Improving User Involvement Through Live Collaborative Creation

Creating an artifact - such as writing a book, developing software, or performing a piece of music - is often limited to those with domain-specific experience or training. As a consequence, effectively involving non-expert end users in such creative processes is challenging. This work explores how computational systems can facilitate collaboration, communication, and participation in the context of involving users in the process of creating artifacts while mitigating the challenges inherent to such processes. In particular, the interactive systems presented in this work support live collaborative creation, in which artifact users collaboratively participate in the artifact creation process with creators in real time. In the systems that I have created, I explored liveness, the extent to which the process of creating artifacts and the state of the artifacts are immediately and continuously perceptible, for applications such as programming, writing, music performance, and UI design. Liveness helps preserve natural expressivity, supports real-time communication, and facilitates participation in the creative process. Live collaboration is beneficial for users and creators alike: making the process of creation visible encourages users to engage in the process and better understand the final artifact. Additionally, creators can receive immediate feedback in a continuous, closed loop with users. Through these interactive systems, non-expert participants help create such artifacts as GUI prototypes, software, and musical performances. This dissertation explores three topics: (1) the challenges inherent to collaborative creation in live settings, and computational tools that address them; (2) methods for reducing the barriers of entry to live collaboration; and (3) approaches to preserving liveness in the creative process, affording creators more expressivity in making artifacts and affording users access to information traditionally only available in real-time processes. In this work, I showed that enabling collaborative, expressive, and live interactions in computational systems allow the broader population to take part in various creative practices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145810/1/snaglee_1.pd

An architectural framework for expert identification based on social network analysis

Social network analysis has been widely used in different application contexts. For example, in Global Software Development (GSD), where multiple developers with diverse skills and knowledge are involved, the use of social networking models helps to understand how these developers collaborate. Finding experts who can help address critical elements or issues in a project is a challenging and critical task. It is especially true in the context of Global Software Development projects, where developers with specific skills and knowledge often need to be identified. In this sense, searching for essential members is a valuable task, as they are fundamental to the evolution of the network. This work proposes an architectural framework for expert identification as a hybrid solution that includes syntactic and semantic analysis in social networks. We seek to address research challenges related to designing recommendation systems when analyzing social structures in the Global Software Development context. In this solution, we define a model for the social network capable of capturing collaboration between developers, incorporate strategies for temporal analysis of the network, explore the network using machine learning algorithms, propose an ontology to enrich the data semantically, and consider a performative approach for high-volume social network analysis methods. We conducted four case studies using data extracted from GitHub to evaluate the proposed approach, as well as a more extensive dataset for the performance studies. The case studies provide evidence that our proposed method can identify specialists, highlighting their expertise and importance to the evolution of the social network.A análise de redes sociais tem sido amplamente utilizada em diferentes contextos de aplicação. Por exemplo, em Desenvolvimento Global de Software, onde vários desenvolvedores com diversos conhecimentos e habilidades estão envolvidos, o uso de modelos de redes sociais ajuda a entender como esses desenvolvedores colaboram. Encontrar especialistas que possam ajudar a abordar elementos ou problemas críticos em um projeto é uma tarefa desafiadora e crítica. Isso é especialmente verdade em projetos no contexto de Desenvolvimento Global de Software, onde desenvolvedores com habilidades e conhecimentos específicos geralmente precisam ser identificados. Nesse sentido, buscar membros essenciais é uma tarefa valiosa, pois eles são fundamentais para a evolução da rede. Este trabalho propõe um framework arquitetural para a identificação de especialistas como uma solução híbrida que inclui análise sintática e semântica em redes sociais. Buscamos abordar desafios de pesquisa relacionados ao projeto de sistemas de recomendação que envolvam a análise de estruturas sociais no contexto de Desenvolvimento Global de Software. Nesta solução, definimos um modelo para a rede social capaz de capturar a colaboração entre desenvolvedores, incorporamos estratégias de análise temporal da rede, exploramos a rede usando algoritmos de aprendizado de máquina, propomos uma ontologia para enriquecer os dados semanticamente e consideramos uma abordagem performativa para métodos de análise de redes de grande volume. Realizamos quatro estudos de caso usando dados extraídos do GitHub para avaliar a abordagem proposta, bem como um conjunto de dados de grande volume para os estudos de performance. Os estudos de caso fornecem evidências de que o método proposto pode identificar especialistas, destacando sua expertise e importância para a evolução da rede social.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Knowledge aggregation in people recommender systems : matching skills to tasks

People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives.Postprint (published version

Evaluating the Potential of Continuous Processes for Monoclonal Antibodies: Economic, Environmental and Operational Feasibility

