This is a sandwich thesis / In an interrupted time series (ITS) design, data are collected at multiple time points before and after the implementation of an intervention or program to investigate the effect of the intervention on an outcome of interest. ITS design is often implemented in healthcare settings and is considered the strongest quasi-experimental design in terms of internal and external validity as well as its ability to establish causal relationships. There are several statistical methods that can be used to analyze data from ITS studies. Nevertheless, limitations exist in practical applications, where researchers inappropriately apply the methods, and frequently ignore the assumptions and factors that may influence the optimality of the statistical analysis. Moreover, there is little to no guidance available regarding the application of the various methods, and a standardized framework for analysis of ITS studies does not exist. As such, there is a need to identify and compare existing ITS methods in terms of their strengths and limitations. Their methodological challenges also need to be investigated to inform and direct future research. In light of this, this PhD thesis addresses two main objectives: 1) to conduct a scoping review of the methods that have been employed in the analysis of ITS studies, and 2) to develop improved methods that address a major limitation of the statistical methods frequently used in ITS data analysis. These objectives are addressed in three projects. For the first project, a scoping review of the methods that have been used in analyzing ITS data was conducted, with the focus on ITS applications in health research. The review was based on the Arksey and O’Malley framework and the Joanna Briggs Handbook for scoping reviews. A total of 1389 studies were included in our scoping review. The articles were grouped into methods papers and applications papers based on the focus of the article. For the methods papers, we narratively described the identified methods and discussed their strengths and limitations. The application papers were summarized using frequencies and percentages. We identified some limitations of current methods and provided some recommendations useful in health research. In the second project, we developed and presented an improved method for ITS analysis when the data at each time point are aggregated across several participants, which is the most common case in ITS studies in healthcare settings. We considered the segmented linear regression approach, which our scoping review identified as the most frequently used method in ITS studies. When data are aggregated, heterogeneity is introduced due to variability in the patient population within sites (e.g. healthcare facilities) and this is ignored in the segmented linear regression method. Moreover, statistical uncertainty (imprecision) is introduced in the data because of the sample size (number of participants from whom data are aggregated). Ignoring this variability and uncertainty will likely lead to invalid estimates and loss of statistical power, which in turn leads to erroneous conclusions. Our proposed method incorporates patient variability and sample size as weights in a weighted segmented regression model. We performed extensive simulations and assessed the performance of our method using established performance criteria, such as bias, mean squared error, level and statistical power. We also compared our method with the segmented linear regression approach. The results indicated that the weighted segmented regression was uniformly more precise, less biased and more powerful than the segmented linear regression method. In the third project, we extended the weighted method to multisite ITS studies, where data are aggregated at two levels: across several participants within sites as well as across multiple sites. The extended method incorporates the two levels of heterogeneity using weights, where the weights are defined using patient variability, sample size, number of sites as well as site-to-site variability. This extended weighted regression model, which follows the weighted least squares approach is employed to estimate parameters and perform significance testing. We conducted extensive empirical evaluations using various scenarios generated from a multi-site ITS study and compared the performance of our method with that of the segmented linear regression model as well as a pooled analysis method previously developed for multisite studies. We observed that for most scenarios considered, our method produced estimates with narrower 95% confidence intervals and smaller p-values, indicating that our method is more precise and is associated with more statistical power. In some scenarios, where we considered low levels of heterogeneity, our method and the previously proposed method showed comparable results. In conclusion, this PhD thesis facilitates future ITS research by laying the groundwork for developing standard guidelines for the design and analysis of ITS studies. The proposed improved method for ITS analysis, which is the weighted segmented regression, contributes to the advancement of ITS research and will enable researchers to optimize their analysis, leading to more precise and powerful results. / Thesis / Doctor of Philosophy (PhD)

<p dir="ltr">Software Package Registries are an integral part of the Software Supply Chain, acting as collaborative platforms that unite contributors, users, and packages, and streamline package management processes. Much of the engineering work around reusing packages from these platforms deals with the issue of synthesis, combining multiple packages into a new package or downstream project. Recently, researchers have examined registries that specialize in providing Pre-Trained Models (PTMs), to explore the nuances of the PTM Supply Chain. These works suggest that the main engineering challenge of PTM reuse is not synthesis but selection. However, these findings have been primarily qualitative and lacking quantitative evidence of the observed differences. I therefore evaluate the following hypothesis:</p><p dir="ltr"><i>The prioritization of selection over synthesis in Pre-Trained Model reuse means that the evolution and reuse of Pre-Trained Models differs compared to traditional software. </i><i>The evolution of models will be more linear, and the reuse of models will be more centralized.</i></p>

