Development of a decision support system for assessment of mobile bridges

The development of a prototype Decision Support System (DSS) for the condition assessment of the Armored Vehicle Launched Bridge (AVLB) has been demonstrated in the current work. AVLB is a mobile bridge that has been used by the US Army for tank and assault vehicle crossing. It is employed for spanning short gaps of 50 feet or less in the terrain. The condition assessment of such bridges, to ensure safety of personnel and tank, is of strategic importance. The methodology of the prototype DSS is based on a refined visual inspection procedure and a previously established vibration measurement technique.;Conforming to the design requirements, the DSS has been developed as an Internet based, interactive application, and is integrated with an automated vibration measurement system. The web-based DSS, which incorporates an expert system and a database system, can be run from a web-browser. The development of the system, as described in this thesis, involved: identification of important visual and vibration inspection parameters; development of an expert system for bridge condition assessment, based on these parameters; and design of a database for storing important inspection data and other vital bridge records. The prototype system has been validated through limited test runs. Discussion on further verification and validation issues has also been put forth in this work.;The unique feature of this DSS is its accessibility through the Internet, which is practical considering that the AVLB is deployed worldwide. With a central database, this system provides a useful research tool for further study of the AVLB behavior and damage mechanisms, and can be used to establish failure prediction model and remaining life assessment

Opportunities for using eye tracking technology in manufacturing and logistics: Systematic literature review and research agenda

Workers play essential roles in manufacturing and logistics. Releasing workers from routine tasks and enabling them to focus on creative, value-adding activities can enhance their performance and wellbeing, and it is also key to the successful implementation of Industry 4.0. One technology that can help identify patterns of worker-system interaction is Eye Tracking (ET), which is a non-intrusive technology for measuring human eye movements. ET can provide moment-by-moment insights into the cognitive state of the subject during task execution, which can improve our understanding of how humans behave and make decisions within complex systems. It also enables explorations of the subject’s interaction mode with the working environment. Earlier research has investigated the use of ET in manufacturing and logistics, but the literature is fragmented and has not yet been discussed in a literature review yet. This article therefore conducts a systematic literature review to explore the applications of ET, summarise its benefits, and outline future research opportunities of using ET in manufacturing and logistics. We first propose a conceptual framework to guide our study and then conduct a systematic literature search in scholarly databases, obtaining 71 relevant papers. Building on the proposed framework, we systematically review the use of ET and categorize the identified papers according to their application in manufacturing (product development, production, quality inspection) and logistics. Our results reveal that ET has several use cases in the manufacturing sector, but that its application in logistics has not been studied extensively so far. We summarize the benefits of using ET in terms of process performance, human performance, and work environment and safety, and also discuss the methodological characteristics of the ET literature as well as typical ET measures used. We conclude by illustrating future avenues for ET research in manufacturing and logistics

Model-based spreadsheet engineering

Tese de doutoramento em Informática (área Fundamentos da Computação)Spreadsheets can be viewed as programming languages for non-professional programmers. These so-called “end-user” programmers vastly outnumber professional programmers creating millions of new spreadsheets every year. As a programming language, spreadsheets lack support for abstraction, testing, encapsulation, or structured programming. As a result, and as numerous studies have shown, the high rate of production is accompanied by an alarming high rate of errors. Some studies report that up to 90% of real-world spreadsheets contain errors. After their initial creation, many spreadsheets turn out to be used for storing and processing increasing amounts of data and supporting increasing numbers of users over long periods of time, making them complicated systems. An emerging solution to handle the complex and evolving software systems is Model-driven Engineering (MDE). To consider models as first class entities and any software artifact as a model or a model element is one of the basic principles of MDE. We adopted some techniques from MDE to solve spreadsheet problems. Most spreadsheets (if not all) lack a proper specification or a model. Using reverse engineering techniques we are able to derive various models from legacy spreadsheets. We use functional dependencies (a formalism that allow us to define how some column values depend on other column values) as building blocks for these models. Models can be used for several spreadsheet improvements, namely refactoring, safe evolution, migration or even generation of edit assistance. The techniques presented in this work are available under the framework HAEXCEL that we developed. It is composed of online and batch tools, reusable HASKELL libraries and OpenOffice.org extensions. A study with several end-users was organized to survey the impact of the techniques we designed. The results of this study indicate that the models can bring great benefits to spreadsheet engineering helping users to commit less errors and to work faster