The next generation of monoclonal antibody (mAb) therapies are under increasing pressure from healthcare providers to offer cost effective treatments in the face of intensified competition from rival manufacturers and the looming loss of patent exclusivity for a number of blockbusters. To remain completive in such a challenging environment companies are looking to reduce R&D and manufacturing costs by improving their manufacturing platform processes whilst maintaining flexibility and product quality. As a result companies are now exploring whether they should choose conventional batch technologies or invest in novel continuous technologies, which may lead to lower production costs. This thesis explores the creation of a dynamic tool as part of a decision-support framework that is capable of simulating and optimising continuous monoclonal antibody manufacturing strategies to assist decision-making in this challenging environment. The decision-support framework is able to tackle the complex problem domain found in biopharmaceutical manufacturing, through holistic technology evaluations employing deterministic discrete-event simulation, Monte Carlo simulation and multi-attribute decision-making techniques. The hierarchal nature of the framework (including a unique sixth hierarchal layer; sub-batches) made it possible to simulate multiple continuous manufacturing scenarios on a number of levels of detail, ranging from high-level process performance metrics to low-level ancillary task estimates. The framework is therefore capable of capturing the impact of future titres, multiple scales of operation and key decisional drivers on manufacturing strategies linking multiple continuous unit operations (perfusion cell culture & semi-continuous chromatography). The work in this thesis demonstrates that the framework is a powerful test bed for assessing the potential of novel continuous technologies and manufacturing strategies, via integrated techno-economic evaluations that take proof-of-concept experimental evaluations to complete life-cycle performance evaluations

Knowledge aggregation in people recommender systems : matching skills to tasks

People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives

Justification for Class 3 Permit Modification, Corrective Action Complete with Controls, Solid Waste Management Unit 76, Mixed Waste Landfill, Sandia National Laboratories/New Mexico, EPA ID Number NM5890110518 Volumes I through VIII

The Department of Energy/National Nuclear Security Administration (DOE) and Sandia Corporation (Sandia) are submitting a request for a Class 3 Modification to Module IV of Hazardous Waste Permit NM5890110518-1 (the Permit). DOE and Sandia are requesting that the New Mexico Environment Department (NMED) designate solid waste management unit (SWMU) 76 as approved for Corrective Action Complete status. NMED made a preliminary determination in October 2014 that corrective action is complete at this SWMU. SWMU 76, known as the Mixed Waste Landfill (MWL), is a 2.6-acre site at Sandia National Laboratories, located on Kirtland Air Force Base immediately southeast of Albuquerque, New Mexico. Radioactive wastes and mixed wastes (radioactive wastes that are also hazardous wastes) were disposed of in the MWL from March 1959 through December 1988. The meximum depth of burial is approximately 25 feet below the ground surface. Groundwater occurs approximately 500 feet below the ground surface at the MWL. DOE and Sandia have implemented corrective measures at SWMU 76 in accordance with the requirements of the Permit; an April 2004 Compliance Order on Consent between NMED, DOE, and Sandia; and the plans approved by NMED. On January 8, 2014, NMED approved a long-term monitoring and maintenance plan (LTMMP) for SWMU 76. DOE and Sandia have implemented the approved LTMMP, maintaining the controls established through the corrective measures. The permit modification request consists of a letter with two enclosures: 1. A brief history or corrective action at SWMU 76 2. An index of the supporting documents that comprise the justification for the permit modification request. The supporting documents are included in an 8-volume set: Justification for Class 3 Permit Modification for Corrective Action Complete With Controls, Solid Waste Management Unit 76, Mixed Waste Landfill. Volume/pages: I/858. II/420. III/556. IV/1128. V/848. VI/1110. VII/914. VIII/866

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Essays on Creative Ideation and New Product Design

Creative ideation, i.e., the generation of novel ideas, represents the terminus-a-quo in the design and development of innovative products. In my dissertation essays, I examine two approaches employed by firms for creative ideation, (1) channeled ideation, a closed approach, which involves applying replicable patterns or properties observed in historical innovations and (2) idea crowdsourcing, an open approach where firms invite crowds to contribute ideas to solve a specific challenge. In my studies, I clarify how firms can incorporate market-related information in the channeled ideation process and examine how the selection of ideas in crowdsourcing challenges relates to local and global novelty. In Essay 1, “Attribute Auto-dynamics and New Product Ideation,” I introduce a replicable property – attribute auto-dynamics, observed in several novel products, where a product possesses the ability to modify its attributes automatically in response to changing customer, product-system, or environmental conditions. I propose a typology of attribute auto-dynamics, based on an analysis of U.S. utility patents. Based on this typology, I specify a procedural framework for new product ideation that integrates market-pull relevant knowledge and technology-push relevant knowledge. I also illustrate how managers and product designers can apply the framework to identify new product ideas for specific target markets using a channeled ideation approach. In Essay 2, “Selection in Crowdsourced Ideation: Role of Local and Global Novelty,” I examine how the selection of ideas in crowdsourced challenges depends on the form of novelty – local or global. Firms often turn to idea crowdsourcing challenges to obtain novel ideas. Yet prior research cautions that ideators and seeker firms may not select novel ideas. To reexamine the links between idea novelty and selection, I propose a bi-faceted notion of idea novelty that may be local or global. Examining data on OpenIDEO, I find that the selection of novel ideas differs according to the selector, the form of novelty, and the challenge task structure. I also specify a predictive model that seeker firms can leverage when ideator selection metrics such as likes are unavailable.Doctor of Philosoph