Who's Driving My Car? A Machine Learning based Approach to Driver Identification

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Artificial Intelligence in Modern Society

Artificial intelligence is progressing rapidly into diverse areas in modern society. AI can be used in several areas such as research in the medical field or creating innovative technology, for instance, autonomous vehicles. Artificial intelligence is used in the medical field to improve the accuracy of programs used for detecting health conditions. AI technology is also used in programs such as Netflix or Spotify. This type of AI will monitor a user’s habits and make recommendations based on their recent activity. Banks use AI systems to monitor activity on members’ accounts to check for identity theft, approve loans and maintain online security. Systems like these can even be found in call centers. These programs analyze a caller’s voice in real time to provide information to the call center which helps them build a faster rapport with the caller. The purpose of this research paper is to explain how artificial intelligence is creating advanced technologies in various fields of study which will create a more efficient society

Investigating the relationship between post-injury occupational change and persistent occupational identity: A mixed methodological study combining quantitative analysis of a survey of adult Australians with qualitative analysis of interviews with Truck Drivers who have experienced occupational change

The literature regarding occupational change indicates that identification with one occupation can present a barrier to making the change to another. Rehabilitation Counsellors are often given the responsibility of assisting individuals to make that change, usually through vocational counselling. The principles which underpin the conduct of vocational counselling commonly do not include consideration of the effect of persistent occupational identity on occupational change. While the concept of occupational identity has been the topic of extensive research for several decades there is no consensus regarding its definition, or how it is different from related concepts and very limited research into how it may influence the outcome of attempted occupational change after injury. Consequently, this study had three aims. Clarification of what occupational identity is; an examination of the experience of occupational identity; and an exploration of its influence on post-injury experiences of occupational change. A mixed methodological approach was adopted consisting of a quantitative analysis of survey data, and qualitative analyses of the experience of occupational identity and post-injury attempts at returning to a more suitable occupation. The survey involved 336 participants who provided demographic details relevant to their working life and an assessment of the level of their occupational identity. Analyses of chi-squared tests indicated that gender, level of educational qualification, employment status and occupational type were influential on the level of occupational identity. As a result of these analyses, recommendations are presented for how occupational identity might be defined and differentiated from related concepts. The qualitative analyses consisted of semi-structured interviews with 11 Truck Drivers who had experience of career disruption due to injury. An Interpretative Phenomenological Analysis method was used to identify themes connecting the experience of both occupational identity and attempts at occupational change. Several themes relevant to occupational identity were identified, including the involvement of a specific agent in its genesis, and a sense of power associated with its maintenance. Themes relevant to occupational change confirmed that a persistent occupational identity presented a substantial barrier to a successful change to a more suitable occupation. As a result of these analyses, recommendations are made for the enhancement of vocational counselling strategies. The implications of a persistent occupational identity on the negotiation of other biographical disruptions are raised as it has the potential to be prevent changes of occupation occasioned by a range of other life changing events

Public health policy for management of hearing impairments based on big data analytics: EVOTION at Genesis

The holistic management of hearing loss (HL) requires appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation; detection and prevention of cognitive decline; protection from noise; and socioeconomic inclusion of HL patients. However, currently the evidential basis for forming such policies is limited. Holistic HL management policies require the analysis of heterogeneous data, including Hearing Aid (HA) usage, noise episodes, audiological, physiological, cognitive, clinical and medication, personal, behavioural, life style, occupational and environmental data. To utilise these data in forming holistic HL management policies, EVOTION, a new European research and innovation project, aims to develop an integrated platform supporting: (a) the analysis of related datasets to enable the identification of causal and other effects amongst them using various forms of big data analytics, (b) policy decision making focusing on the selection of effective interventions related to the holistic management of HL, based on the outcomes of (a) and the formulation of related public health policies, and (c) the specification and monitoring of such policies in a sustainable manner. In this paper, we describe the EVOTION approach

Comparing Change Management Processes for Requirements and Manufacturing: An Interview Based Study

This study compares requirement and manufacturing change management processes to determine the processes in use and if similar processes can be used for both types of changes. A literature review is used to identify prescribed process stages. Ten stages are identified for both requirements and manufacturing change management. A series of interviews are then conducted with four different population groups to determine the process stages actually used in the field. The resulting requirement and manufacturing change process models are compared with the process models from the literature and with each other. Further, a thematic analysis is performed on the interview findings. Ultimately, differences are found between the prescribed and practiced change management models for both types of changes. Formal documentation stages are more prevalent for the manufacturing domain, though documentation in practice is less than what is prescribed. This includes the issuance of change requests and change orders in manufacturing change management that are not present in requirement change management processes. Significant differences were also found between the two change types; namely, requirement changes deal with more abstract concepts and as such can afford more informal documentation, whereas manufacturing changes deal with existing artifacts and require solid documentation. Additional research thrusts are identified to help reconcile change management processes across the life cycle

A solution to the hyper complex, cross domain reality of artificial intelligence: The hierarchy of AI

