556 research outputs found
Grounds for a Third Place : The Starbucks Experience, Sirens, and Space
My goal in this dissertation is to help demystify or âfilterâ the âStarbucks Experienceâ for a post-pandemic world, taking stock of how a multi-national company has long outgrown its humble beginnings as a wholesale coffee bean supplier to become a digitally-integrated and hypermodern cafĂ©. I look at the role Starbucks plays within the larger cultural history of the coffee house and also consider how Starbucks has been idyllically described in corporate discourse as a comfortable and discursive âthird placeâ for informal gathering, a term that also prescribes its own radical ethos as a globally recognized customer service platform. Attempting to square Starbucksâ iconography and rhetoric with a new critical methodology, in a series of interdisciplinary case studies, I examine the role Starbucksâ âthird placeâ philosophy plays within larger conversations about urban space and commodity culture, analyze Starbucks advertising, architecture and art, and trace the mythical rise of the Starbucks Siren (and the reiterations and re-imaginings of the Starbucks Siren in art and media). While in corporate rhetoric Starbucksâ âthird placeâ is depicted as an enthralling adventure, full of play, discovery, authenticity, or âromance,â I draw on critical theory to discuss how it operates today as a space of distraction, isolation, and loss
Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches
Traditional networking devices support only fixed features and limited configurability.
Network softwarization leverages programmable software and hardware platforms to remove those limitations.
In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms.
This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0.
P4 is the most popular technology to implement programmable data planes.
However, programmable data planes, and in particular, the P4 technology, emerged only recently.
Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking.
The research of this thesis focuses on two open issues of programmable data planes.
First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet.
Second, it enables BIER in high-performance P4 data planes.
BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet.
The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study.
Two more peer-reviewed papers contain additional content that is not directly related to the main results.
They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts
Evaluating cognitive load of text-to-speech synthesis
This thesis addresses the vital topic of evaluating synthetic speech and its impact on the end-user, taking into consideration potential negative implications on cognitive load. While conventional methods like transcription tests and Mean Opinion Scores (MOS) tests offer a valuable overall understanding of system performance, they fail to provide deeper insights into the reasons behind the performance. As text-to-speech (TTS) systems are increasingly used in real-world applications, it becomes crucial to explore whether synthetic speech imposes a greater cognitive load on listeners compared to human speech, as excessive cognitive effort could lead to fatigue over time. The study focuses on assessing the cognitive load of synthetic speech by presenting two methodologies: the dual-task paradigm and pupillometry. The dual-task paradigm initially seemed promising but was eventually deemed unreliable and unsuitable due to uncertainties in experimental setups which requires further investigation. However, pupillometry emerged as a viable approach, demonstrating its efficacy in detecting differences in cognitive load among various speech synthesizers. Notably, the research confirmed that accurate measurement of listening difficulty requires imposing sufficient cognitive load on listeners. To achieve this, the most viable experimental setup involved measuring the pupil response while listening to speech in the presence of noise. Through these experiments, intriguing contrasts between human and synthetic speech were revealed. Human speech consistently demanded the least cognitive load. On the other hand, state-of-the-art TTS systems showed promising results, indicating a significant improvement in their cognitive load performance compared to rule-based synthesizers of the past. Pupillometry offers a deeper understanding of the contributing factors to increased cognitive load in synthetic speech processing. Particularly, an experiment highlighted that the separate modeling of spectral feature prediction and duration in TTS systems led to heightened cognitive load. However, encouragingly, many modern end-to-end TTS systems have addressed these issues by predicting acoustic features within a unified framework, and thus effectively reducing the overall cognitive load imposed by synthetic speech. As the gap between human and synthetic speech diminishes with advancements in TTS technology, continuous evaluation using pupillometry remains essential for optimizing TTS systems for low cognitive load. Although pupillometry demands advanced analysis techniques and is time-consuming, the meaningful insights it provides into the cognitive load of synthetic speech contribute to an enhanced user experience and better TTS system development. Overall, this work successfully establishes pupillometry as a viable and effective method for measuring cognitive load of synthetic speech, propelling synthetic speech evaluation beyond traditional metrics. By gaining a deeper understanding of synthetic speech's interaction with the human cognitive processing system, researchers and developers can work towards creating TTS systems that offer improved user experiences with reduced cognitive load, ultimately enhancing the overall usability and acceptance of such technologies.
