Experiment Design. The main goal of the PMM experiment is to test an adaptive follow-me media streaming services across multiple devices and locations in the Smart City. This goal translates into investigating how the FLAME platform can impact on personalized media distribution with a specific interest in four main aspects: FLAME platform mechanisms for intelligent service endpoint management Dynamic service routing to direct traffic to the most appropriate local service instance Support for cross-layer optimizations and reduction of network traffic through localization of traffic wherever possible Support of fully secured surrogate media distribution service endpoints A detailed description of the experiment rationale, narrative storylines, stakeholders and requirements has been provided in Deliverable D3.1: FMI Vision, Use Cases and Scenarios (v1.1) [1]. Starting from that standpoint, the PMM experiment has been designed to cover incrementally up to three scenarios to be deployed in the City of Barcelona with increasing system complexity and number of involved end-users: Scenario 1: PMM distribution in walking areas in Barcelona, i.e. my screen & preferences follow me from home to my smart hand-held devices to continue media consumption while walking in the Smart City FLAME-empowered Smart City Infrastructure Figure 3: PMM Experiment Scenario 1 -Distribution of personal media in walking areas in Xxxxxxxxx Xxxxxxxx 0: PMM on aggregation areas of the Smart City, i.e. my media follow me also in aggregation area (e.g. shop, cafeteria, and mall), and surrogate functions for media distribution are allocated in edge nodes for more users. FLAME-empowered Smart City Infrastructure Figure 4: PMM Experiment Scenario 2 -Distribution of personal media in aggregation areas of the Smart City Scenario 3: PMM in digital signage posts, i.e. access to media contents from large public events in the Smart City at digital signage posts and swipe them in the personal device. FLAME-empowered Smart City Infrastructure Figure 5: PMM Experiment Scenario 3 -Distribution of public media contents in digital signage posts in the Smart City The planned experiment size and key demonstration steps are described in the following Error! Reference source not found. for the three scenarios. Table 2: Characteristics of the PMM experiment scenarios Experiment scenario # Scale Key demonstration points 1 Very Small 1-5 users “My screen follows me” from home to smart hand-held devices in the Smart cit...
Experiment Design. The goal is to explore how to leverage FLAME media services to enable a user to experience a branching, interactive narrative within a branching city environment as depicted in Figure 7. This section describes several of the system capabilities influencing the design of the validation experiment. Figure 7: Experience a branching, interactive narrative within a branching city environment.
Experiment Design. Gnome Trader involves several novel game mechanics which can benefit from a high performance underlying network. Played by users during their daily commutes, Gnome Trader requires the network to adapt to the variations of positions and densities of players. Moreover, these moving players request access to large data such as 3D models and expect to experience an uninterrupted gameplay. As a consequence, the requested content should be available close to the players’ locations, and, in some cases, even follow the players as they move in the city. Overall, there are many ways in which the Gnome Trader game mechanics depend on the performance of the network. The experiments presented in this section have a dual goal of assessing the performance of the FLAME platform, and ensuring the game requirements are met to provide the best player experience.
Experiment Design. The videos were assessed through a video HRI study. The study was designed as a 1 by 3 between-participants design with random assignment of participants. The independent variable is the sound condition which has three levels: no sound, a simple beeping sound, or the use of musical utterance. The “no sound” condition serves as a baseline to see to what extent the situational context signals the robot's intent. We also included a condition where the robot would use a simple beeping sound to assess whether the complexity of music improves emotion elicitation or intent communication.
Experiment Design. We designed a three-hour qualitative study aimed at designing creative and easily understandable robot movements when dealing with social errors. To achieve this, we collaborated with movement experts such as dancers and improv actors. We tasked them with portraying both the Harmony robot and hospital stakeholders, acting out three distinct social error scenarios that the robot might encounter while navigating the corridors. These scenarios included a navigation mistake, interrupting the hospital's calmness, and not meeting people's social expectations. To control the expressive range of the performers when portraying the robot's role, we imposed limitations on their modalities, such as allowing them to use only one arm or restricting changes to their shape and size. Additionally, we are also interested in looking at how the performers make use of the robot's sound modalities when communicating with the "stakeholders," as this could inspire future studies on the IDM Harmony robot's sound.
Experiment Design. The experiment began with the assignment of Treatment and Control groups. An in- dividual would be assigned to treatment groups if he received job-related training or self-improvement in skills & knowledge within three months before the survey. The remaining individuals would be automatically assigned to control groups. Figure 3.1 presents the details of eight treatment-control pais.