Artificial Intelligence (AI) is an umbrella term used to describe machine-based forms of learning. This can encapsulate anything from Siri, Apple's smartphone-based assistant, to Tesla's autonomous vehicles (self-driving cars). At present, there are no set criteria to classify AI. The implications of which include public uncertainty, corporate scepticism, diminished confidence, insufficient funding and limited progress. Current substantial challenges exist with AI such as the use of combinationally large search space, prediction errors against ground truth values, the use of quantum error correction strategies. These are discussed in addition to fundamental data issues across collection, sample error and quality. The concept of cross realms and domains used to inform AI, is considered. Furthermore there is the issue of the confusing range of current AI labels. This paper aims to provide a more consistent form of classification, to be used by institutions and organisations alike, as they endeavour to make AI part of their practice. In turn, this seeks to promote transparency and increase trust. This has been done through primary research, including a panel of data scientists / experts in the field, and through a literature review on existing research. The authors propose a model solution in that of the Hierarchy of AI

Making Sense of Making Sense - Exploring users’ understanding of automated vehicles during use

Automation has for a long time been embraced by the vehicle industry and in recent years, the amount and sophistication of automation in vehicles have rapidly increased, creating more advanced automated vehicle (AV) systems. The complexity of AVs does not only pose a technical challenge, but the entry of automation into vehicles also creates new dynamics in the human-vehicle interaction, that puts new demands on the user. Previous research has identified the importance of user understanding of Automated Vehicles, as this affects usage directly as well as indirectly by impacting acceptance. In this thesis, a design approach has been chosen that uses a product semantic framework as the basis for addressing the issue of user understanding with the aim of exploring how users make sense of the AV. The research presented is based on data from three quasi-experimental study, conducted with users of a (i) seemingly fully automated vehicle, (ii) vehicle with two different levels of automation, and (iii) an advanced driver assistance system for docking buses. The findings show that use of the AVs gave rise to several levels of meaning, based on two different processes. The main one was an intermeaning process, where integration of the participants’ conceptual models, artefactual signifiers, and situational signifiers in a context developed meaning. However, an intrameaning process was also evident, where meanings themselves developed new meanings. The findings also show that the usage of the AV itself is an integral part of the process of making sense, where both processes affect how the system is used and the usage triggers new meaning to arise. This thesis presents a model based on the findings, describing four important factors: the user’s conceptual model, the signifiers, the meanings that arise during use of the AV, and the context in which it is used. The model illustrates the complex interplay between these four components and can be used to better understand and investigate how users make sense of AVs to aid the design and development of AVs. The thesis also contributes to the field of product semantics through the practical application of product semantic theories, in addition to providing further insight into how users develop meaning and make sense of artefacts, by describing the processes and components which seem to be the foundation when making sense of artefacts.Having said that, further studies need to explore in greater detail the dynamics of the process of making sense, how meaning changes during a prolonged usage, and how the tentative model could be advanced to be able to be used in the AV development and evaluation processes

Spartan Daily, September 3, 2003

Volume 121, Issue 4https://scholarworks.sjsu.edu/spartandaily/9870/thumbnail.jp

Robust Algorithms for Estimating Vehicle Movement from Motion Sensors Within Smartphones

Building sustainable traffic control solutions for urban streets (e.g., eco-friendly signal control) and highways requires effective and reliable sensing capabilities for monitoring traffic flow conditions so that both the temporal and spatial extents of congestion are observed. This would enable optimal control strategies to be implemented for maximizing efficiency and for minimizing the environmental impacts of traffic. Various types of traffic detection systems, such as inductive loops, radar, and cameras have been used for these purposes. However, these systems are limited, both in scope and in time. Using GPS as an alternative method is not always viable because of problems such as urban canyons, battery depletion, and precision errors. In this research, a novel approach has been taken, in which smartphone low energy sensors (such as the accelerometer) are exploited. The ubiquitous use of smartphones in everyday life, coupled with the fact that they can collect, store, compute, and transmit data, makes them a feasible and inexpensive alternative to the mainstream methods. Machine learning techniques have been used to develop models that are able to classify vehicle movement and to detect the stop and start points during a trip. Classifiers such as logistic regression, discriminant analysis, classification trees, support vector machines, neural networks, and Hidden Markov models have been tested. Hidden Markov models substantially outperformed all the other methods. The feature quality plays a key role in the success of a model. It was found that, the features which exploited the variance of the data were the most effective. In order to assist in quantifying the performance of the machine learning models, a performance metric called Change Point Detection Performance Metric (CPDPM) was developed. CPDPM proved to be very useful in model evaluation in which the goal was to find the change points in time series data with high accuracy and precision. The integration of accelerometer data, even in the motion direction, yielded an estimated speed with a steady slope, because of factors such as phone sensor bias, vibration, gravity, and other white noise. A calibration method was developed that makes use of the predicted stop and start points and the slope of integrated accelerometer data, which achieves great accuracy in estimating speed. The developed models can serve as the basis for many applications. One such field is fuel consumption and CO2 emission estimation, in which speed is the main input. Transportation mode detection can be improved by integrating speed information. By integrating Vehicle (Phone) to Infrastructure systems (V2I), the model outputs, such as the stop and start instances, average speed along a corridor, and queue length at an intersection, can provide useful information for traffic engineers, planners, and decision makers