arXiv subjects

Yaohui Chen

Publications and source records attributed to Yaohui Chen.

16 recordsLinked to original sources

arXiv2025

Clustering-Guided Multi-Layer Contrastive Representation Learning for Citrus Disease Classification

Citrus, as one of the most economically important fruit crops globally, suffers severe yield depressions due to various diseases. Accurate disease detection and classification serve as critical prerequisites for implementing targeted control measures. Recent advancements in artificial intelligence, particularly deep learning-based computer vision algorithms, have substantially decreased time and labor requirements while maintaining the accuracy of detection and classification. Nevertheless, these methods predominantly rely on massive, high-quality annotated training examples to attain promising performance. By introducing two key designs: contrasting with cluster centroids and a multi-layer contrastive training (MCT) paradigm, this paper proposes a novel clustering-guided self-supervised multi-layer contrastive representation learning (CMCRL) algorithm. The proposed method demonstrates several advantages over existing counterparts: (1) optimizing with massive unannotated samples; (2) effective adaptation to the symptom similarity across distinct citrus diseases; (3) hierarchical feature representation learning. The proposed method achieves state-of-the-art performance on the public citrus image set CDD, outperforming existing methods by 4.5\%-30.1\% accuracy. Remarkably, our method narrows the performance gap with fully supervised counterparts (all samples are labeled). Beyond classification accuracy, our method shows great performance on other evaluation metrics (F1 score, precision, and recall), highlighting the robustness against the class imbalance challenge.

cs.CV↗

arXiv2025

SpatialScore: Towards Comprehensive Evaluation for Spatial Intelligence

Existing evaluations of multimodal large language models (MLLMs) on spatial intelligence are typically fragmented and limited in scope. In this work, we aim to conduct a holistic assessment of the spatial understanding capabilities of modern MLLMs and propose complementary data-driven and agent-based solutions. Specifically, we make the following contributions: (i) we introduce SpatialScore, to our knowledge, the most comprehensive and diverse benchmark for multimodal spatial intelligence to date. It covers multiple visual data types, input modalities, and question-answering formats, and contains approximately 5K manually verified samples spanning 30 distinct tasks; (ii) using SpatialScore, we extensively evaluate 49 representative MLLMs, revealing persistent challenges and a substantial gap between current models and human-level spatial intelligence; (iii) to advance model capabilities, we construct SpatialCorpus, a large-scale training resource with 331K multimodal QA samples that supports fine-tuning on spatial reasoning tasks and significantly improves the performance of existing models (e.g., Qwen3-VL); (iv) to complement this data-driven route with a training-free paradigm, we develop SpatialAgent, a multi-agent system equipped with 12 specialized spatial perception tools that supports both Plan-Execute and ReAct reasoning, enabling substantial gains in spatial reasoning without additional model training. Extensive experiments and in-depth analyses demonstrate the effectiveness of our benchmark, corpus, and agent framework. We expect these resources to serve as a solid foundation for advancing MLLMs toward human-level spatial intelligence. All data, code, and models will be released to the research community.

cs.CV↗

arXiv2024

TSGaussian: Semantic and Depth-Guided Target-Specific Gaussian Splatting from Sparse Views

Recent advances in Gaussian Splatting have significantly advanced the field, achieving both panoptic and interactive segmentation of 3D scenes. However, existing methodologies often overlook the critical need for reconstructing specified targets with complex structures from sparse views. To address this issue, we introduce TSGaussian, a novel framework that combines semantic constraints with depth priors to avoid geometry degradation in challenging novel view synthesis tasks. Our approach prioritizes computational resources on designated targets while minimizing background allocation. Bounding boxes from YOLOv9 serve as prompts for Segment Anything Model to generate 2D mask predictions, ensuring semantic accuracy and cost efficiency. TSGaussian effectively clusters 3D gaussians by introducing a compact identity encoding for each Gaussian ellipsoid and incorporating 3D spatial consistency regularization. Leveraging these modules, we propose a pruning strategy to effectively reduce redundancy in 3D gaussians. Extensive experiments demonstrate that TSGaussian outperforms state-of-the-art methods on three standard datasets and a new challenging dataset we collected, achieving superior results in novel view synthesis of specific objects. Code is available at: https://github.com/leon2000-ai/TSGaussian.