In today's digital age, social media platforms have become powerful tools for communication, enabling users to express their opinions while also exposing them to various forms of hateful speech and content. While prior research has often focused on the efficacy of online counterspeech, little is known about peoples' motivations for engaging in it. Based on a survey of 458 U.S. participants, we develop and validate a multi-item scale for understanding counterspeech motivations, revealing that differing motivations impact counterspeech engagement between those that do and not find counterspeech to be an effective mechanism for counteracting online hate. Additionally, our analysis explores peoples' perceived effectiveness of their self-written counterspeech to hateful posts, influenced by individual motivations to engage in counterspeech and demographic factors. Finally, we examine peoples' willingness to employ AI assistance, such as ChatGPT, in their counterspeech writing efforts. Our research provides insight into the factors that influence peoples' online counterspeech activity and perceptions, including the potential role of AI assistance in countering online hate. / Master of Science / In today's digital age, social media platforms have become powerful tools for communication, enabling users to express their opinions while also exposing them to various forms of hateful speech and content. In addition to content moderation, counterspeech, or direct responses aimed at undermining hateful speech, is a tool that is being explored by organizations to counteract online hate, as it has been shown to prevent "platform hopping" while also promoting free speech. While prior research has primarily focused on the effectiveness of various types of counterspeech, little is known about what motivates people to engage in it. Based on a survey of 458 U.S. participants, we develop and validate a multi-item scale for understanding counterspeech motivations, revealing that differing motivations impact counterspeech engagement between those that do and not find counterspeech to be an effective mechanism for counteracting online hate. Additionally, our analysis explores peoples' perceived effectiveness of their counterspeech, influenced by individual motivations to engage in counterspeech and demographic factors. Finally, we examine peoples' willingness to employ AI assistance, such as ChatGPT, in their counterspeech writing efforts. Our research provides insight into the factors that influence peoples' online counterspeech activity and perceptions, including the potential role of AI assistance in countering online hate.

El problema del taller de flujo surge hace unos 60 años como una aproximación de la realidad de los procesos industriales de fabricación, más exactamente de la programación de la producción. La programación de la producción se refiere a la ordenación de las tareas productivas pendientes en una industria fabril. A pesar de que han pasado muchos años desde sus comienzos, aun hoy existe una gran diferencia entre los problemas teóricos propuestos y la realidad industrial de las empresas. Una de las diferencias más evidentes es el hecho de que al intentar resolver un problema de programación de la producción casi nunca se tiene en mente un único objetivo. Normalmente se tienen en mente varias cosas a la vez, como por ejemplo, terminar cuanto antes la producción, al mismo tiempo maximizar el uso de recursos y también cumplir con las fechas de entregas. En este contexto han surgido los problemas de taller de flujo multi-objetivo. En los últimos 20 años los problemas de taller de flujo multi-objetivo han tenido un gran empuje, acercado el desarrollo teórico a los problemas reales. En este trabajo de tesis presentaremos un recorrido por algunos de los problemas de taller de flujo multi-objetivo, partiendo desde los más básicos y yendo hacia los más complejos, y al mismo tiempo, los que reflejan mejor la realidad. Este trabajo tiene además otros objetivos. Uno de los problemas que más se ha dejado de lado en la optimización multi-objetivo es la medición y comparación correcta de los resultados. Presentaremos un recorrido por los métodos existentes para la medición de resultados multi-objetivo, señalando los problemas y ventajas de cada uno, con la finalidad de obtener una metodología válida, clara y consistente para la comparación de los resultados de problemas multi-objetivo. Para comenzar el recorrido por el taller de flujo planteamos una tarea que nunca se ha llevado a cabo hasta la fecha: la implementación y comparación experimental de 23 algoritmos multi-objetivo. Alguno de ellos propuestos para el taller de flujo multi-objetivo y otros de carácter general. Esto nos dará un importante punto de partida para conocer las metodologías existentes en la literatura parar resolver problemas multiobjetivo. Como resultado conoceremos metodologías que van desde algoritmos genéticos, pasando por la búsqueda tabú, colonias de hormigas, recocido simulado, etc. Todo este trabajo inicial nos permitirá ver las ventajas y desventajas de cada método propuesto y determinar los puntos fuertes de los mejores para, finalmente, proponer un método de resolución de problemas de taller de flujo general, eficaz y eficiente. El recorrido por distintos problemas de taller de flujo nos permitirá conocer el estado actual de la literatura y acercarnos paso a paso a los problemas que mejor representan la realidad. En cada paso realizaremos un profundo estudio del estado actual de la literatura, comparando los métodos existentes contra un método propuesto por nosotros mismos. En este aspecto partiremos del problema del taller de flujo de permutación multi-objetivo, luego ampliaremos este problema añadiéndole tiempos de cambio dependientes de la secuencia y finalmente estudiaremos el problema del taller de flujo híbrido multi-objetivo. / Minella, GG. (2014). Optimización multi-objetivo para la programación de la producción [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37980

