arXiv subjects

Kaixin Ji

Publications and source records attributed to Kaixin Ji.

At least 19 recordsLinked to original sources

arXiv2026

StoryEcho: A Narrative Mirroring Loop Generative Storytelling System for Picky-Eating Intervention

Picky eating can limit children's dietary variety and create tension in family feeding routines. Existing food-related technologies often focus on mealtime intervention or standalone educational artifacts, offering limited support for connecting low-pressure narrative engagement with children's real-world food exploration over time. We present StoryEcho, a generative storytelling system centered on a narrative mirroring loop, in which personalized stories model sensory exploration through a persistent counterpart and children's subsequent food encounters are reflected back into narrative feedback and future story development. Informed by a formative study, we designed StoryEcho and evaluated it in a 14-day between-subjects field study with 26 families. Compared with food-personalized generative stories without the narrative mirroring loop, StoryEcho was associated with higher try level, approach, and intake, and lower resistance and caregiver pressure. These findings suggest that narrative mirroring can support children's low-pressure food exploration in family routines, while highlighting design tensions for future generative storytelling interventions.

cs.HC↗

arXiv2026

Reconstruction and Reflection of Positive Experiences through Resurfacing Laughter-indexed Everyday Moments

Positive everyday moments often escape deliberate recording, while continuous self-tracking can generate extensive records that are difficult to revisit. We explore laughter as a naturally occurring, sparse index for constructing contextualized personal records to support later reconstruction and reflection. A formative study with 12 participants characterized laughter as an affective but semantically incomplete index and informed \textit{LaughAnchor}, a mobile and wearable self-tracking system. During participant-initiated recording, the system assembles detected laughter and aligned context into candidate moments for later reconstruction and reflection, with layered context disclosure, user-controlled curation, and near-term and long-term resurfacing. In a three-week field deployment with 12 participants, passive indexing preserved moments they considered unlikely to record deliberately but valued retrospectively. During resurfacing, participants attributed affective re-experiencing to laughter and used additional context both to reconstruct episodes and to explore already-recalled experiences. Across moments and reviews, resurfacing supported rediscovery and broader awareness of relationships, routines, and emotional states. These findings inform self-tracking designs that use sparse affective indices to organize contextual records for reconstruction and reflection, while keeping interpretation and retention under user control.

cs.HC↗

arXiv2025

SenseSeek Dataset: Multimodal Sensing to Study Information Seeking Behaviors

Information processing tasks involve complex cognitive mechanisms that are shaped by various factors, including individual goals, prior experience, and system environments. Understanding such behaviors requires a sophisticated and personalized data capture of how one interacts with modern information systems (e.g., web search engines). Passive sensors, such as wearables, capturing physiological and behavioral data, have the potential to provide solutions in this context. This paper presents a novel dataset, SenseSeek, designed to evaluate the effectiveness of consumer-grade sensors in a complex information processing scenario: searching via systems (e.g., search engines), one of the common strategies users employ for information seeking. The SenseSeek dataset comprises data collected from 20 participants, 235 trials of the stimulated search process, 940 phases of stages in the search process, including the realization of Information Need (IN), Query Formulation (QF), Query Submission by Typing (QS-T) or Speaking (QS-S), and Relevance Judgment by Reading (RJ-R) or Listening (RJ-L). The data includes Electrodermal Activities (EDA), Electroencephalogram (EEG), PUPIL, GAZE, and MOTION data, which were captured using consumer-grade sensors. It also contains 258 features extracted from the sensor data, the gaze-annotated screen recordings, and task responses. We validate the usefulness of the dataset by providing baseline analysis on the impacts of different cognitive intents and interaction modalities on the sensor data, and effectiveness of the data in discriminating the search stages. To our knowledge, SenseSeek is the first dataset that characterizes the multiple stages involved in information seeking with physiological signals collected from multiple sensors. We hope this dataset can serve as a reference for future research on information-seeking behaviors.

