arXiv subjects

Zirui Zhao

Publications and source records attributed to Zirui Zhao.

27 records · Page 2Linked to original sources

arXiv2023

A Virtual Reality Training System for Automotive Engines Assembly and Disassembly

Automotive engine assembly and disassembly are common and crucial programs in the automotive industry. Traditional education trains students to learn automotive engine assembly and disassembly in lecture courses and then to operate with physical engines, which are generally low effectiveness and high cost. In this work, we developed a multi-layer structured Virtual Reality (VR) system to provide students with training in automotive engine (Buick Verano) assembly and disassembly. We designed the VR training system with The VR training system is designed to have several major features, including replaceable engine parts and reusable tools, friendly user interfaces and guidance, and bottom-up designed multi-layer architecture, which can be extended to various engine models. The VR system is evaluated with controlled experiments of two groups of students. The results demonstrate that our VR training system provides remarkable usability in terms of effectiveness and efficiency. Currently, our VR system has been demonstrated and employed in the courses of Chinese colleges to train students in automotive engine assembly and disassembly. A free-to-use executable file (Microsoft Windows) and open-source code are available at https://github.com/LadissonLai/SUSTech_VREngine for facilitating the development of VR systems in the automotive industry. Finally, a video describing the operations in our VR training system is available at https://www.youtube.com/watch?v=yZe4YTwwAC4

cs.HC↗

arXiv2023

Large Language Models as Commonsense Knowledge for Large-Scale Task Planning

Large-scale task planning is a major challenge. Recent work exploits large language models (LLMs) directly as a policy and shows surprisingly interesting results. This paper shows that LLMs provide a commonsense model of the world in addition to a policy that acts on it. The world model and the policy can be combined in a search algorithm, such as Monte Carlo Tree Search (MCTS), to scale up task planning. In our new LLM-MCTS algorithm, the LLM-induced world model provides a commonsense prior belief for MCTS to achieve effective reasoning; the LLM-induced policy acts as a heuristic to guide the search, vastly improving search efficiency. Experiments show that LLM-MCTS outperforms both MCTS alone and policies induced by LLMs (GPT2 and GPT3.5) by a wide margin, for complex, novel tasks. Further experiments and analyses on multiple tasks -- multiplication, multi-hop travel planning, object rearrangement -- suggest minimum description length (MDL) as a general guiding principle: if the description length of the world model is substantially smaller than that of the policy, using LLM as a world model for model-based planning is likely better than using LLM solely as a policy.

cs.RO↗

arXiv2023

Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement

We present a new method, PARsing And visual GrOuNding (ParaGon), for grounding natural language in object placement tasks. Natural language generally describes objects and spatial relations with compositionality and ambiguity, two major obstacles to effective language grounding. For compositionality, ParaGon parses a language instruction into an object-centric graph representation to ground objects individually. For ambiguity, ParaGon uses a novel particle-based graph neural network to reason about object placements with uncertainty. Essentially, ParaGon integrates a parsing algorithm into a probabilistic, data-driven learning framework. It is fully differentiable and trained end-to-end from data for robustness against complex, ambiguous language input.

cs.RO↗

arXiv2023

MgF$_2$ as an effective additive for improving ionic conductivity of ceramic solid electrolytes

As typical solid-state electrolytes (SSEs), {Na}$_{1+x}${Zr}$_2${Si}$_{x}${P}$_{3-x}${O}$_{12}$ NASICONs provide an ideal platform for solid-state batteries (SSBs) that display higher safety and accommodate higher energy densities. The critical points for achieving SSBs with higher efficiencies are to improve essentially the ionic conductivity and to reduce largely the interfacial resistance between SSEs and cathode materials, which would necessitate extremely high level of craftsmanship and high-pressure equipment. An alternative to higher-performance and lower-cost SSBs is additive manufacturing. Here, we report on an effective additive, MgF$_2$, which was used in synthesizing NASICONs, resulting in SSEs with fewer defects and higher performance. With an addition of mere 1 wt$\%$ MgF$_2$ additive, the total room-temperature ionic conductivity of the NASICON electrolyte reaches up to 2.03 mS cm$^{-1}$, improved up to $\sim$ 181.3$\%$, with an activation energy of 0.277 eV. Meanwhile, the stability of the Na plating/stripping behavior in symmetric cells increases from 236 to 654 h. We tried to reveal the microscopic origins of the higher ionic conductivity of MgF$_2$-doped NASICONs by comprehensive in-house characterizations. Our study discovers a novel MgF$_2$ additive and provides an efficient way to prepare higher-performance SSEs, making it possible to fabricate lower-cost SSBs in industries.