The first and most imperative step when designing a biologically inspired robot is to identify the underlying mechanics of the system or animal of interest. It is most common, perhaps, that this process generates a set of coupled nonlinear ordinary or partial differential equations. For this class of systems, the models derived from morphology of the skeleton are usually very high dimensional, nonlinear, and complex. This is particularly true if joint and link flexibility are included in the model. In addition to complexities that arise from morphology of the animal, some of the external forces that influence the dynamics of animal motion are very hard to model. A very well-established example of these forces is the unsteady aerodynamic forces applied to the wings and the body of insects, birds, and bats. These forces result from the interaction of the flapping motion of the wing and the surround- ing air. These forces generate lift and drag during flapping flight regime. As a result, they play a significant role in the description of the physics that underlies such systems. In this research we focus on dynamic and kinematic models that govern the motion of ground based robots that emulate flapping flight. The restriction to ground based biologically inspired robotic systems is predicated on two observations. First, it has become increasingly popular to design and fabricate bio-inspired robots for wind tunnel studies. Second, by restricting the robotic systems to be anchored in an inertial frame, the robotic equations of motion are well understood, and we can focus attention on flapping wing aerodynamics for such nonlinear systems. We study nonlinear modeling, identification, and control problems that feature the above complexities. This document summarizes research progress and plans that focuses on two key aspects of modeling, identification, and control of nonlinear dynamics associated with flapping flight. / Ph. D. / In this work we focus on modeling flapping flight mechanics by focusing our attention in two aspects of modeling. We first model the behavior of aerodynamic forces in charge of keeping the flying animal airborn. We present a mathematical model for history dependent profile of these forces. Also, we propose a novel adaptive controller to compensate these unknown forces in the dynamic model of the system. We also propose an algorithm to derive dynamic equations of the animal motion by using video data. We expect the model derived by this novel method to emulate the animal motion closely.

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第25380号 / 人博第1122号 / 新制||人||261(附属図書館) / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)准教授 金丸 敏幸, 教授 谷口 一美, 教授 守田 貴弘, 准教授 大谷 直輝 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DGAM

In the modern computing landscape, the challenge of tuning software systems is exacerbated by the necessity to accommodate multiple divergent execution environments and stakeholders. Achieving optimal performance requires a different configuration for every combination of hardware setups and business requirements. In addition, the state of the art in system tuning can involve complex statistical models and tools which require deep expertise not commonly possessed by the average software engineer. As an alternative approach to performance tuning, this thesis puts forward the use of sonification-conveying information via non-speech audio-to aid software engineers in tuning complex systems. In particular, this thesis designs, develops, and evaluates a tuning system that interactively (i.e., in response to user actions) sonifies the performance metrics of a computer system. This thesis demonstrates that interactive sonification can effectively guide software engineers through performance tuning of a computer system. To that end, a scientific survey determined which sound characteristics (e.g., loudness, panning, pitch, tempo, etc.) are best suited to express information to the engineer. These characteristics were used to create a proof-of-concept tuning system that was applied to tune the parameters of a real world enterprise application server. Equipped with the tuning system, engineers-not experts in enterprise computing nor performance tuning-were able to tune the server, so that its resulting performance surpasses that exhibited under the standard configuration. The results indicate that sound-based tuning approaches can provide valuable solutions to the challenges of configuring complex computer systems. / Master of Science

