arXiv subjects

Jiachen Du

Publications and source records attributed to Jiachen Du.

6 recordsLinked to original sources

arXiv2026

SkinSpline: A Body-Attached Skeleton-Supported Haptic Interface for Continuous Skin Deformation through Physical Interpolation

We present SkinSpline, a body-attached skeleton-supported haptic interface that renders continuous skin deformation through physical interpolation of sparse mechanical actuation. SkinSpline combines a low-resolution array of rack-and-pinion linear actuators with an elastic interlocking skeleton that transforms discrete actuator motions into smooth surface deformation, enabling continuous cutaneous feedback without dense actuator arrays. The system includes a modular hardware architecture, a configurable control pipeline, and a visual interface supporting real-time configuration and actuation. We demonstrate SkinSpline through multiple scenarios, including wave rendering, video-synchronized rhythmic touch, visually driven water-wave feedback in VR, and sensor-based remote touch reproduction. SkinSpline explores an alternative approach to continuous on-body haptic rendering by leveraging structural coupling between sparse actuation and deformable surfaces.

cs.HC↗

arXiv2026

VisGuardian: A Lightweight Group-based Privacy Control Technique For Front Camera Data From AR Glasses in Home Environments

Always-on sensing of AI applications on AR glasses makes traditional permission techniques ill-suited for context-dependent visual data, especially within home environments. The home presents a highly challenging privacy context due to the high density of sensitive objects, and the frequent presence of non-consenting family members, and the intimate nature of daily routines, making it a critical focus area for scalable privacy control mechanisms. Existing fine-grained controls, while offering nuanced choices, are inefficient for managing multiple private objects. We propose VisGuardian, a fine-grained content-based visual permission technique for AR glasses. VisGuardian features a group-based control mechanism that enables users to efficiently manage permissions for multiple private objects. VisGuardian detects objects using YOLO and adopts a pre-classified schema to group them. By selecting a single object, users can efficiently obscure groups of related objects based on criteria including privacy sensitivity, object category, or spatial proximity. A technical evaluation shows VisGuardian achieves mAP50 of 0.6704 with only 14.0 ms latency and a 1.7% increase in battery consumption per hour. Furthermore, a user study (N=24) comparing VisGuardian to slider-based and object-based baselines found it to be significantly faster for setting permissions and was preferred by users for its efficiency, effectiveness, and ease of use.

cs.HC↗

arXiv2023

DiVa: An Iterative Framework to Harvest More Diverse and Valid Labels from User Comments for Music

Towards sufficient music searching, it is vital to form a complete set of labels for each song. However, current solutions fail to resolve it as they cannot produce diverse enough mappings to make up for the information missed by the gold labels. Based on the observation that such missing information may already be presented in user comments, we propose to study the automated music labeling in an essential but under-explored setting, where the model is required to harvest more diverse and valid labels from the users' comments given limited gold labels. To this end, we design an iterative framework (DiVa) to harvest more $\underline{\text{Di}}$verse and $\underline{\text{Va}}$lid labels from user comments for music. The framework makes a classifier able to form complete sets of labels for songs via pseudo-labels inferred from pre-trained classifiers and a novel joint score function. The experiment on a densely annotated testing set reveals the superiority of the Diva over state-of-the-art solutions in producing more diverse labels missed by the gold labels. We hope our work can inspire future research on automated music labeling.

cs.IR↗

arXiv2022

Empathetic Response Generation with State Management

A good empathetic dialogue system should first track and understand a user's emotion and then reply with an appropriate emotion. However, current approaches to this task either focus on improving the understanding of users' emotion or on proposing better responding strategies, and very few works consider both at the same time. Our work attempts to fill this vacancy. Inspired by task-oriented dialogue systems, we propose a novel empathetic response generation model with emotion-aware dialogue management. The emotion-aware dialogue management contains two parts: (1) Emotion state tracking maintains the current emotion state of the user and (2) Empathetic dialogue policy selection predicts a target emotion and a user's intent based on the results of the emotion state tracking. The predicted information is then used to guide the generation of responses. Experimental results show that dynamically managing different information can help the model generate more empathetic responses compared with several baselines under both automatic and human evaluations.

cs.CL↗

arXiv2019

Context-aware Embedding for Targeted Aspect-based Sentiment Analysis

Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA). However, existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. This may result in targets or aspects having the same vector representations in different contexts and losing the context-dependent information. To address this problem, we propose a novel method to refine the embeddings of targets and aspects. Such pivotal embedding refinement utilizes a sparse coefficient vector to adjust the embeddings of target and aspect from the context. Hence the embeddings of targets and aspects can be refined from the highly correlative words instead of using context-independent or randomly initialized vectors. Experiment results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.

cs.CL↗

arXiv2017

A Question Answering Approach to Emotion Cause Extraction

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure.

cs.CL↗