arXiv subjects

Chun-Hsiung Tseng

Publications and source records attributed to Chun-Hsiung Tseng.

5 recordsLinked to original sources

arXiv2026

Accelerating AI-Powered Research: The PuppyChatter Framework for Usable and Flexible Tooling

This research addresses the challenges inherent in developing Artificial Intelligence (AI) applications, particularly those leveraging Large Language Models (LLMs). While AI vendors provide Application Programming Interfaces (APIs) and Software Development Kits (SDKs) to facilitate developer interaction, the former often requires intricate manual request construction, and the latter can lead to significant vendor lock-in. Furthermore, existing model abstraction frameworks, though mitigating vendor dependency, introduce an additional layer of complexity and potential security concerns. To reconcile these conflicting factors, the study introduces PuppyChatter, a novel software framework designed to preserve the intuitive simplicity of vendor-specific SDKs while simultaneously adhering to the vendor-neutrality principles characteristic of model abstraction, thereby offering a more streamlined and flexible development paradigm.

cs.AI↗

arXiv2025

Personalized Programming Education: Using Machine Learning to Boost Learning Performance Based on Students' Personality Traits

Studies have indicated that personality is related to achievement, and several personality assessment models have been developed. However, most are either questionnaires or based on marker systems, which entails limitations. We proposed a physiological signal based model, thereby ensuring the objectivity of the data and preventing unreliable responses. Thirty participants were recruited from the Department of Electrical Engineering of Yuan Ze University in Taiwan. Wearable sensors were used to collect physiological signals as the participants watched and summarized a video. They then completed a personality questionnaire based on the big five factor markers system. The results were used to construct a personality prediction model, which revealed that galvanic skin response and heart rate variance were key factors predicting extroversion; heart rate variance also predicted agreeableness and conscientiousness. The results of this experiment can elucidate students personality traits, which can help educators select the appropriate pedagogical methods.

cs.HC↗

arXiv2024

Do Students with Different Personality Traits Demonstrate Different Physiological Signals in Video-based Learning?

Past researches show that personality trait is a strong predictor for ones academic performance. Today, mature and verified marker systems for assessing personality traits already exist. However, marker systems-based assessing methods have their own limitations. For example, dishonest responses cannot be avoided. In this research, the goal is to develop a method that can overcome the limitations. The proposed method will rely on physiological signals for the assessment. Thirty participants have participated in this experiment. Based on the statistical results, we found that there are correlations between students personality traits and their physiological signal change when learning via videos. Specifically, we found that participants degree of extraversion, agreeableness, conscientiousness, and openness to experiences are correlated with the variance of heart rates, the variance of GSR values, and the skewness of voice frequencies, etc.

cs.HC↗

arXiv2017

Ehrenfest urn model with interaction

We studied the Ehrenfest urn model in which particles in the same urn interact with each other. Depending on the nature of interaction, the system undergoes a first-order or second-order phase transition. The relaxation time to the equilibrium state, the Poincare cycles of the equilibrium state and the most far-from-equilibrium state, and the duration time of the states during first-order phase transition are calculated. It was shown that the scaling behavior of the Poincare cycles could be served as an indication to the nature of phase transition, and the ratio of duration time of the states could be a strong evidence of the metastability during first-order phase transition.

cond-mat.stat-mech↗

arXiv2013

Is Somebody Watching Your Facebook Newsfeed?

With the popularity of Social Networking Services (SNS), more and more sensitive information are stored online and associated with SNS accounts. The obvious value of SNS accounts motivates the usage stealing problem -- unauthorized, stealthy use of SNS accounts on the devices owned/used by account owners without any technology hacks. For example, anxious parents may use their kids' SNS accounts to inspect the kids' social status; husbands/wives may use their spouses' SNS accounts to spot possible affairs. Usage stealing could happen anywhere in any form, and seriously invades the privacy of account owners. However, there is no any currently known defense against such usage stealing. To an SNS operator (e.g., Facebook Inc.), usage stealing is hard to detect using traditional methods because such attackers come from the same IP addresses/devices, use the same credentials, and share the same accounts as the owners do. In this paper, we propose a novel continuous authentication approach that analyzes user browsing behavior to detect SNS usage stealing incidents. We use Facebook as a case study and show that it is possible to detect such incidents by analyzing SNS browsing behavior. Our experiment results show that our proposal can achieve higher than 80% detection accuracy within 2 minutes, and higher than 90% detection accuracy after 7 minutes of observation time.

cs.SI↗