arXiv subjects

Zhixing Liu

Publications and source records attributed to Zhixing Liu.

3 recordsLinked to original sources

arXiv2026

Leveraging AI for Direct Bystander Intervention Against Cyberbullying

Cyberbullying is a pervasive problem in online environments, causing substantial psychological harm to victims. Although bystander intervention has proven effective in mitigating its impact, motivating bystanders to engage in direct intervention remains a persistent challenge. Studies have suggested that difficulties in intervention skills and defending self-efficacy hinder bystanders from initiating direct intervention. To address this challenge, we introduced EmojiGen, an AI intervention tool designed to empower bystanders for direct intervention. EmojiGen enabled users to simply select an emoji as an intention clue, which subsequently combined the cyberbullying context to generate responses. In a between-subjects experiment involving 90 participants on a custom-built social media platform, we found that EmojiGen significantly increased the frequency of direct bystander interventions, both in supporting victims and in confronting perpetrators, driven by different factors. EmojiGen also increased the sense of knowing how to help and defending self-efficacy, while reducing perceived workload and anxiety associated with initiating intervention. The study contributed to the CSCW community through offering an effective direct bystander intervention method and providing design implications for future cyberbullying interventions.

cs.HC↗

arXiv2025

From Passive to Proactive: A Hierarchical Multi-Agent Framework for Automated Medical Pre-Consultation

The post-pandemic surge in healthcare demand, coupled with critical nursing shortages, has placed unprecedented pressure on medical triage systems, necessitating innovative AI-driven solutions. We present a multi-agent interactive intelligent system for medical triage that addresses three fundamental challenges in current AI-based triage systems: inadequate medical specialization leading to misclassification, heterogeneous department structures across healthcare institutions, and inefficient detail-oriented questioning that impedes rapid triage decisions. Our system employs three specialized agents--RecipientAgent, InquirerAgent, and DepartmentAgent--that collaborate through Inquiry Guidance mechanism and Classification Guidance Mechanism to transform unstructured patient symptoms into accurate department recommendations. To ensure robust evaluation, we constructed a comprehensive Chinese medical triage dataset from "Ai Ai Yi Medical Network", comprising 3,360 real-world cases spanning 9 primary departments and 62 secondary departments. Experimental results demonstrate that our multi-agent system achieves 89.6% accuracy in primary department classification and 74.3% accuracy in secondary department classification after four rounds of patient interaction. The system's dynamic matching based guidance mechanisms enable efficient adaptation to diverse hospital configurations while maintaining high triage accuracy. We successfully developed this multi-agent triage system that not only adapts to organizational heterogeneity across healthcare institutions but also ensures clinically sound decision-making.

cs.AI↗

arXiv2024

Detecting Secular Perturbations in Kepler Planetary Systems Using Simultaneous Impact Parameter Variation Analysis (SIPVA)

Secular impact-parameter variations encode dynamical perturbations and can help reveal unseen companions or constrain planetary masses when combined with dynamical interpretation. Existing methods either fit transits independently or jointly model the light curves and orbital dynamics. We introduce Simultaneous Impact Parameter Variation Analysis (SIPVA), which avoids the N-body integrations used in many dynamical or photodynamical fitting approaches. SIPVA directly incorporates a linear time-dependent model for impact parameters into the Markov Chain Monte Carlo (MCMC) framework by fitting all transits simultaneously. We evaluate SIPVA and the transit-by-transit implementation on artificial systems, grouped by the fixed-model injection strength label L = sqrt(LLR_theory). In these controlled injections, SIPVA outperforms the particular transit-by-transit implementation examined here in both detection and recovery performance. Applied to 16 Kepler planets previously identified as exhibiting significant transit-duration variations, SIPVA detects impact-parameter trends in 9 planets, compared with 5 using independent transit fits.SIPVA provides a joint light-curve framework for measuring secular impact-parameter trends without specifying a dynamical architecture or performing N-body integrations.

astro-ph.EP↗