arXiv subjects

Zhuohao Zhang

Publications and source records attributed to Zhuohao Zhang.

3 recordsLinked to original sources

arXiv2026

Mystra: Declarative Dynamic Taint Analysis via Shadow Virtual Machine

Dynamic taint analysis (DTA) for interpreted languages like JavaScript and Python requires three capabilities: observing host-runtime operations, maintaining parallel taint states, and defining how taint propagates. Existing systems couple these capabilities within an instrumentation mechanism -- source-rewriting or engine-native -- either incurring high runtime overhead or demanding engine-specific embeddings. There is yet to be a runtime-independent abstraction of a general DTA that separates taint semantics and state transitions from how a host runtime executes them. We set out to develop a DTA engine that is extensible, performant, and accurate. To achieve this, we introduce a Shadow Virtual Machine executing alongside host runtimes that tracks multi-level taint, provenance, and cross-invocation context. We design Mystra, a declarative taint specification language with formal operational semantics. Mystra is designed to be language model friendly, and is equipped with validators enabling trustworthy automated synthesis of rules. Mystra is also the first to express higher-order function taint transfer declaratively. Further, Mystra rules are compiled ahead of time to a binary representation and dispatch in constant runtime. We implement our vision into a tool named Shar, which contains a shared core engine and instantiations on three runtimes: V8 in both Node$.$js and Chromium (embedding), SpiderMonkey (engine), and CPython (language). Accuracy wise, on SecBench$.$js (493 in-scope CVEs across four CWE categories), our V8 instantiation achieves 95.5% recall with zero false positives on patched-version testing. Regarding performance, the runtime overhead of Shar is 1.85$\times$ over vanilla Node$.$js on NodeMedic's benchmarks, and is 22.7$\times$ lower than NodeMedic-FINE on identical workloads, all the while producing 33.2% higher recall in its supported categories.

cs.PL↗

arXiv2025

Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening

Art has long played a profound role in shaping human emotion, cognition, and behavior. While visual arts such as painting and architecture have been studied through eye tracking, revealing distinct gaze patterns between experts and novices, analogous methods for auditory art forms remain underdeveloped. Music, despite being a pervasive component of modern life and culture, still lacks objective tools to quantify listeners' attention and perceptual focus during natural listening experiences. To our knowledge, this is the first attempt to decode selective attention to musical elements using naturalistic, studio-produced songs and a lightweight consumer-grade EEG device with only four electrodes. By analyzing neural responses during real world like music listening, we test whether decoding is feasible under conditions that minimize participant burden and preserve the authenticity of the musical experience. Our contributions are fourfold: (i) decoding music attention in real studio-produced songs, (ii) demonstrating feasibility with a four-channel consumer EEG, (iii) providing insights for music attention decoding, and (iv) demonstrating improved model ability over prior work. Our findings suggest that musical attention can be decoded not only for novel songs but also across new subjects, showing performance improvements compared to existing approaches under our tested conditions. These findings show that consumer-grade devices can reliably capture signals, and that neural decoding in music could be feasible in real-world settings. This paves the way for applications in education, personalized music technologies, and therapeutic interventions.

q-bio.NC↗

arXiv2024

Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings

Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representations to cortical representations. Building upon this concept, our approach reverses the direction of prediction: we utilize ANN representations as a supervisory signal to train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, we focus on constructing a recognition model for music identification, where electroencephalography (EEG) brain recordings collected during music listening serve as input. By training an EEG recognition model to predict ANN representations-representations associated with music identification-we observed a substantial improvement in classification accuracy. This study introduces a novel approach to developing recognition models for brain recordings in response to external auditory stimuli. It holds promise for advancing brain-computer interfaces (BCI), neural decoding techniques, and our understanding of music cognition. Furthermore, it provides new insights into the relationship between auditory brain activity and ANN representations.

q-bio.NC↗