cs.CV↗

arXiv2024

CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models

Large language models (LLMs) introduce new security risks, but there are few comprehensive evaluation suites to measure and reduce these risks. We present BenchmarkName, a novel benchmark to quantify LLM security risks and capabilities. We introduce two new areas for testing: prompt injection and code interpreter abuse. We evaluated multiple state-of-the-art (SOTA) LLMs, including GPT-4, Mistral, Meta Llama 3 70B-Instruct, and Code Llama. Our results show that conditioning away risk of attack remains an unsolved problem; for example, all tested models showed between 26% and 41% successful prompt injection tests. We further introduce the safety-utility tradeoff: conditioning an LLM to reject unsafe prompts can cause the LLM to falsely reject answering benign prompts, which lowers utility. We propose quantifying this tradeoff using False Refusal Rate (FRR). As an illustration, we introduce a novel test set to quantify FRR for cyberattack helpfulness risk. We find many LLMs able to successfully comply with "borderline" benign requests while still rejecting most unsafe requests. Finally, we quantify the utility of LLMs for automating a core cybersecurity task, that of exploiting software vulnerabilities. This is important because the offensive capabilities of LLMs are of intense interest; we quantify this by creating novel test sets for four representative problems. We find that models with coding capabilities perform better than those without, but that further work is needed for LLMs to become proficient at exploit generation. Our code is open source and can be used to evaluate other LLMs.

cs.CR↗

arXiv2020

SoK: All You Ever Wanted to Know About x86/x64 Binary Disassembly But Were Afraid to Ask

Disassembly of binary code is hard, but necessary for improving the security of binary software. Over the past few decades, research in binary disassembly has produced many tools and frameworks, which have been made available to researchers and security professionals. These tools employ a variety of strategies that grant them different characteristics. The lack of systematization, however, impedes new research in the area and makes selecting the right tool hard, as we do not understand the strengths and weaknesses of existing tools. In this paper, we systematize binary disassembly through the study of nine popular, open-source tools. We couple the manual examination of their code bases with the most comprehensive experimental evaluation (thus far) using 3,788 binaries. Our study yields a comprehensive description and organization of strategies for disassembly, classifying them as either algorithm or else heuristic. Meanwhile, we measure and report the impact of individual algorithms on the results of each tool. We find that while principled algorithms are used by all tools, they still heavily rely on heuristics to increase code coverage. Depending on the heuristics used, different coverage-vs-correctness trade-offs come in play, leading to tools with different strengths and weaknesses. We envision that these findings will help users pick the right tool and assist researchers in improving binary disassembly.

cs.CR↗

arXiv2020

MEUZZ: Smart Seed Scheduling for Hybrid Fuzzing

Seed scheduling is a prominent factor in determining the yields of hybrid fuzzing. Existing hybrid fuzzers schedule seeds based on fixed heuristics that aim to predict input utilities. However, such heuristics are not generalizable as there exists no one-size-fits-all rule applicable to different programs. They may work well on the programs from which they were derived, but not others. To overcome this problem, we design a Machine learning-Enhanced hybrid fUZZing system (MEUZZ), which employs supervised machine learning for adaptive and generalizable seed scheduling. MEUZZ determines which new seeds are expected to produce better fuzzing yields based on the knowledge learned from past seed scheduling decisions made on the same or similar programs. MEUZZ's learning is based on a series of features extracted via code reachability and dynamic analysis, which incurs negligible runtime overhead (in microseconds). Moreover, MEUZZ automatically infers the data labels by evaluating the fuzzing performance of each selected seed. As a result, MEUZZ is generally applicable to, and performs well on, various kinds of programs. Our evaluation shows MEUZZ significantly outperforms the state-of-the-art grey-box and hybrid fuzzers, achieving 27.1% more code coverage than QSYM. The learned models are reusable and transferable, which boosts fuzzing performance by 7.1% on average and improves 68% of the 56 cross-program fuzzing campaigns. MEUZZ discovered 47 deeply hidden and previously unknown bugs--with 21 confirmed and fixed by the developers--when fuzzing 8 well-tested programs with the same configurations as used in previous work.

cs.CR↗

arXiv2020

A Reconfigurable Hybrid Actuator with Rigid and Soft Components

Classical rigid-bodied robotic systems are presented with proven success in theoretical development and industrial applications, are recently challenged by the emergence of soft robotics due to a growing need in physical human-robot interactions (pHRI), such as wearable devices, medical robots, personal robots, etc. In this paper, we present the design and fabrication of a robust, hybrid bending actuator build from both rigid and soft components inspired by crustaceans, where its bending radius and axis can be mechanically programmed through the selective activation of the rigid exterior joints, actuated by the soft actuators inside. The hybrid actuator was experimentally measured in terms of bending and force tests to demonstrate the utility of this design. Finally, a case study was presented to demonstrate its capacity to adapt to specific objects geometry, anticipating its potential application in situations where compliance is the priority.

