A situational awareness framework for improving earthquake response, recovery and resilience

When we think of understanding the impact on the buildings of a city from an earthquake we imagine structural engineers assessing structures and the local area through measurements and readings. However, the access to such areas is not always straightforward and nor is it necessarily possible to have enough manpower to complete these analyses. Instead, crowdsourcing and smart sensors can be utilized in both the pre and post disaster phases using information witnesses to give enhanced situational awareness to those coordinating the earthquake response effort. Even in remote areas many people have access to smartphones, wearable technology and mobile internet access. Furthermore, with the advent of smart cities, further sensors can be placed strategically on infrastructure and transmit information about its structural health. Dedicated mobile applications can be used to capture reports, photographs and videos of vulnerable infrastructure before and after an earthquake. These photos and reports can then be mapped to identify areas where structures or critical infrastructure are most at risk or where other secondary effects may occur. This can be done before sending in expensive manpower to areas that may not yet be safe. Moreover, those who are submitting information do so in the knowledge that they are contributing to a faster and more efficient response, providing vital information about where resource can be most effectively used, and, in return, closing the intelligence loop, receive situational awareness information about their immediate environment. We present an initial situational awareness framework for earthquake management that encompasses the preparedness, response and recovery phases. It is envisaged that this framework will help develop more effective risk assessment and management frameworks for structures and critical infrastructure (e.g. industrial facilities)

The ethics of digital well-being: a multidisciplinary perspective

This chapter serves as an introduction to the edited collection of the same name, which includes chapters that explore digital well-being from a range of disciplinary perspectives, including philosophy, psychology, economics, health care, and education. The purpose of this introductory chapter is to provide a short primer on the different disciplinary approaches to the study of well-being. To supplement this primer, we also invited key experts from several disciplines—philosophy, psychology, public policy, and health care—to share their thoughts on what they believe are the most important open questions and ethical issues for the multi-disciplinary study of digital well-being. We also introduce and discuss several themes that we believe will be fundamental to the ongoing study of digital well-being: digital gratitude, automated interventions, and sustainable co-well-being

Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations

ME-EM 2018-19 Annual Report

Table of Contents Faculty Research Enrollment & Degrees Department News Graduates Faculty & Staff Alumni Donors Contracts & Grants Patents & Publicationshttps://digitalcommons.mtu.edu/mechanical-annualreports/1000/thumbnail.jp

Knowledge Components and Methods for Policy Propagation in Data Flows

Data-oriented systems and applications are at the centre of current developments of the World Wide Web (WWW). On the Web of Data (WoD), information sources can be accessed and processed for many purposes. Users need to be aware of any licences or terms of use, which are associated with the data sources they want to use. Conversely, publishers need support in assigning the appropriate policies alongside the data they distribute. In this work, we tackle the problem of policy propagation in data flows - an expression that refers to the way data is consumed, manipulated and produced within processes. We pose the question of what kind of components are required, and how they can be acquired, managed, and deployed, to support users on deciding what policies propagate to the output of a data-intensive system from the ones associated with its input. We observe three scenarios: applications of the Semantic Web, workflow reuse in Open Science, and the exploitation of urban data in City Data Hubs. Starting from the analysis of Semantic Web applications, we propose a data-centric approach to semantically describe processes as data flows: the Datanode ontology, which comprises a hierarchy of the possible relations between data objects. By means of Policy Propagation Rules, it is possible to link data flow steps and policies derivable from semantic descriptions of data licences. We show how these components can be designed, how they can be effectively managed, and how to reason efficiently with them. In a second phase, the developed components are verified using a Smart City Data Hub as a case study, where we developed an end-to-end solution for policy propagation. Finally, we evaluate our approach and report on a user study aimed at assessing both the quality and the value of the proposed solution

Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications

Blended learning environments to foster self-directed learning

This book on blended learning environments to foster self-directed learning highlights the focus on research conducted in several teaching and learning contexts where blended learning had been implemented and focused on the fostering of self-directed learning. Several authors have contributed to the book, and each chapter provides a unique perspective on blended learning and self-directed learning research. From each chapter, it becomes evident that coherence on the topics mentioned is established. One of the main aspects drawn in this book, and addressed by several authors in the book, is the use of the Community of Inquiry (CoI) framework when implementing teaching and learning strategies in blended learning environments to foster self-directed learning. This notion of focusing on the CoI framework is particularly evident in both theoretical and empirical dissemination presented in this book. What makes this book unique is the fact that researchers and peers in varied fields would benefit from the findings presented by each chapter, albeit theoretical, methodological or empirical in nature – this, in turn, provides opportunities for future research endeavours to further the narrative of how blended learning environments can be used to foster self-directed learning

Contelog: A Formal Declarative Framework for Contextual Knowledge Representation and Reasoning

Context-awareness is at the core of providing timely adaptations in safety-critical secure applications of pervasive computing and Artificial Intelligence (AI) domains. In the current AI and application context-aware frameworks, the distinction between knowledge and context are blurred and not formally integrated. As a result, adaptation behaviors based on contextual reasoning cannot be formally derived and reasoned about. Also, in many smart systems such as automated manufacturing, decision making, and healthcare, it is essential for context-awareness units to synchronize with contextual reasoning modules to derive new knowledge in order to adapt, alert, and predict. A rigorous formalism is therefore essential to (1) represent contextual domain knowledge as well as application rules, and (2) efficiently and effectively reason to draw contextual conclusions. This thesis is a contribution in this direction. The thesis introduces first a formal context representation and a context calculus used to build context models for applications. Then, it introduces query processing and optimization techniques to perform context-based reasoning. The formal framework that achieves these two tasks is called Contelog Framework, obtained by a conservative extension of the syntax and semantics of Datalog. It models contextual knowledge and infers new knowledge. In its design, contextual knowledge and contextual reasoning are loosely coupled, and hence contextual knowledge is reusable on its own. The significance is that by fixing the contextual knowledge, rules in the program and/or query may be changed. Contelog provides a theory of context, in a way that is independent of the application logic rules. The context calculus developed in this thesis allows exporting knowledge inferred in one context to be used in another context. Following the idea of Magic sets from Datalog, Magic Contexts together with query rewriting algorithms are introduced to optimize bottom-up query evaluation of Contelog programs. A Book of Examples has been compiled for Contelog, and these examples are implemented to showcase a proof of concept for the generality, expressiveness, and rigor of the proposed Contelog framework. A variety of experiments that compare the performance of Contelog with earlier Datalog implementations reveal a significant improvement and bring out practical merits of current stage of Contelog and its potential for future extensions in context representation and reasoning of emerging applications of context-aware computing