienting in response to central cues is interruptable by automatic orienting in response to uninformative peripheral flashes. Interruption also occurs when irrelevant flashes compete with peripheral cues. However, interference is less marked for the early automatic than for the following controlled orienting component. Indication of a second position (four-location display) to be most likely resulted in a marked sensitivity gain for this position, relative to uncued locations in a single cue condition. That is, attention could be simultaneously shared between two cued positions. For a luminance detection task (single target), cued locations showed no advantage in sensitivity; but for letter detection tasks (target plus distractors), there was a marked priming effect. That is, letter detection is capacity limited, whereas luminance detection is not. In all tasks, decision criteria are largely preset according to a-priori target probabilities assigned to particular locations, i.e. more liberal for cued and more conservative for uncued locations