Defining Safe Training Datasets for Machine Learning Models Using Ontologies

Machine Learning (ML) models have been gaining popularity in recent years in a wide variety of domains, including safety-critical domains. While ML models have shown high accuracy in their predictions, they are still considered black boxes, meaning that developers and users do not know how the models make their decisions. While this is simply a nuisance in some domains, in safetycritical domains, this makes ML models difficult to trust. To fully utilize ML models in safetycritical domains, there needs to be a method to improve trust in their safety and accuracy without human experts checking each decision. This research proposes a method to increase trust in ML models used in safety-critical domains by ensuring the safety and completeness of the model’s training dataset. Since most of the complexity of the model is built through training, ensuring the safety of the training dataset could help to increase the trust in the safety of the model. The method proposed in this research uses a domain ontology and an image quality characteristic ontology to validate the domain completeness and image quality robustness of a training dataset. This research also presents an experiment as a proof of concept for this method where ontologies are built for the emergency road vehicle domain

How Police Chaplains Can Facilitate Building and Maintaining Trust with Law Enforcement: Through the Ministry of Presence

CPP Chaplaincy Corps is a ministry that provides spiritual, emotional, and mental support to its law enforcement personnel. Their function is to enhance the well-being and quality of life of the police officers as they serve and protect the citizens of their city, by offering a listening ear, a supportive presence, and providing individual pastoral care. This project seeks to determine if the newly organized CPP Chaplaincy Corps can be successful in facilitating the process of building trust with law enforcement through the Ministry of Presence and Pastoral Care. It also documents the progress and changes that have been made during the three years of its inauguration. Finally, this project evaluates whether the CPP Chaplaincy Corps mission has been successful in the CPP police department. The research method will be surveying how to build trust in this current ministry, analyzing the survey results, and suggesting ways to improve the ministry situation

Is the Automotive Industry using Design-for-Assembly Anymore?

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Methods and techniques for analyzing human factors facets on drivers

Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

Maximum risk reduction with a fixed budget in the railway industry

Decision-makers in safety-critical industries such as the railways are frequently faced with the complexity of selecting technological, procedural and operational solutions to minimise staff, passengers and third parties’ safety risks. In reality, the options for maximising risk reduction are limited by time and budget constraints as well as performance objectives. Maximising risk reduction is particularly necessary in the times of economic recession where critical services such as those on the UK rail network are not immune to budget cuts. This dilemma is further complicated by statutory frameworks stipulating ‘suitable and sufficient’ risk assessments and constraints such as ‘as low as reasonably practicable’. These significantly influence risk reduction option selection and influence their effective implementation. This thesis provides extensive research in this area and highlights the limitations of widely applied practices. These practices have limited significance on fundamental engineering principles and become impracticable when a constraint such as a fixed budget is applied – this is the current reality of UK rail network operations and risk management. This thesis identifies three main areas of weaknesses to achieving the desired objectives with current risk reduction methods as: Inaccurate, and unclear problem definition; Option evaluation and selection removed from implementation subsequently resulting in misrepresentation of risks and costs; Use of concepts and methods that are not based on fundamental engineering principles, not verifiable and with resultant sub-optimal solutions. Although not solely intended for a single industrial sector, this thesis focuses on guiding the railway risk decision-maker by providing clear categorisation of measures used on railways for risk reduction. This thesis establishes a novel understanding of risk reduction measures’ application limitations and respective strengths. This is achieved by applying ‘key generic engineering principles’ to measures employed for risk reduction. A comprehensive study of their preventive and protective capability in different configurations is presented. Subsequently, the fundamental understanding of risk reduction measures and their railway applications, the ‘cost-of-failure’ (CoF), ‘risk reduction readiness’ (RRR), ‘design-operationalprocedural-technical’ (DOPT) concepts are developed for rational and cost-effective risk reduction. These concepts are shown to be particularly relevant to cases where blind applications of economic and mathematical theories are misleading and detrimental to engineering risk management. The case for successfully implementing this framework for maximum risk reduction within a fixed budget is further strengthened by applying, for the first time in railway risk reduction applications, the dynamic programming technique based on practical railway examples

Regular Education Teachers\u27 Lived Experiences with Self-Efficacy in Light of the Endrew F. Decision of 2017 - A Phenomenological Study

The purpose of this phenomenological study was to describe the lived experiences of regular education teachers\u27 perceptions of self-efficacy aligned with the FAPE mandate in an inclusion model at a small, rural school district on the east coast. The central research question was as follows: What are the lived experiences of regular education teachers working within an inclusion model with students with disabilities? The theory guiding this study involves Bandura\u27s self-efficacy theory of behavioral change, which Bandura defines as a core belief in one\u27s capabilities to act to produce results. A qualitative hermeneutical phenomenology approach aligns with the study by offering researchers embedded in a phenomenon the flexibility to interpret lived experiences in an attempt not only to find but also to determine meaning. Fifteen educators from an east-coast school district comprised the sample pool. Collection methods included a survey, fifteen interviews, and a focus group. Triangulation of data revealed how regular education teachers working within an inclusion model understand the FAPE mandate and its impact on a teacher\u27s self-efficacy. Use of a content analysis strategy allowed for categorical interpretation of the structure, order, and patterns found from the lived experiences among the fifteen regular education participants. Three distinct major themes emerged, along with eight subthemes. This study revealed that although the participant teachers were supportive of inclusion model instructional programming, there were lived experiences aligned to the FAPE mandate that negatively (and significantly) affected their self-efficacy

A human factors perspective on volunteered geographic information

This thesis takes a multidisciplinary approach to understanding the unique abilities of Volunteered Geographic Information (VGI) to enhance the utility of online mashups in ways not achievable with Professional Geographic Information (PGI). The key issues currently limiting the use of successful of VGI are the concern for quality, accuracy and value of the information, as well as the polarisation and bias of views within the user community. This thesis reviews different theoretical approaches in Human Factors, Geography, Information Science and Computer Science to help understand the notion of user judgements relative to VGI within an online environment (Chapter 2). Research methods relevant to a human factors investigation are also discussed (Chapter 3). (Chapter 5) The scoping study established the fundamental insights into the terminology and nature of VGI and PGI, a range of users were engaged through a series of qualitative interviews. This led the development of a framework on VGI (Chapter 4), and comparative description of users in relation to one another through a value framework (Chapter 5). Study Two produced qualitative multi-methods investigation into how users perceive VGI and PGI in use (Chapter 6), demonstrating similarities and the unique ability for VGI to provide utility to consumers. Chapter Seven and Study Three brought insight into the specific abilities for VGI to enhance the user judgement of online information within an information relevance context (Chapter 7 and 8). In understanding the outcomes of these studies, this thesis discusses how users perceive VGI as different from PGI in terms of its benefit to consumers from a user centred design perspective (Chapter 9). In particular, the degree to which user concerns are valid, the limitation of VGI in application and its potential strengths in enriching the user experiences of consumers engaged within an information search. In conclusion, specific contributions and avenues for further work are highlighted (Chapter 10)