cs.RO↗

arXiv2020

A Lobster-inspired Robotic Glove for Hand Rehabilitation

This paper presents preliminary results of the design, development, and evaluation of a hand rehabilitation glove fabricated using lobster-inspired hybrid design with rigid and soft components for actuation. Inspired by the bending abdomen of lobsters, hybrid actuators are built with serially jointed rigid shells actuated by pressurized soft chambers inside to generate bending motions. Such bio-inspiration absorbs features from the classical rigid-bodied robotics with precisely-defined motion generation, as well as the emerging soft robotics with light-weight, physically safe, and adaptive actuation. The fabrication procedure is described, followed by experiments to mechanically characterize these actuators. Finally, an open-palm glove design integrated with these hybrid actuators is presented for a qualitative case study. A hand rehabilitation system is developed by learning patterns of the sEMG signals from the user's forearm to train the assistive glove for hand rehabilitation exercises.

cs.RO↗

arXiv2020

A Lobster-inspired Hybrid Actuator With Rigid and Soft Components

Soft actuators have drawn significant attention from researchers with an inherently compliant design to address the safety issues in physical human-robot interactions. However, they are also vulnerable and pose new challenges in the design, fabrication, and analysis due to their inherent material softness. In this paper, a novel hybrid actuator design is presented with bio-inspirations from the lobster, or crustaceans in a broader perspective. We enclose a soft chamber with rectangular cross-section using a series of articulated rigid shells to produce bending under pneumatic input. By mimicking the shell pattern of lobsters' abdomen, foldable rigid shells are designed to provide the soft actuator with full protection throughout the motion range. The articulation of the rigid shells predefines the actuator's bending motions. As a result, the proposed design enables one to analyze this hybrid actuator with simplified quasi-static models and rigid-body kinematics, which are further validated by mechanical tests. This paper demonstrates that the proposed hybrid actuator design is capable of bridging the major design drawbacks of the entirely rigid and soft robots while preserving their engineering merits in performance.

cs.RO↗

arXiv2019

SAVIOR: Towards Bug-Driven Hybrid Testing

Hybrid testing combines fuzz testing and concolic execution. It leverages fuzz testing to test easy-to-reach code regions and uses concolic execution to explore code blocks guarded by complex branch conditions. However, its code coverage-centric design is inefficient in vulnerability detection. First, it blindly selects seeds for concolic execution and aims to explore new code continuously. However, as statistics show, a large portion of the explored code is often bug-free. Therefore, giving equal attention to every part of the code during hybrid testing is a non-optimal strategy. It slows down the detection of real vulnerabilities by over 43%. Second, classic hybrid testing quickly moves on after reaching a chunk of code, rather than examining the hidden defects inside. It may frequently miss subtle vulnerabilities despite that it has already explored the vulnerable code paths. We propose SAVIOR, a new hybrid testing framework pioneering a bug-driven principle. Unlike the existing hybrid testing tools, SAVIOR prioritizes the concolic execution of the seeds that are likely to uncover more vulnerabilities. Moreover, SAVIOR verifies all vulnerable program locations along the executing program path. By modeling faulty situations using SMT constraints, SAVIOR reasons the feasibility of vulnerabilities and generates concrete test cases as proofs. Our evaluation shows that the bug-driven approach outperforms mainstream automated testing techniques, including state-of-the-art hybrid testing systems driven by code coverage. On average, SAVIOR detects vulnerabilities 43.4% faster than DRILLER and 44.3% faster than QSYM, leading to the discovery of 88 and 76 more uniquebugs,respectively.Accordingtotheevaluationon11 well fuzzed benchmark programs, within the first 24 hours, SAVIOR triggers 481 UBSAN violations, among which 243 are real bugs.

cs.SE↗

arXiv2019

PTrix: Efficient Hardware-Assisted Fuzzing for COTS Binary

Despite its effectiveness in uncovering software defects, American Fuzzy Lop (AFL), one of the best grey-box fuzzers, is inefficient when fuzz-testing source-unavailable programs. AFL's binary-only fuzzing mode, QEMU-AFL, is typically 2-5X slower than its source-available fuzzing mode. The slowdown is largely caused by the heavy dynamic instrumentation. Recent fuzzing techniques use Intel Processor Tracing (PT), a light-weight tracing feature supported by recent Intel CPUs, to remove the need of dynamic instrumentation. However, we found that these PT-based fuzzing techniques are even slower than QEMU-AFL when fuzzing real-world programs, making them less effective than QEMU-AFL. This poor performance is caused by the slow extraction of code coverage information from highly compressed PT traces. In this work, we present the design and implementation of PTrix, which fully unleashes the benefits of PT for fuzzing via three novel techniques. First, PTrix introduces a scheme to highly parallel the processing of PT trace and target program execution. Second, it directly takes decoded PT trace as feedback for fuzzing, avoiding the expensive reconstruction of code coverage information. Third, PTrix maintains the new feedback with stronger feedback than edge-based code coverage, which helps reach new code space and defects that AFL may not. We evaluated PTrix by comparing its performance with the state-of-the-art fuzzers. Our results show that, given the same amount of time, PTrix achieves a significantly higher fuzzing speed and reaches into code regions missed by the other fuzzers. In addition, PTrix identifies 35 new vulnerabilities in a set of previously well-fuzzed binaries, showing its ability to complement existing fuzzers.

