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Profil bibliographique

Shuaiqi Wang

Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.

35Publications signalées
125Citations signalées
2Affiliations récentes

Les institutions déclarées

Les domaines associés

Privacy-Preserving Technologies in DataPlant Stress Responses and ToleranceCryptography and Data SecurityPhotosynthetic Processes and MechanismsPrivacy, Security, and Data Protection

Les publications récentes

Accès ouvert 2026 preprint OpenAlex

QuanText: Protecting Dataset-Level Secrets in Textual Data Sharing

Shuaiqi Wang, Zinan Lin, Giulia Fanti

Natural-language datasets support many downstream applications and research studies, but releasing text can reveal sensitive global properties of the underlying data source, such as the proportion of records associated with a particular gender, diagnosis, or political stance. Existing work has largely focused …

0 citations arXiv (Cornell University)
Accès ouvert 2026 preprint OpenAlex

SynAE: A Framework for Measuring the Quality of Synthetic Data for Tool-Calling Agent Evaluations

Shuaiqi Wang, Aadyaa Maddi, Zinan Lin, Giulia Fanti

Today, tool-calling agents are commonly evaluated or tested on static datasets of execution traces, including input commands, agent responses, and associated tool calls. However, internal production datasets are often insufficient or unusable for testing; for example, they may contain sensitive or proprietary …

0 citations arXiv (Cornell University)
Accès ouvert 2026 preprint OpenAlex

SynAE: A Framework for Measuring the Quality of Synthetic Data for Tool-Calling Agent Evaluations

Shuaiqi Wang, Aadyaa Maddi, Zinan Lin, Giulia Fanti

Today, tool-calling agents are commonly evaluated or tested on static datasets of execution traces, including input commands, agent responses, and associated tool calls. However, internal production datasets are often insufficient or unusable for testing; for example, they may contain sensitive or proprietary …

us, gb (code pays fourni par la source)

0 citations arXiv (Cornell University)
Accès ouvert 2026 article OpenAlex

Heterologous GSNOR expression modulates nitric oxide and glutathione redox to differentially shape plant responses to herbicides

Shuaiqi Wang, Bing Zhang, H. Q. Dong, Yibo Dong et autres

Redox and nitric oxide (NO) signaling are central to plant responses to herbicides, yet whether strengthening NO–redox buffering improves tolerance remains unresolved. We generated Arabidopsis thaliana lines overexpressing the bacterial S-nitrosoglutathione reductase (GSNOR) gene FrmA , alone ( FrmA OE ) or …

cn (code pays fourni par la source)

0 citations Journal of Hazardous Materials
2025 conference-paper OpenAlex

PIBT-CBS and NN-MPC Based Cooperative Transportation System: An Integrated Framework

Xiaodong Li, Rui Qiu, Shuaiqi Wang, Yue Ming

This paper proposes an integrated framework composed of Priority Inheritance with Backtracking-Conflict Based Search (PIBT-CBS), and Neural Network Model Predictive Control (NN-MPC) to address the planning and control problem of Cooperative Transportation System (CTS) performing collaborative transportation in complex scenarios. Initially, in …

cn (code pays fourni par la source)

0 citations
2025 conference-paper OpenAlex

“You Have to Ignore the Dangers”: User Perceptions of the Security and Privacy Benefits of WhatsApp Mods

Collins W. Munyendo, Kentrell Owens, Shuaiqi Wang, Adam J. Aviv et autres

WhatsApp is the most popular social messaging platform, and modified versions (or “mods”) of the official WhatsApp are increasingly popular. Mods advertise additional features and customization. However, some of these features, e.g., retaining deleted messages and statuses, enable mod users to subvert …

us (code pays fourni par la source)

2 citations
Accès ouvert 2025 conference-paper OpenAlex

Inferentially-Private Private Information

Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti et autres

Information disclosure can compromise privacy when revealed information is correlated with private information. We consider the notion of inferential privacy, which measures privacy leakage by bounding the inferential power a Bayesian adversary can gain by observing a released signal. Our goal is …

us, cn (code pays fourni par la source)

0 citations

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