cs.HC↗

arXiv2025

GLOSS: Group of LLMs for Open-Ended Sensemaking of Passive Sensing Data for Health and Wellbeing

The ubiquitous presence of smartphones and wearables has enabled researchers to build prediction and detection models for various health and behavior outcomes using passive sensing data from these devices. Achieving a high-level, holistic understanding of an individual's behavior and context, however, remains a significant challenge. Due to the nature of passive sensing data, sensemaking -- the process of interpreting and extracting insights -- requires both domain knowledge and technical expertise, creating barriers for different stakeholders. Existing systems designed to support sensemaking are either not open-ended or cannot perform complex data triangulation. In this paper, we present a novel sensemaking system, Group of LLMs for Open-ended Sensemaking (GLOSS), capable of open-ended sensemaking and performing complex multimodal triangulation to derive insights. We demonstrate that GLOSS significantly outperforms the commonly used Retrieval-Augmented Generation (RAG) technique, achieving 87.93% accuracy and 66.19% consistency, compared to RAG's 29.31% accuracy and 52.85% consistency. Furthermore, we showcase the promise of GLOSS through four use cases inspired by prior and ongoing work in the UbiComp and HCI communities. Finally, we discuss the potential of GLOSS, its broader implications, and the limitations of our work.

cs.HC↗

arXiv2025

Exploring Emotional and Social Dynamics in Mobile Usage During Home Confinement

Home confinement, a situation experienced by individuals for reasons ranging from medical quarantines, rehabilitation needs, disability accommodations, and remote working, is a common yet impactful aspect of modern life. While essential in various scenarios, confinement within the home environment can profoundly influence mental well-being and digital device usage. Using the COVID-19 lockdown as a case study, this research explores the emotional and social effects of prolonged home confinement on mobile device usage. We conducted an in-situ study with 32 participants, analyzing three weeks of mobile usage data to assess emotional well-being and social dynamics in restricted environments. Our findings reveal that app usage patterns serve as strong indicators of emotional states, offering insights into how digital interactions can reflect and influence well-being during isolation. This study highlights the potential for developing targeted interventions and support systems for individuals in long-term home confinement, including those with chronic illness, recovery needs, or permanent remote work situations.

cs.HC↗

arXiv2025

A 2D-CFT Factory: Critical Lattice Models from Competing Anyon Condensation Processes in SymTO/SymTFT

In this paper, we introduce a ``CFT factory'' : a novel algorithm of methodically generating 2D lattice models that would flow to 2D conformal fixed points in the infrared. These 2D models are realised by giving critical boundary conditions to 3D topological orders (symTOs/symTFTs) described by string-net models, often called the strange correlators. We engineer these critical boundary conditions by introducing a commensurate amount of non-commuting anyon condensates. The non-invertible symmetries preserved at the critical point can be controlled by studying a novel ``refined condensation tree''. Our structured method generates an infinite family of critical lattice models, including the A-series minimal models, and uncovers previously unknown critical points. Notably, we find at least three novel critical points (c$\approx 1.3$, $1.8$, and $2.5$ respectively) preserving the Haagerup symmetries, in addition to recovering previously reported ones. The condensation tree, together with a generalised Kramers-Wannier duality, predicts precisely large swathes of phase boundaries, fixes almost completely the global phase diagram, and sieves out second order phase transitions. This is not only illustrated in well-known examples (such as the 8-vertex model related to the $A_5$ category) but also further verified with precision numerics, using our improved (non-invertible) symmetry-preserving tensor-network RG, in novel examples involving the Haagerup symmetries. We show that critical couplings can be precisely encoded in the categorical data (Frobenius algebras and quantum dimensions in unitary fusion categories), thus establishing a powerful, systematic route to discovering and potentially classifying new conformal field theories.