Experiment Design. Definition of the goals At the outset of these experiments there are number of ways in which the emergence of such synergies might be observed. For instance, the coordination of articulatory gestures, resulting in different vowel qualities, could be studied in combination with situations where the infant would also be required to achieve a certain voicing fundamental frequency (F0) or maintaining the vocalization’s acoustic level within certain limits. However, given the infant’s typically short attention span during a controlled experimental situation and some experience from pilot experiments, it was decided to focus on a relatively simple vocal parameter that would allow disclosing whether the infant could understand the consequences of its vocalizations. Thus, the specific synergy that will be considered in this report is that expressed by the coordination of aerodynamic and mioelastic phonation parameters necessary to control the vocalizations’ F0 in order plan for and obtain expected frequencies. Measuring the infant’s ability to control F0 The basic methodology consisted in creating experimental situations in which the subjects are offered the opportunity of discovering hidden contingencies between their own vocal (or motor) actions and visual events that have been linked to certain variables associated with those actions. Specifically the x-coordinate on which a figure appears on a computer screen was made dependent on the instantaneous pitch of the infant’s utterance. A logarithmic function mapping the relation between the vocalization’s pitch and an a priori defined “reference pitch” was used. This function was defined so that -1.6 octaves relative to the reference pitch would map onto a location on the left end side of the screen and +1.6 octaves on the screen’s right end side. The figure moved along a horizontal line at about 50% of the screen’s height.1 If subjects succeed in capturing the “hidden link” between their fundamental frequencies and the screen coordinates on which the visual object will be displayed, their gaze fixation points are expected to anticipate the coordinate on which the visual object is going to be displayed. By studying the relative timing between the appearance of the visual object at a given location and the gaze orientation towards that location, it may be possible to achieve a model of how infants develop the capacity to control their F0 and of predicting consequences of their vocal actions, if possible, as a funct...
Experiment Design. At the specified time and date of the experiment, participants were invited to remotely log into a project PC that was connected to the university network using TeamViewer (a software application for remote control, desktop sharing). For this purpose, a Dell Alienware Aurora R8 was set up as follows: IntelⓇ Core™ i7-6700K CPU @4 GHz, 64GB RAM, 2 x GeForce RTX 2080 Super (Base clock: 1650 MHz, 8 Gb of GDDR6 Memory, and 3,072 CUDA cores), running Windows 10 Pro (1904), Visual Studio 2017 version 15.9.17, and Unity version 2018.4.12f1 (LTS). An average internet provider speed of Up = 901.41 Mbps (SD = 41.08), Down = 521.46 (SD = 11.35), and Ping = 1.6 ms (SD = 0.49) were measured at the PC before each session. The study followed a two-step scenario testing strategy. The first scenario involved creating a simulated crowd scene and the second involved retargeting the crowd from the first scene to a semantically similar one. Two crowd simulation scenes were therefore created, each serving as a problem discovery measure for the new crowd simulation tools. This also allowed a comparison of re-targeting time between the two scenes. Users were provided with descriptions for what should be achieved in each scene, along with a brief video tutorial on how to use our tools in the Unity Editor. The tasks in the steps document were a decomposition of how to create the simulated crowd scene. Participants were allowed to ask questions about how to complete these tasks. Specific information on the result they should aim to achieve was also provided. The opportunity to discuss and analyze these procedures with a project researcher post-task ensured that the participants fully understood how the tools worked and could provide an informed evaluation. Participants were allowed a 30-minute break between the creation of each scene.
Experiment Design. Collecting a large enough sample of perceptual ratings to be able to evaluate both inter-rater agreement and human- automated reliability is challenging. For example, for lis- teners to rate 5 recordings requires making judgments of sets of individual features for 5 recordings (1, 2, 3, 4, 5), similarity for 10 different pairs (1 vs. 2, 1 vs. 3, 2 vs. 3, etc.) or 10 different triplets (1 vs. 2 vs. 3, 1 vs. 2 vs. 4, etc.), which takes approximately 30 minutes. How- ever, human judgments for only 5 recordings would not be enough to meaningfully compare with automated al- gorithms. On the other hand, increasing the sample to 10 recordings would require rating 10 sets of features, 45 pairs, and 120 triplets, which is already more than can be collected within the course of a 1-hour experiment, espe- cially when accounting for listener fatigue. If we attempt to spread out the data collection across multiple different participants by having different participants rate different recordings, we lose the ability to compare inter-rater agree- ment between participants. Unfamiliarity, use/absence of reference tracks, and order effects can also affect percep- tion of similarity. To balance the need for enough data to compare both human-human and human-automated agreement, we de- signed an experiment where we divided the set of 30 di- verse recordings previously used to evaluate inter-rater agreement into 6 sets of 5 recordings. For each set, we collected perceptual judgments of all possible features, pairs, and triplets from 10-11 participants per set (total n = 62 participants). The 62 participants were divided into 6 groups, where all members within each group rated the same 5 songs from the 30-song dataset. Each experiment lasted approximately 20-30 minutes and was divided into three blocks: feature evaluation, pair- wise evaluation and triplet (odd-one-out) evaluation. Be- fore beginning the experiment, participants are played a se- xxxx of reference tracks taken from the Cantometrics train- ing tapes in order to familiarize them with the features they would be rating and the types of recordings they would be asked to rate. Participants then evaluate a set of features for each song after listening to each song at least once, after which they performed the triplet and pairwise sim- ilarity tasks. The order of the triplet and pairwise blocks, and the order of songs/combinations within each block was randomized so as to negate order effects, but the feature evaluation...