physics.app-ph↗

arXiv2021

Speculative Interference Attacks: Breaking Invisible Speculation Schemes

Recent security vulnerabilities that target speculative execution (e.g., Spectre) present a significant challenge for processor design. The highly publicized vulnerability uses speculative execution to learn victim secrets by changing cache state. As a result, recent computer architecture research has focused on invisible speculation mechanisms that attempt to block changes in cache state due to speculative execution. Prior work has shown significant success in preventing Spectre and other vulnerabilities at modest performance costs. In this paper, we introduce speculative interference attacks, which show that prior invisible speculation mechanisms do not fully block these speculation-based attacks. We make two key observations. First, misspeculated younger instructions can change the timing of older, bound-to-retire instructions, including memory operations. Second, changing the timing of a memory operation can change the order of that memory operation relative to other memory operations, resulting in persistent changes to the cache state. Using these observations, we demonstrate (among other attack variants) that secret information accessed by mis-speculated instructions can change the order of bound-to-retire loads. Load timing changes can therefore leave secret-dependent changes in the cache, even in the presence of invisible speculation mechanisms. We show that this problem is not easy to fix: Speculative interference converts timing changes to persistent cache-state changes, and timing is typically ignored by many cache-based defenses. We develop a framework to understand the attack and demonstrate concrete proof-of-concept attacks against invisible speculation mechanisms. We provide security definitions sufficient to block speculative interference attacks; describe a simple defense mechanism with a high performance cost; and discuss how future research can improve its performance.

cs.AR↗

arXiv2020

Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.

cs.RO↗

arXiv2019

Active Learning for Risk-Sensitive Inverse Reinforcement Learning

One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assuming that humans act according to a random cost with respect to a set of subjectively distorted distributions instead of a fixed one. Such assumption provides the additional flexibility to model human's risk preferences, represented by a risk envelope, in safe-critical tasks. However, like other learning from demonstration techniques, RS-IRL could also suffer inefficient learning due to redundant demonstrations. Inspired by the concept of active learning, this research derives a probabilistic disturbance sampling scheme to enable an RS-IRL agent to query expert support that is likely to expose unrevealed boundaries of the expert's risk envelope. Experimental results confirm that our approach accelerates the convergence of RS-IRL algorithms with lower variance while still guaranteeing unbiased convergence.

cs.LG↗

arXiv2018

DeepObfuscation: Securing the Structure of Convolutional Neural Networks via Knowledge Distillation

This paper investigates the piracy problem of deep learning models. Designing and training a well-performing model is generally expensive. However, when releasing them, attackers may reverse engineer the models and pirate their design. This paper, therefore, proposes deep learning obfuscation, aiming at obstructing attackers from pirating a deep learning model. In particular, we focus on obfuscating convolutional neural networks (CNN), a widely employed type of deep learning architectures for image recognition. Our approach obfuscates a CNN model eventually by simulating its feature extractor with a shallow and sequential convolutional block. To this end, we employ a recursive simulation method and a joint training method to train the simulation network. The joint training method leverages both the intermediate knowledge generated by a feature extractor and data labels to train a simulation network. In this way, we can obtain an obfuscated model without accuracy loss. We have verified the feasibility of our approach with three prevalent CNNs, i.e., GoogLeNet, ResNet, and DenseNet. Although these networks are very deep with tens or hundreds of layers, we can simulate them in a shallow network including only five or seven convolutional layers. The obfuscated models are even more efficient than the original models. Our obfuscation approach is very effective to protect the critical structure of a deep learning model from being exposed to attackers. Moreover, it can also thwart attackers from pirating the model with transfer learning or incremental learning techniques because the shallow simulation network bears poor learning ability. To our best knowledge, this paper serves as a first attempt to obfuscate deep learning models, which may shed light on more future studies.

cs.CR↗

arXiv2018

On Benchmarking the Capability of Symbolic Execution Tools with Logic Bombs

Symbolic execution now becomes an indispensable technique for software testing and program analysis. There are several symbolic execution tools available off-the-shelf, and we need a practical benchmark approach to learn their capabilities. Therefore, this paper introduces a novel approach to benchmark symbolic execution tools in a fine-grained and efficient manner. In particular, our approach evaluates the performance of such tools against the known challenges faced by general symbolic execution techniques, such as floating-point numbers and symbolic memories. To this end, we first survey related papers and systematize the challenges of symbolic execution. We extract 12 distinct challenges from the literature and categorize them into two categories: symbolic-reasoning challenges and path-explosion challenges. Then, we develop a dataset of logic bombs and a framework to benchmark symbolic execution tools automatically. For each challenge, our dataset contains several logic bombs, each of which is guarded by a specific challenging problem. If a symbolic execution tool can find test cases to trigger logic bombs, it indicates that the tool can handle the corresponding problems. We have conducted real-world experiments with three popular symbolic execution tools: KLEE, Angr, and Triton. Experimental results show that our approach can reveal their capabilities and limitations in handling particular issues accurately and efficiently. The benchmark process generally takes only dozens of minutes to evaluate a tool. We release our dataset on GitHub as open source, with an aim to better facilitate the community to conduct future work on benchmarking symbolic execution tools.

cs.SE↗