cond-mat.str-el↗

arXiv2024

Simplex tensor network renormalization group for boundary theory of 3+1D symTFT

Following the construction in arXiv:2210.12127, we develop a symmetry-preserving renormalization group (RG) flow for 3D symmetric theories. These theories are expressed as boundary conditions of a symTFT, which in our case is a 3+1D Dijkgraaf-Witten topological theory in the bulk. The boundary is geometrically organized into tetrahedra and represented as a tensor network, which we refer to as the "simplex tensor network" state. Each simplex tensor is assigned indices corresponding to its vertices, edges, and faces. We propose a numerical algorithm to implement RG flows for these boundary conditions, and explicitly demonstrate its application to a $\mathbb{Z}_2$ symmetric theory. By linearly interpolating between three topological fixed-point boundaries, we map the phase transitions characterized by local and non-local order parameters, which respectively detects the breaking of a 0-form and a 2-form symmetry. This formalism is readily extendable to other discrete symmetry groups and, in principle, can be generalized to describe 3D symmetric topological orders.

cond-mat.str-el↗

arXiv2024

Report on the Workshop on Simulations for Information Access (Sim4IA 2024) at SIGIR 2024

This paper is a report of the Workshop on Simulations for Information Access (Sim4IA) workshop at SIGIR 2024. The workshop had two keynotes, a panel discussion, nine lightning talks, and two breakout sessions. Key takeaways were user simulation's importance in academia and industry, the possible bridging of online and offline evaluation, and the issues of organizing a companion shared task around user simulations for information access. We report on how we organized the workshop, provide a brief overview of what happened at the workshop, and summarize the main topics and findings of the workshop and future work.

cs.IR↗

arXiv2024

Inside Out or Not: Privacy Implications of Emotional Disclosure

Privacy is dynamic, sensitive, and contextual, much like our emotions. Previous studies have explored the interplay between privacy and context, privacy and emotion, and emotion and context. However, there remains a significant gap in understanding the interplay of these aspects simultaneously. In this paper, we present a preliminary study investigating the role of emotions in driving individuals' information sharing behaviour, particularly in relation to urban locations and social ties. We adopt a novel methodology that integrates context (location and time), emotion, and personal information sharing behaviour, providing a comprehensive analysis of how contextual emotions affect privacy. The emotions are assessed with both self-reporting and electrodermal activity (EDA). Our findings reveal that self-reported emotions influence personal information-sharing behaviour with distant social groups, while neutral emotions lead individuals to share less precise information with close social circles, a pattern is potentially detectable with wrist-worn EDA. Our study helps lay the foundation for personalised emotion-aware strategies to mitigate oversharing risks and enhance user privacy in the digital age.

cs.CY↗

arXiv2024

Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search

Instruments such as eye-tracking devices have contributed to understanding how users interact with screen-based search engines. However, user-system interactions in audio-only channels -- as is the case for Spoken Conversational Search (SCS) -- are harder to characterize, given the lack of instruments to effectively and precisely capture interactions. Furthermore, in this era of information overload, cognitive bias can significantly impact how we seek and consume information -- especially in the context of controversial topics or multiple viewpoints. This paper draws upon insights from multiple disciplines (including information seeking, psychology, cognitive science, and wearable sensors) to provoke novel conversations in the community. To this end, we discuss future opportunities and propose a framework including multimodal instruments and methods for experimental designs and settings. We demonstrate preliminary results as an example. We also outline the challenges and offer suggestions for adopting this multimodal approach, including ethical considerations, to assist future researchers and practitioners in exploring cognitive biases in SCS.

cs.HC↗

arXiv2024

ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality

This paper explores the intersection of technology and sleep pattern comprehension, presenting a cutting-edge two-stage framework that harnesses the power of Large Language Models (LLMs). The primary objective is to deliver precise sleep predictions paired with actionable feedback, addressing the limitations of existing solutions. This innovative approach involves leveraging the GLOBEM dataset alongside synthetic data generated by LLMs. The results highlight significant improvements, underlining the efficacy of merging advanced machine-learning techniques with a user-centric design ethos. Through this exploration, we bridge the gap between technological sophistication and user-friendly design, ensuring that our framework yields accurate predictions and translates them into actionable insights.