Note: There was a 2-year break in the work reported in this thesis where an initial pilot was performed in early 2020 and was then suspended due to the covid-19 pandemic. Experiments were therefore rerun in 2022/23 with the most recent state-of-the-art models so that we could determine whether the increased cognitive load result is still applicable. This thesis was thus concluded by answering whether such cognitive load methods developed in this thesis are still useful, practical and/or relevant for current state-of-the-art text-to-speech systems
Online receding horizon planning of multi-contact locomotion
Legged robots can traverse uneven terrain by using multiple contacts between their limbs and the environment. Nevertheless, to enable reliable operation in the real world, legged robots necessarily require the capability to online re-plan their motions in response to changing conditions, such as environment changes, or state deviations due to external force perturbations. To approach this goal, Receding Horizon Planning (RHP) can be a promising solution. RHP refers to the planning framework that can constantly update the motion plan for immediate execution. To achieve successful RHP, we typically need to consider an extended planning horizon, which consists of an execution horizon that plans the motion to be executed, and a prediction horizon that foresees the future. Although the prediction horizon is never executed, it is important to the success of RHP. This is because the prediction horizon serves as a value function approximation that evaluates the feasibility and the future effort required for accomplishing the given task starting from a chosen robot state. Having such value information can guide the execution horizon toward the states that are beneficial for the future.
Nevertheless, computing such multi-contact motions for a legged robot to traverse uneven terrain can be time-consuming, especially when considering a long planning horizon. The computation complexity typically comes from the simultaneous resolution of the following two sub-problems: 1) selecting a gait pattern that specifies the sequence in which the limbs break and make contact with the environment; 2)synthesizing the contact and motion plan that determines the robot state trajectory along with the contact plan, i.e., contact locations and contact timings. The issue of gait pattern selection introduces combinatorial complexity into the planning problem,while the computation of the contact and motion plan brings high-dimensionality and non-convexity due to the consideration of complex non-linear dynamics constraints.
To facilitate online RHP of multi-contact motions, in this thesis, we focus on exploring novel methods to address these two sub-problems efficiently. To give more detail, we firstly consider the problem of planning contact and motion plans in an online receding horizon fashion. In this case, we pre-specifying the gait pattern as a priori. Although this helps us to avoid the combinatorial complexity, the resulting planning problem is still high-dimensional and non-convex, which can hinder online computation. To improve the computation speed, we propose to simplify the modeling of the value function approximation that is required for guiding the RHP. This leads to 1) Receding Horizon Planning with Multiple Levels of Model Fidelity, where we compute the prediction horizon with a convex relaxed model; 2) Locally- Guided Receding Horizon Planningâwhere we propose to learn an oracle to predict local objectives (intermediate goals) for completing a given task, and then we use these local objectives to construct local value functions to guide a short-horizon RHP. We evaluate our methods for planning centroidal trajectories of a humanoid robot walking on moderate slopes as well as large slopes where static stability cannot be maintained.The result of multi-fidelity RHP demonstrates that we can accelerate the computation speed by relaxing the model accuracy in the prediction horizon. However, the relaxation cannot be arbitrary. Furthermore, owing to the shortened planning horizon, we find that locally-guided RHP demonstrates the best computation efficiency (95%-98.6%cycles converge online). This computation advantage enables us to demonstrate online RHP for our real-world humanoid robot Talos walking in dynamic environments that change on-the-fly.
To handle the combinatorial complexity that arises from the gait pattern selection issue, we propose the idea of constructing a map from the task specifications to the gait pattern selections for a given environment model and performance objective(cost). We show that for a 2D half-cheetah model and a quadruped robot, a direct mapping between a given task and an optimal gait pattern can be established. We use supervised learning to capture the structure of this map in the form of gait regions.Furthermore, we also find that the trajectories in each gait region are qualitatively similar. We utilize this property to construct a warm-starting trajectory for each gait region, i.e., the mean of the trajectories discovered in each region. We empirically show that these warm-starting trajectories can improve the computation speed of our trajectory optimization problem up to 60 times when compared with random initial guesses. Moreover, we also conduct experimental trials on the ANYmal robot to validate our method
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
- âŠ