Game sound, as a feedback mechanism, functions along a diegetic continuum that bridges the boundary between player, game systems, and narrative. In narrative-driven strategy games, this continuum becomes especially important, as audio must communicate diegetically relevant information about story and character, while also supporting complex decision making. This raises key questions about how transdiegetic sound relates to design, narrative, and player experience for this genre. Specifically, how transdiegetic sound is incorporated into feedback systems in narrative-driven strategy games, and what kind of information they are used to communicate. In this paper, we examine the use of transdiegetic sound as form of feedback by analyzing 11 commercial narrative-focused strategy game titles. We map how such audio cues are designed for both narrative and gameplay functions, the specifics of how they traverse the diegetic boundaries, and discuss how these designs may shape player experience. Building on related work on transdiegetic sound in games, this study documents recurring design patterns and cues, and explores their communicative role within the context of narrative-driven strategy games.

We propose a new method to create plausible pedestrian crossing trajectories that cover a given arbitrarily shaped crosswalk area for simulation-based testing of autonomous vehicles. This method addresses the crossing area coverage problem where the trajectories produced by the generative methods do not cover the entire area that pedestrians may possibly walk on. The actual area covered by pedestrians often differs from marked crosswalks on the road. Furthermore, in the case of jaywalking, the area can take a variety of shapes based on the road structure and surrounding places of interest. Our method is a constructive process that generates trajectories conditioned on an area defined with polygons. We demonstrate that the method can generate trajectories that cover a wide range of crossing areas, including ones from the InD dataset.

In the field of data stream processing, there are two prevalent models, i.e., insertion-only, and turnstile models. Most previous works were proposed for the insertion-only model, which assumes new elements arrive continuously as a stream, and neglects the possibilities of removing existing elements. In this paper, we make a bounded deletion assumption, putting a constraint on the number of deletions allowed. For such a turnstile stream, we focus on a new problem of universal measurement that estimates multiple kinds of statistical metrics simultaneously using limited memory and in an online fashion, including per-element frequency, heavy hitters, frequency moments, and frequency distribution. There are two key challenges for processing a turnstile stream with bounded deletions. Firstly, most previous methods for detecting heavy hitters cannot ensure a bounded detection error when there are deletion events. Secondly, there is still no prior work to estimate the per-element frequency moments under turnstile model, especially in an online fashion. In this paper, we address the former challenge by proposing a Removable Augmented Sketch, and address the latter by a Removable Universal Sketch, enhanced with an Online Moment Estimator. In addition, we improve the accuracy of frequency estimation by a compressed counter design, which can halve the memory cost of a frequency counter and support addition/minus operations. Our experiments show that our solution outperforms other algorithms by 16%~69% in F1 Score of heavy hitter detection, and improves the throughput of frequency moment estimation by 3.0x10 4 times.

In computer networks, traffic measurement is a module in a network probe to measure flow-level statistics from an IP packet stream, which are the basis for network performance monitoring and malicious activity detection. This module extracts the flow IDs from incoming IP packets, classifies packets into flows, and counts the number of packets (or bytes) for each flow. It is a great challenge to measure the per-flow statistics for a high-speed network device, using only the size-limited SRAM on its line cards. Therefore, many algorithms using sublinear memory have been proposed, such as CountMin and CountSketch. However, most of previous algorithms are designed for specific measurement tasks. To obtain multiple types of statistics, people have to deploy multiple sketches, which demands more resources of a network device. It is useful to design a universal sketch that can track not only the top-$k$largest individual flows (called heavy hitters) but also the overall traffic distribution statistics (called moments). Prior work named UnivMon successfully tackled this ambitious quest. However, it incurs large and variable per-packet processing overhead, which may result in a significant throughput bottleneck in high-rate packet stream, given that each packet requires 33 hashes and 32 memory accesses on average and many times of that in the worst case. To address this performance issue, we fundamentally redesign the solution architecture from hierarchical sampling to new progressive sampling and from CountSketch to new GenericCM, which ensure that per-packet overhead is a small constant (5 hashes and 8 memory accesses in the worst case), making it more suitable for online operations, especially for hardware pipeline implementation. This new design also makes effort to reduce memory footprint or equivalently improve measurement accuracy under the same memory. Our experiments show that our solution reduces measurement error by roughly 98.1% for second-order moment and by 91.5% for entropy, when given the same 0.2MB memory as UnivMon.