pwc-archive/papers-with-abstracts huggingface dataset:
TaiChi Action Capture and Performance Analysis with Multi-view RGB Cameras
Authors: Jianwei Li, Siyu Mo, Yanfei Shen
Date: 2023-06-26 00:00:00
Tasks: Surface Reconstruction

Recent advances in computer vision and deep learning have influenced the field of sports performance analysis for researchers to track and reconstruct freely moving humans without any marker attachment. However, there are few works for vision-based motion capture and intelligent analysis for professional TaiChi movement. In this paper, we propose a framework for TaiChi performance capture and analysis with multi-view geometry and artificial intelligence technology. The main innovative work is as follows: 1) A multi-camera system suitable for TaiChi motion capture is built and the multi-view TaiChi data is collected and processed; 2) A combination of traditional visual method and implicit neural radiance field is proposed to achieve sparse 3D skeleton fusion and dense 3D surface reconstruction. 3) The normalization modeling of movement sequences is carried out based on motion transfer, so as to realize TaiChi performance analysis for different groups. We have carried out evaluation experiments, and the experimental results have shown the efficiency of our method.
Decentralized Multi-Robot Formation Control Using Reinforcement Learning
Authors: Juraj Obradovic, Marko Krizmancic, Stjepan Bogdan
Date: 2023-06-26 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a decentralized leader-follower multi-robot formation control based on a reinforcement learning (RL) algorithm applied to a swarm of small educational Sphero robots. Since the basic Q-learning method is known to require large memory resources for Q-tables, this work implements the Double Deep Q-Network (DDQN) algorithm, which has achieved excellent results in many robotic problems. To enhance the system behavior, we trained two different DDQN models, one for reaching the formation and the other for maintaining it. The models use a discrete set of robot motions (actions) to adapt the continuous nonlinear system to the discrete nature of RL. The presented approach has been tested in simulation and real experiments which show that the multi-robot system can achieve and maintain a stable formation without the need for complex mathematical models and nonlinear control laws.
Moving Avatars and Agents in Social Extended Reality Environments
Authors: Jann Philipp Freiwald, Susanne Schmidt, Bernhard E. Riecke, Frank Steinicke
Date: 2023-06-26 00:00:00
Tasks: Navigate

Natural interaction between multiple users within a shared virtual environment (VE) relies on each other's awareness of the current position of the interaction partners. This, however, cannot be warranted when users employ noncontinuous locomotion techniques, such as teleportation, which may cause confusion among bystanders. In this paper, we pursue two approaches to create a pleasant experience for both the moving user and the bystanders observing that movement. First, we will introduce a Smart Avatar system that delivers continuous full-body human representations for noncontinuous locomotion in shared virtual reality (VR) spaces. Smart Avatars imitate their assigned user's real-world movements when close-by and autonomously navigate to their user when the distance between them exceeds a certain threshold, i.e., after the user teleports. As part of the Smart Avatar system, we implemented four avatar transition techniques and compared them to conventional avatar locomotion in a user study, revealing significant positive effects on the observer's spatial awareness, as well as pragmatic and hedonic quality scores. Second, we introduce the concept of Stuttered Locomotion, which can be applied to any continuous locomotion method. By converting a continuous movement into short-interval teleport steps, we provide the merits of non-continuous locomotion for the moving user while observers can easily keep track of their path. Thus, while the experience for observers is similarly positive as with continuous motion, a user study confirmed that Stuttered Locomotion can significantly reduce the occurrence of cybersickness symptoms for the moving user, making it an attractive choice for shared VEs. We will discuss the potential of Smart Avatars and Stuttered Locomotion for shared VR experiences, both when applied individually and in combination.
Medical Federated Model with Mixture of Personalized and Sharing Components
Authors: Yawei Zhao, Qinghe Liu, Xinwang Liu, Kunlun He
Date: 2023-06-26 00:00:00
Tasks: Federated Learning, Tumor Segmentation

Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical data among multiple institutes. Therefore, it has draw much attention due to its natural merit on privacy protection. However, when heterogenous medical data exists between different hospitals, federated learning usually has to face with degradation of performance. In the paper, we propose a new personalized framework of federated learning to handle the problem. It successfully yields personalized models based on awareness of similarity between local data, and achieves better tradeoff between generalization and personalization than existing methods. After that, we further design a differentially sparse regularizer to improve communication efficiency during procedure of model training. Additionally, we propose an effective method to reduce the computational cost, which improves computation efficiency significantly. Furthermore, we collect 5 real medical datasets, including 2 public medical image datasets and 3 private multi-center clinical diagnosis datasets, and evaluate its performance by conducting nodule classification, tumor segmentation, and clinical risk prediction tasks. Comparing with 13 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency. Source code is public, and can be accessed at: https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code.
STEF-DHNet: Spatiotemporal External Factors Based Deep Hybrid Network for Enhanced Long-Term Taxi Demand Prediction
Authors: Sheraz Hassan, Muhammad Tahir, Momin Uppal, Zubair Khalid, Ivan Gorban, Selim Turki
Date: 2023-06-26 00:00:00