cs.LG↗

arXiv2024

Characterizing Information Seeking Processes with Multiple Physiological Signals

Information access systems are getting complex, and our understanding of user behavior during information seeking processes is mainly drawn from qualitative methods, such as observational studies or surveys. Leveraging the advances in sensing technologies, our study aims to characterize user behaviors with physiological signals, particularly in relation to cognitive load, affective arousal, and valence. We conduct a controlled lab study with 26 participants, and collect data including Electrodermal Activities, Photoplethysmogram, Electroencephalogram, and Pupillary Responses. This study examines informational search with four stages: the realization of Information Need (IN), Query Formulation (QF), Query Submission (QS), and Relevance Judgment (RJ). We also include different interaction modalities to represent modern systems, e.g., QS by text-typing or verbalizing, and RJ with text or audio information. We analyze the physiological signals across these stages and report outcomes of pairwise non-parametric repeated-measure statistical tests. The results show that participants experience significantly higher cognitive loads at IN with a subtle increase in alertness, while QF requires higher attention. QS involves demanding cognitive loads than QF. Affective responses are more pronounced at RJ than QS or IN, suggesting greater interest and engagement as knowledge gaps are resolved. To the best of our knowledge, this is the first study that explores user behaviors in a search process employing a more nuanced quantitative analysis of physiological signals. Our findings offer valuable insights into user behavior and emotional responses in information seeking processes. We believe our proposed methodology can inform the characterization of more complex processes, such as conversational information seeking.

cs.IR↗

arXiv2024

CFT$_D$ from TQFT$_{D+1}$ via Holographic Tensor Network, and Precision Discretisation of CFT$_2$

We show that the path-integral of conformal field theories in $D$ dimensions (CFT$_D$) can be constructed by solving for eigenstates of an RG operator following from the Turaev-Viro formulation of a topological field theory in $D+1$ dimensions (TQFT$_{D+1}$), explicitly realising the holographic sandwich relation between a symmetric theory and a TQFT. Generically, exact eigenstates corresponding to symmetric-TQFT$_D$ follow from Frobenius algebra in the TQFT$_{D+1}$. For $D=2$, we constructed eigenstates that produce 2D rational CFT path-integral exactly, which, curiously connects a continuous field theoretic path-integral with the Turaev-Viro state sum. We also devise and illustrate numerical methods for $D=2,3$ to search for CFT$_D$ as phase transition points between symmetric TQFT$_D$. Finally since the RG operator is in fact an exact analytic holographic tensor network, we compute ``bulk-boundary'' correlator and compare with the AdS/CFT dictionary at $D=2$. Promisingly, they are numerically compatible given our accuracy, although further works will be needed to explore the precise connection to the AdS/CFT correspondence.

hep-th↗

arXiv2024

Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot

Creating and deploying customized applications is crucial for operational success and enriching user experiences in the rapidly evolving modern business world. A prominent facet of modern user experiences is the integration of chatbots or voice assistants. The rapid evolution of Large Language Models (LLMs) has provided a powerful tool to build conversational applications. We present Walert, a customized LLM-based conversational agent able to answer frequently asked questions about computer science degrees and programs at RMIT University. Our demo aims to showcase how conversational information-seeking researchers can effectively communicate the benefits of using best practices to stakeholders interested in developing and deploying LLM-based chatbots. These practices are well-known in our community but often overlooked by practitioners who may not have access to this knowledge. The methodology and resources used in this demo serve as a bridge to facilitate knowledge transfer from experts, address industry professionals' practical needs, and foster a collaborative environment. The data and code of the demo are available at https://github.com/rmit-ir/walert.