cs.SE↗

arXiv2015

Impact of slow-light enhancement on optical propagation in active semiconductor photonic crystal waveguides

We derive and validate a set of coupled Bloch wave equations for analyzing the reflection and transmission properties of active semiconductor photonic crystal waveguides. In such devices, slow-light propagation can be used to enhance the material gain per unit length, enabling, for example, the realization of short optical amplifiers compatible with photonic integration. The coupled wave analysis is compared to numerical approaches based on the Fourier modal method and a frequency domain finite element technique. The presence of material gain leads to the build-up of a backscattered field, which is interpreted as distributed feedback effects or reflection at passive-active interfaces, depending on the approach taken. For very large material gain values, the band structure of the waveguide is perturbed, and deviations from the simple coupled Bloch wave model are found.

physics.optics↗

arXiv2015

Threshold Characteristics of Slow-Light Photonic Crystal Lasers

The threshold properties of photonic crystal quantum dot lasers operating in the slow-light regime are investigated experimentally and theoretically. Measurements show that, in contrast to conventional lasers, the threshold gain attains a minimum value for a specific cavity length. The experimental results are explained by an analytical theory for the laser threshold that takes into account the effects of slow-light and random disorder due to unavoidable fabrication imperfections. Longer lasers are found to operate deeper into the slow-light region, leading to a trade-off between slow-light induced reduction of the mirror loss and slow-light enhancement of disorder-induced losses.

physics.optics↗

arXiv2014

Nonreciprocal transmission in a photonic-crystal Fano structure enabled by symmetry breaking

Nanostructures that feature nonreciprocal light transmission are highly desirable building blocks for realizing photonic integrated circuits. Here, a simple and ultra-compact photonic-crystal structure, where a waveguide is coupled to a single nanocavity, is proposed and experimentally demonstrated, showing very efficient optical diode functionality. The key novelty of the structure is the use of a Fano resonance in combination with spatial symmetry breaking and cavity enhanced material nonlinearities to realize non-reciprocal propagation effects at ultra-low power and with a good wavelength tunability. The nonlinearity of the device relies on ultrafast carrier dynamics, rather than the thermal effects usually considered, allowing the demonstration of nonreciprocal operation at a bit-rate of 10 Gbit/s with a low energy consumption of 4.5 fJ/bit.

physics.optics↗

arXiv2014

Slow-light enhanced gain in active photonic crystal waveguides

Slow light is a fascinating physical effect, raising fundamental questions related to our understanding of light-matter interactions as well as offering new possibilities for photonic devices. From the first demonstrations of slow light propagation in ultra-cold atomic gasses, solid-state Ruby and photonic crystal structures, focus has shifted to applications, with slow light offering the ability to enhance and control light-matter interactions. The demonstration of tuneable delay lines, enhanced nonlinearities and spontaneous emission, enlarged spectral sensitivity and increased phase shifts illustrate the possibilities enabled by slow light propagation, with microwave photonics emerging as one of the promising applications. Here, we demonstrate that slow light can be used to control and increase the gain coefficient of an active semiconductor waveguide. The effect was theoretically predicted but not yet experimentally demonstrated. These results show a route towards realizing ultra-compact optical amplifiers for linear and nonlinear applications in integrated photonics and prompts further research into the rich physics of such structures.

physics.optics↗

arXiv2014

Fano resonance control in a photonic crystal structure and its application to ultrafast switching

Fano resonances appear in quantum mechanical as well as classical systems as a result of the interference between two paths: one involving a discrete resonance and the other a continuum. Compared to a conventional resonance, characterized by a Lorentzian spectral response, the characteristic asymmetric and sharp spectral response of a Fano resonance is suggested to enable photonic switches and sensors with superior characteristics. While experimental demonstrations of the appearance of Fano resonances have been made in both plasmonic and photonic-crystal structures, the control of these resonances is experimentally challenging, often involving the coupling of near-resonant cavities. Here, we experimentally demonstrate two simple structures that allow surprisingly robust control of the Fano spectrum. One structure relies on controlling the amplitude of one of the paths and the other uses symmetry breaking. Short-pulse dynamic measurements show that besides drastically increasing the switching contrast, the transmission dynamics itself is strongly affected by the nature of the resonance. The influence of slow-recovery tails implied by a long carrier lifetime can thus be reduced using a Fano resonance due to a hitherto unrecognized reshaping effect of the nonlinear Fano transfer function. For the first time, we present a system application of a Fano structure, demonstrating its advantages by the experimental realization of 10 Gbit/s all-optical modulation with bit-error-ratios on the order of 10$^{-7}$ for input powers less than 1 mW. These results represent a significant improvement compared to the use of a conventional Lorentzian resonance.

physics.optics↗