Arrow Research search

Author name cluster

Jing Peng

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

15 papers
2 author rows

Possible papers

15

JBHI Journal 2025 Journal Article

BAHBench: A Unified Benchmark for Evaluating Bio-Acoustic Health With Acoustic Foundation Models

  • Weixiang Xu
  • Zhongren Dong
  • Jing Peng
  • Runming Wang
  • Zixing Zhang

Acoustic foundation models, through self-supervised learning on large amounts of unlabeled speech data, can acquire rich acoustic representations. In recent years, these models have demonstrated substantial potential in audio-based health-related tasks, remarkably enhancing the efficiency and quality of healthcare services and contributing to the advancement of smart healthcare. However, there is currently a lack of systematic research and exploration on the performance of acoustic foundation models in health-related tasks. Furthermore, inconsistencies in evaluation methods and experimental setups hinder fair comparisons between different methods, severely impeding progress in this field. To address these challenges, we establish a unified Benchmark for evaluating Bio-Acoustic health via acoustic foundation models, namely BAHBench. BAHBench encompasses 6 distinct health-related tasks and evaluates 12 acoustic foundation models within a unified evaluation framework and parameter settings, enabling fair comparisons across different models. Our objective is to explore the effectiveness of current acoustic foundation models in health-related tasks. Thus, we discuss the impact of model size and data diversity on performance, and investigate feature selection and efficient fine-tuning strategy. Experimental results show that different health-related tasks benefit from features from different layers of the foundation model, while LoRA fine-tuning further enhances the model's performance on downstream tasks. Our goal is to provide clear and comprehensive guidance for future researchers. The code related to this study will be available to the research community to promote transparency and reproducibility.

Details DOI

AAAI Conference 2020 Conference Paper

Linguistic Fingerprints of Internet Censorship: The Case of Sina Weibo

  • Kei Yin Ng
  • Anna Feldman
  • Jing Peng

This paper studies how the linguistic components of blogposts collected from Sina Weibo, a Chinese microblogging platform, might affect the blogposts’ likelihood of being censored. Our results go along with King et al. (2013)’s Collective Action Potential (CAP) theory, which states that a blogpost’s potential of causing riot or assembly in real life is the key determinant of it getting censored. Although there is not a definitive measure of this construct, the linguistic features that we identify as discriminatory go along with the CAP theory. We build a classifier that significantly outperforms non-expert humans in predicting whether a blogpost will be censored. The crowdsourcing results suggest that while humans tend to see censored blogposts as more controversial and more likely to trigger action in real life than the uncensored counterparts, they in general cannot make a better guess than our model when it comes to ‘reading the mind’ of the censors in deciding whether a blogpost should be censored. We do not claim that censorship is only determined by the linguistic features. There are many other factors contributing to censorship decisions. The focus of the present paper is on the linguistic form of blogposts. Our work suggests that it is possible to use linguistic properties of social media posts to automatically predict if they are going to be censored.

PDF Details

AAAI Conference 2019 Conference Paper

Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation

  • Wei Feng
  • Wentao Liu
  • Tong Li
  • Jing Peng
  • Chen Qian
  • Xiaolin Hu

Human-object interactions (HOI) recognition and pose estimation are two closely related tasks. Human pose is an essential cue for recognizing actions and localizing the interacted objects. Meanwhile, human action and their interacted objects’ localizations provide guidance for pose estimation. In this paper, we propose a turbo learning framework to perform HOI recognition and pose estimation simultaneously. First, two modules are designed to enforce message passing between the tasks, i. e. pose aware HOI recognition module and HOI guided pose estimation module. Then, these two modules form a closed loop to utilize the complementary information iteratively, which can be trained in an end-to-end manner. The proposed method achieves the state-of-the-art performance on two public benchmarks including Verbs in COCO (V-COCO) and HICO-DET datasets.

PDF Details

JAAMAS Journal 2007 Journal Article

Generalized multiagent learning with performance bound

  • Bikramjit Banerjee
  • Jing Peng

Abstract We present new Multiagent learning (MAL) algorithms with the general philosophy of policy convergence against some classes of opponents but otherwise ensuring high payoffs. We consider a 3-class breakdown of opponent types: (eventually) stationary, self-play and “other” (see Definition 4) agents. We start with ReDVaLeR that can satisfy policy convergence against the first two types and no-regret against the third, but it needs to know the type of the opponents. This serves as a baseline to delineate the difficulty of achieving these goals. We show that a simple modification on ReDVaLeR yields a new algorithm, RV σ( t ), that achieves no-regret payoffs in all games, and convergence to Nash equilibria in self-play (and to best response against eventually stationary opponents—a corollary of no-regret) simultaneously, without knowing the opponent types, but in a smaller class of games than ReDVaLeR. RV σ( t ) effectively ensures the performance of a learner during the process of learning, as opposed to the performance of a learned behavior. We show that the expression for regret of RV σ( t ) can have a slightly better form than those of other comparable algorithms like GIGA and GIGA-WoLF though, contrastingly, our analysis is in continuous time. Moreover, experiments show that RV σ( t ) can converge to an equilibrium in some cases where GIGA, GIGA-WoLF would fail, and to better equilibria where GIGA, GIGA-WoLF converge to undesirable equilibria (coordination games). This important class of coordination games also highlights the key desirability of policy convergence as a criterion for MAL in self-play instead of high average payoffs. To our knowledge, this is also the first successful (guaranteed) attempt at policy convergence of a no-regret algorithm in the Shapley game.

Details DOI

AAAI Conference 2004 Conference Paper

Performance Bounded Reinforcement Learning in Strategic Interactions

  • Bikramjit Banerjee
  • Jing Peng

Despite increasing deployment of agent technologies in several business and industry domains, user confidence in fully automated agent driven applications is noticeably lacking. The main reasons for such lack of trust in complete automation are scalability and non-existence of reasonable guarantees in the performance of selfadapting software. In this paper we address the latter issue in the context of learning agents in a Multiagent System (MAS). Performance guarantees for most existing on-line Multiagent Learning (MAL) algorithms are realizable only in the limit, thereby seriously limiting its practical utility. Our goal is to provide certain meaningful guarantees about the performance of a learner in a MAS, while it is learning. In particular, we present a novel MAL algorithm that (i) converges to a best response against stationary opponents, (ii) converges to a Nash equilibrium in self-play and (iii) achieves a constant bounded expected regret at any time (no-averageregret asymptotically) in arbitrary sized general-sum games with non-negative payoffs, and against any number of opponents.

PDF Details

NeurIPS Conference 2000 Conference Paper

An Adaptive Metric Machine for Pattern Classification

  • Carlotta Domeniconi
  • Jing Peng
  • Dimitrios Gunopulos

Nearest neighbor classification assumes locally constant class con(cid: 173) ditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a locally adaptive nearest neighbor classification method to try to minimize bias. We use a Chi-squared distance analysis to compute a flexible metric for pro(cid: 173) ducing neighborhoods that are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities tend to be smoother in the mod(cid: 173) ified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using a variety of real world data.

PDF Details