cs.IR↗

arXiv2023

Designing and Evaluating Presentation Strategies for Fact-Checked Content

With the rapid growth of online misinformation, it is crucial to have reliable fact-checking methods. Recent research on finding check-worthy claims and automated fact-checking have made significant advancements. However, limited guidance exists regarding the presentation of fact-checked content to effectively convey verified information to users. We address this research gap by exploring the critical design elements in fact-checking reports and investigating whether credibility and presentation-based design improvements can enhance users' ability to interpret the report accurately. We co-developed potential content presentation strategies through a workshop involving fact-checking professionals, communication experts, and researchers. The workshop examined the significance and utility of elements such as veracity indicators and explored the feasibility of incorporating interactive components for enhanced information disclosure. Building on the workshop outcomes, we conducted an online experiment involving 76 crowd workers to assess the efficacy of different design strategies. The results indicate that proposed strategies significantly improve users' ability to accurately interpret the verdict of fact-checking articles. Our findings underscore the critical role of effective presentation of fact reports in addressing the spread of misinformation. By adopting appropriate design enhancements, the effectiveness of fact-checking reports can be maximized, enabling users to make informed judgments.

cs.IR↗

arXiv2023

Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities

Physiological signals can potentially be applied as objective measures to understand the behavior and engagement of users interacting with information access systems. However, the signals are highly sensitive, and many controls are required in laboratory user studies. To investigate the extent to which controlled or uncontrolled (i.e., confounding) variables such as task sequence or duration influence the observed signals, we conducted a pilot study where each participant completed four types of information-processing activities (READ, LISTEN, SPEAK, and WRITE). Meanwhile, we collected data on blood volume pulse, electrodermal activity, and pupil responses. We then used machine learning approaches as a mechanism to examine the influence of controlled and uncontrolled variables that commonly arise in user studies. Task duration was found to have a substantial effect on the model performance, suggesting it represents individual differences rather than giving insight into the target variables. This work contributes to our understanding of such variables in using physiological signals in information retrieval user studies.

cs.IR↗

arXiv2023

Scrambling and Entangling Spinning Particles

In this paper we revisit the gravitational eikonal amplitudes of two scattering spinning particles and inspect their scrambling power in the spin spaces that is quantified through the tripartite information. We found that in the non-relativistic limit and a special high-energy limit the leading contribution is a quantity that is universal and theory independent. The minimal coupling is singled out with minimal scrambling in a different high momenta limit. We also inspected the initial state dependence of entanglement generation and found that the spin coherent state with vanishing spin may not necessarily be the hardest to entangle. Interestingly, among a family of mixed states, the only P-rep state there known to be the best approximation of classical mixed states was singled out as one with minimal entanglement generated.

hep-th↗

arXiv2022

Individual and Group-wise Classroom Seating Experience: Effects on Student Engagement in Different Courses

Seating location in the classroom can affect student engagement, attention and academic performance by providing better visibility, improved movement, and participation in discussions. Existing studies typically explore how traditional seating arrangements (e.g. grouped tables or traditional rows) influence students' perceived engagement, without considering group seating behaviours under more flexible seating arrangements. Furthermore, survey-based measures of student engagement are prone to subjectivity and various response bias. Therefore, in this research, we investigate how individual and group-wise classroom seating experiences affect student engagement using wearable physiological sensors. We conducted a field study at a high school and collected survey and wearable data from 23 students in 10 courses over four weeks. We aim to answer the following research questions: 1. How does the seating proximity between students relate to their perceived learning engagement? 2. How do students' group seating behaviours relate to their physiologically-based measures of engagement (i.e. physiological arousal and physiological synchrony)? Experiment results indicate that the individual and group-wise classroom seating experience is associated with perceived student engagement and physiologically-based engagement measured from electrodermal activity. We also find that students who sit close together are more likely to have similar learning engagement and tend to have high physiological synchrony. This research opens up opportunities to explore the implications of flexible seating arrangements and has great potential to maximize student engagement by suggesting intelligent seating choices in the future.

cs.HC↗