Evaluation of crash count data as a function of roadway characteristics allows Departments of Transportation to predict expected average crash risks in order to assist in identifying segments that could benefit from various treatments. Currently, the evaluation is performed using negative binomial regression, as a function of average annual daily traffic (AADT) and other variables. For this thesis, a crash study was carried out for the interstate, primary and secondary routes, in the Salem District of Virginia. The data used in the study included the following information obtained from Virginia Department of Transportation (VDOT) records: 2010 to 2012 crash data, 2010 to 2012 AADT, and horizontal radius of curvature (CV). Additionally, tire-pavement friction or skid resistance was measured using a continuous friction measurement, fixed-slip device called a Grip Tester. In keeping with the current practice, negative binomial regression was used to relate the crash data to the AADT, skid resistance and CV. To determine which of the variables to include in the final models, the Akaike Information Criterion (AIC) and Log-Likelihood Ratio Tests were performed. By mathematically combining the information acquired from the negative binomial regression models and the information contained in the crash counts, the parameters of each network's true average crash risks were empirically estimated using the Empirical Bayes (EB) approach. The new estimated average crash risks were then used to rank segments according to their empirically estimated crash risk and to prioritize segments according to their expected crash reduction if a friction treatment were applied. / Master of Science

This dissertation presents three essays that focus on various aspects pertaining to the management of online platforms, defined as "digital services that facilitate interactions between two or more distinct, but interdependent sets of users (whether firms or individuals) who interact through the service via the Internet" (OECD, 2019). The interactions benefit both the users and the platform. Managing online platforms involves developing strategies for one or more of three value adding functions: (a) lowering search costs for the parties connecting through the platform, (b) providing a technology infrastructure that facilitates transactions at scale by sharing both demand and supply side costs; and (c) locating other audiences or consumers for the output that results from the transaction. The platform manager must manage these value adding functions. Thus, one important management task is to recognize potential asymmetries in the economic and/or psychological motivations of the transacting parties connected through the platform. In this dissertation, I empirically examine these issues in greater detail. The first essay, "Incentivizing User-Generated Content—A Double-Edged Sword: Evidence from Field Data and a Controlled Experiment," addresses the conundrum faced by online platform managers interested in crowdsourcing user-generated content (UGC) in prosocial contexts. The dilemma stems from the fact that offering monetary incentives to stimulate UGC contributions also has a damping effect on peer approval, which is an important source of non-monetary recognition valued by UGC contributors in prosocial contexts. The second essay, "Matching and Making in Matchmaking Platforms: A Structural Analysis," examines matchmaking platforms, focusing specifically on the problem of misaligned incentives between the platform and the agents. Based on data from the Ultimate Fighting Championship (UFC) on fighter characteristics, and pay-per-view revenues associated with specific bouts, we identify the potential for conflicts of interest and examine strategies that may be used to mitigate such problems. The third essay, "Matching and Making in Matching Markets: A Managerial Decision Calculus," extends the empirical model and analytical work to a class of commonly encountered one-sided matching market problems. It provides the conceptual outline of a decision calculus that allows managers to explore the revenue and profitability implications of adaptive changes to the tier structures and matching algorithms. / Doctor of Philosophy / The 21st century has witnessed the rise of the platform economy. Consumers routinely interact with online platforms ways in their day to day activities. For instance, they interact with platforms such as Quora, StackOverflow, Uber, and Airbnb to name only a few. Such platforms address a variety of needs starting from providing users with answers to a variety of questions to matching them with a range of service providers (e.g., for travel and dining needs). However, the rapid growth of the platform economy has created a knowledge gap for both consumers and platforms. The three essays in this dissertation attempt to contribute to the literature in this area. The first essay, "Incentivizing User-Generated Content—A Double-Edged Sword: Evidence from Field Data and a Controlled Experiment," examines how crowdsourcing contests influence the quantity and quality of user-generated content (UGC). Analyzing data from the popular question and answer website Quora, we find that offering monetary incentives to stimulate UGC contributions increases contributions but also has a simultaneous damping effect on peer endorsement, which is an important source of non-monetary recognition for UGC contributors in prosocial contexts. The second essay, "Matching and Making in Matchmaking Platforms: A Structural Analysis," examines matchmaking platforms, focusing on the problem of misaligned incentives between the platform and the agents. Based on data from the Ultimate Fighting Championship (UFC) on fighter characteristics, and pay-per-view revenues associated with specific bouts, we identify the potential for conflicts of interest and examine strategies that may be used to mitigate such problems. The third essay, "Matching and Making in Matching Markets: A Managerial Decision Calculus," extends the empirical model and analytical work to a class of commonly encountered one-sided matching market problems. It provides the conceptual outline of a decision calculus that allows managers to explore the revenue and profitability implications of adaptive changes to the tier structures and matching algorithms.