Collaborating through sounds: audio-only interaction with diagrams

PhDThe widening spectrum of interaction contexts and users’ needs continues to expose the limitations of the Graphical User Interface. But despite the benefits of sound in everyday activities and considerable progress in Auditory Display research, audio remains under-explored in Human- Computer Interaction (HCI). This thesis seeks to contribute to unveiling the potential of using audio in HCI by building on and extending current research on how we interact with and through the auditory modality. Its central premise is that audio, by itself, can effectively support collaborative interaction with diagrammatically represented information. Before exploring audio-only collaborative interaction, two preliminary questions are raised; first, how to translate a given diagram to an alternative form that can be accessed in audio; and second, how to support audio-only interaction with diagrams through the resulting form. An analysis of diagrams that emphasises their properties as external representations is used to address the first question. This analysis informs the design of a multiple perspective hierarchybased model that captures modality-independent features of a diagram when translating it into an audio accessible form. Two user studies then address the second question by examining the feasibility of the developed model to support the activities of inspecting, constructing and editing diagrams in audio. The developed model is then deployed in a collaborative lab-based context. A third study explores audio-only collaboration by examining pairs of participants who use audio as the sole means to communicate, access and edit shared diagrams. The channels through which audio is delivered to the workspace are controlled, and the effect on the dynamics of the collaborations is investigated. Results show that pairs of participants are able to collaboratively construct diagrams through sounds. Additionally, the presence or absence of audio in the workspace, and the way in which collaborators chose to work with audio were found to impact patterns of collaborative organisation, awareness of contribution to shared tasks and exchange of workspace awareness information. This work contributes to the areas of Auditory Display and HCI by providing empirically grounded evidence of how the auditory modality can be used to support individual and collaborative interaction with diagrams.Algerian Ministry of Higher Education and Scientific Research. (MERS

Machine-assisted mixed methods: augmenting humanities and social sciences with artificial intelligence

The increasing capacities of large language models (LLMs) present an unprecedented opportunity to scale up data analytics in the humanities and social sciences, augmenting and automating qualitative analytic tasks previously typically allocated to human labor. This contribution proposes a systematic mixed methods framework to harness qualitative analytic expertise, machine scalability, and rigorous quantification, with attention to transparency and replicability. 16 machine-assisted case studies are showcased as proof of concept. Tasks include linguistic and discourse analysis, lexical semantic change detection, interview analysis, historical event cause inference and text mining, detection of political stance, text and idea reuse, genre composition in literature and film; social network inference, automated lexicography, missing metadata augmentation, and multimodal visual cultural analytics. In contrast to the focus on English in the emerging LLM applicability literature, many examples here deal with scenarios involving smaller languages and historical texts prone to digitization distortions. In all but the most difficult tasks requiring expert knowledge, generative LLMs can demonstrably serve as viable research instruments. LLM (and human) annotations may contain errors and variation, but the agreement rate can and should be accounted for in subsequent statistical modeling; a bootstrapping approach is discussed. The replications among the case studies illustrate how tasks previously requiring potentially months of team effort and complex computational pipelines, can now be accomplished by an LLM-assisted scholar in a fraction of the time. Importantly, this approach is not intended to replace, but to augment researcher knowledge and skills. With these opportunities in sight, qualitative expertise and the ability to pose insightful questions have arguably never been more critical

Assessing and Improving Industrial Software Processes

Software process is a complex phenomenon that involves a multitude of different artifacts, human actors with different roles, activities to be performed in order to produce a software product. Even though the research community is devoting a great effort in proposing solutions aimed at improving software process, several issues are still open. In this Thesis work I propose different solutions for assessing and improving software processes carried out in real industrial contexts. More in detail, I proposed a solution, based on ALM and MDE, for supporting Gap Analysis processes for assessing if a software process is carried out in accordance with Standards or Evaluation Framework. Then, I focused on a solution based on tool integration for the management of trace links among the artifacts involved in the software process. As another contribution, I proposed a Reverse engineering process and a tool, named EXACT, for supporting the analysis and comprehension of spreadsheet based artifacts involved in software development processes. Finally, I realized a semi-automatic approach, named AutoMative, for supporting the introduction in real Industrial software processes of SPL for managing the variability of the software products to be developed. Case studies conducted in real industrial settings showed the feasibility and the positive impact of the proposed solutions on real industrial software processes

Computer detection of spatial visualization in a location-based task

An untapped area of productivity gains hinges on automatic detection of user cognitive characteristics. One such characteristic, spatial visualization ability, relates to users’ computer performance. In this dissertation, we describe a novel, behavior-based, spatial visualization detection technique. The technique does not depend on sensors or knowledge of the environment and can be adopted on generic computers. In a Census Bureau location-based address verification task, detection rates exceeded 80% and approached 90%