Augmenting Mobility Simulation by Public Transport: A Case Study for the ONE Simulator

The Opportunistic Network Environment (ONE) simulator is an extensible tool for evaluating Delay-Tolerent Networking (DTN) protocols and applications under different types of mobility patterns. To further increase the reality of the public transportation system modeling, we enable the modeling for metro system by extending the ONE simulator into a multi-plane structure as a first order approximation for a 3-dimensional (3D) world modeling. As there are more and more public transit agencies open their timetable data to public, it is possible to utilize those open data and make the public transportation vehicles follow the real-world schedules. To achieve that, we developed tools for converting timetable data into the compatible format for the ONE simulator, and extended the movement model for public transport vehicles so that they can follow the provisioned schedule. This master’s thesis presents how the new features were designed, implemented and verified. In addition, we show sample simulations to demonstrate the impact of the new supported scenarios

Style-Consistent 3D Indoor Scene Synthesis with Decoupled Objects

Controllable 3D indoor scene synthesis stands at the forefront of technological progress, offering various applications like gaming, film, and augmented/virtual reality. The capability to stylize and de-couple objects within these scenarios is a crucial factor, providing an advanced level of control throughout the editing process. This control extends not just to manipulating geometric attributes like translation and scaling but also includes managing appearances, such as stylization. Current methods for scene stylization are limited to applying styles to the entire scene, without the ability to separate and customize individual objects. Addressing the intricacies of this challenge, we introduce a unique pipeline designed for synthesis 3D indoor scenes. Our approach involves strategically placing objects within the scene, utilizing information from professionally designed bounding boxes. Significantly, our pipeline prioritizes maintaining style consistency across multiple objects within the scene, ensuring a cohesive and visually appealing result aligned with the desired aesthetic. The core strength of our pipeline lies in its ability to generate 3D scenes that are not only visually impressive but also exhibit features like photorealism, multi-view consistency, and diversity. These scenes are crafted in response to various natural language prompts, demonstrating the versatility and adaptability of our model

StarNet: Style-Aware 3D Point Cloud Generation

This paper investigates an open research task of reconstructing and generating 3D point clouds. Most existing works of 3D generative models directly take the Gaussian prior as input for the decoder to generate 3D point clouds, which fail to learn disentangled latent codes, leading noisy interpolated results. Most of the GAN-based models fail to discriminate the local geometries, resulting in the point clouds generated not evenly distributed at the object surface, hence degrading the point cloud generation quality. Moreover, prevailing methods adopt computation-intensive frameworks, such as flow-based models and Markov chains, which take plenty of time and resources in the training phase. To resolve these limitations, this paper proposes a unified style-aware network architecture combining both point-wise distance loss and adversarial loss, StarNet which is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network that can effectively disentangle the Gaussian prior from input's high-level attributes in the mapped latent space to generate realistic interpolated objects. Experimental results demonstrate that our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks, but is more lightweight in model size, requires much fewer parameters and less time for model training

Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search

Increasingly complex and diverse deep neural network (DNN) models necessitate distributing the execution across multiple devices for training and inference tasks, and also require carefully planned schedules for performance. However, existing practices often rely on predefined schedules that may not fully exploit the benefits of emerging diverse model-aware operator placement strategies. Handcrafting high-efficiency schedules can be challenging due to the large and varying schedule space. This paper presents Tessel, an automated system that searches for efficient schedules for distributed DNN training and inference for diverse operator placement strategies. To reduce search costs, Tessel leverages the insight that the most efficient schedules often exhibit repetitive pattern (repetend) across different data inputs. This leads to a two-phase approach: repetend construction and schedule completion. By exploring schedules for various operator placement strategies, Tessel significantly improves both training and inference performance. Experiments with representative DNN models demonstrate that Tessel achieves up to 5.5x training performance speedup and up to 38% inference latency reduction.Comment: The paper is accepted by HPCA 202

Empirical Analysis of Reputation-aware Task Delegation by Humans from a Multi-agent Game (Extended Abstract)

ABSTRACT What are the strategies people adopt when deciding how to delegated tasks to agents when the agents' reputation and productivity information is available? How effective are these strategies under different conditions? These questions are important since they have significant implications to the ongoing research of reputation aware task delegation in multi-agent systems (MASs). In this paper, we conduct an empirical study to address the aforementioned research questions by providing a gamified mechanism for people to report the reputation-aware task delegation strategies they adopt. The findings from this empirical study may help MAS researchers develop multi-agent trust evaluation testbeds with more realistic simulated human behaviours

Towards data-driven software engineering skills assessment

Purpose - Today’s software engineers often work in teams to develop complex software systems. Therefore, successful software engineering in practice require team members to possess not only sound programming skills such as analysis, design, coding and testing but also soft skills such as communication, collaboration and self-management. However, existing examination-based assessments are often inadequate for quantifying students’ soft skill development. The purpose of this paper is to explore alternative ways for assessing software engineering students’ skills through a data-driven approach. Design/methodology/approach - In this paper, the exploratory data analysis approach is adopted. Leveraging the proposed online agile project management tool – Human-centred Agile Software Engineering (HASE), a study was conducted involving 21 Scrum teams consisting of over 100 undergraduate software engineering students in multi-week coursework projects in 2014. Findings - During this study, students performed close to 170,000 software engineering activities logged by HASE. By analysing the collected activity trajectory data set, the authors demonstrate the potential for this new research direction to enable software engineering educators to have a quantifiable way of understanding their students’ skill development, and take a proactive approach in helping them improve their programming and soft skills. Originality/value - To the best of the authors’ knowledge, there has yet to be published previous studies using software engineering activity data to assess software engineers’ skills