In recent years, the design, fabrication, and control of robotic systems inspired by biology has gained renewed attention due to the potential improvements in efficiency, maneuverability, and adaptability with which animals interact with their environments. Motion studies of biological systems such as humans, fish, insects, birds and bats are often used as a basis for robotic system design. Often, these studies are conducted by recording natural motions of the system of interest using a few high-resolution, high-speed cameras. Such equipment enables the use of standard methods for corresponding features and producing three-dimensional reconstructions of motion. These studies are then interpreted by a designer for kinematic, dynamic, and control systems design of a robotic system. This methodology generates impressive robotic systems which imitate their biological counter parts. However, the equipment used to study motion is expensive and designer interpretation of kinematics data requires substantial time and talent, can be difficult to identify correctly, and often yields kinematic inconsistencies between the robot and biology. To remedy these issues, this dissertation leverages the use of low-cost, low-speed, low-resolution cameras for tracking bat flight and presents a methodology for automatically learning physical geometry which restricts robotic joints to a motion submanifold identified from motion capture data. To this end, we present a spatially recursive state estimator which incorporates inboard state correction for producing accurate state estimates of bat flight. Using these state estimates, we construct a Gaussian process dynamic model (GPDM) of bat flight which is the first nonlinear dimensionality reduction of flapping flight in bats. Additionally, we formulate a novel method for learning robotic joint geometry directly from the experimental observations. To do this, we leverage recent developments in learning theory which derive analytical-empirical potential energy fields for identifying an underlying motion submanifold. We use these energy fields to optimize a compliant structure around a single degree-of-freedom elbow joint and to design rigid structures around spherical joints for an entire bat wing. Validation experiments show that the learned joint geometry restricts the motion of the joints to those observed during experiment. / Ph. D. / In recent years, robots modeled after biological systems have become increasingly prevalent. Such robots are often designed based on motion capture experiments of the animal they aim to imitate. The motion studies are typically conducted using commercial motion capture systems such as ViconTM or OptiTrackTM or a few high-speed, high-resolution cameras such as those marketed by PhotronTM or PhantomTM. These systems allow for automated processing of video sequences into three-dimensional reconstructions of the biological motion using standard image processing and state estimation techniques. The motion data is then used to drive robotic system designs such as the SonyTM AiboTM dog and the Boston Dynamics Atlas humanoid robot. While the motion capture data forms a basis for these impressive robots, the progression from data to robotic system is neither algorithmic nor rigorous and requires substantial interpretation by a human. In contrast, this dissertation presents a novel experimental and computational framework which uses low-speed, low-resolution cameras for capturing the complex motion of bats in flight and introduces a methodology which uses the motion capture data to directly design geometry which restricts the motion of joints to the motions observed in experiment. The advantage of our method is that the designer only needs to specify a general joint geometry such as a ball or pin joint, and geometry which restricts the motion is automatically identified. To do this, we learn an energy field over the set of kinematic configurations observed during experiment. This energy field “pushes” system trajectories towards those experimentally observed trajectories. We then learn compliant or rigid geometry which approximates this energy field to physically restrict the range of motion of the joint. We validate our method by fabricating joint geometry designed using both these approaches and present experiments which confirm that the reachable set of the joint is approximately the same as the set of configurations observed during experiments.