Accurately predicting the demand for ride-hailing services can result in significant benefits such as more effective surge pricing strategies, improved driver positioning, and enhanced customer service. By understanding the demand fluctuations, companies can anticipate and respond to consumer requirements more efficiently, leading to increased efficiency and revenue. However, forecasting demand in a particular region can be challenging, as it is influenced by several external factors, such as time of day, weather conditions, and location. Thus, understanding and evaluating these factors is essential for predicting consumer behavior and adapting to their needs effectively. Grid-based deep learning approaches have proven effective in predicting regional taxi demand. However, these models have limitations in integrating external factors in their spatiotemporal complexity and maintaining high accuracy over extended time horizons without continuous retraining, which makes them less suitable for practical and commercial applications. To address these limitations, this paper introduces STEF-DHNet, a demand prediction model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to integrate external features as spatiotemporal information and capture their influence on ride-hailing demand. The proposed model is evaluated using a long-term performance metric called the rolling error, which assesses its ability to maintain high accuracy over long periods without retraining. The results show that STEF-DHNet outperforms existing state-of-the-art methods on three diverse datasets, demonstrating its potential for practical use in real-world scenarios.
Knowledge Graph-Augmented Korean Generative Commonsense Reasoning
Authors: Dahyun Jung, Jaehyung Seo, Jaewook Lee, Chanjun Park, Heuiseok Lim
Date: 2023-06-26 00:00:00
Tasks: Text Generation

Generative commonsense reasoning refers to the task of generating acceptable and logical assumptions about everyday situations based on commonsense understanding. By utilizing an existing dataset such as Korean CommonGen, language generation models can learn commonsense reasoning specific to the Korean language. However, language models often fail to consider the relationships between concepts and the deep knowledge inherent to concepts. To address these limitations, we propose a method to utilize the Korean knowledge graph data for text generation. Our experimental result shows that the proposed method can enhance the efficiency of Korean commonsense inference, thereby underlining the significance of employing supplementary data.
A General Framework for Sequential Decision-Making under Adaptivity Constraints
Authors: Nuoya Xiong, Zhaoran Wang, Zhuoran Yang
Date: 2023-06-26 00:00:00
Tasks: Decision Making, Sequential Decision Making

We take the first step in studying general sequential decision-making under two adaptivity constraints: rare policy switch and batch learning. First, we provide a general class called the Eluder Condition class, which includes a wide range of reinforcement learning classes. Then, for the rare policy switch constraint, we provide a generic algorithm to achieve a $\widetilde{\mathcal{O}}(\log K) $ switching cost with a $\widetilde{\mathcal{O}}(\sqrt{K})$ regret on the EC class. For the batch learning constraint, we provide an algorithm that provides a $\widetilde{\mathcal{O}}(\sqrt{K}+K/B)$ regret with the number of batches $B.$ This paper is the first work considering rare policy switch and batch learning under general function classes, which covers nearly all the models studied in the previous works such as tabular MDP (Bai et al. 2019; Zhang et al. 2020), linear MDP (Wang et al. 2021; Gao et al. 2021), low eluder dimension MDP (Kong et al. 2021; Gao et al. 2021), generalized linear function approximation (Qiao et al. 2023), and also some new classes such as the low $D_\Delta$-type Bellman eluder dimension problem, linear mixture MDP, kernelized nonlinear regulator and undercomplete partially observed Markov decision process (POMDP).
Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation
Authors: Weinan Song, Yaxuan Zhu, Lei He, YingNian Wu, Jianwen Xie
Date: 2023-06-26 00:00:00
Tasks: Image-to-Image Translation

This paper studies a novel energy-based cooperative learning framework for multi-domain image-to-image translation. The framework consists of four components: descriptor, translator, style encoder, and style generator. The descriptor is a multi-head energy-based model that represents a multi-domain image distribution. The components of translator, style encoder, and style generator constitute a diversified image generator. Specifically, given an input image from a source domain, the translator turns it into a stylised output image of the target domain according to a style code, which can be inferred by the style encoder from a reference image or produced by the style generator from a random noise. Since the style generator is represented as an domain-specific distribution of style codes, the translator can provide a one-to-many transformation (i.e., diversified generation) between source domain and target domain. To train our framework, we propose a likelihood-based multi-domain cooperative learning algorithm to jointly train the multi-domain descriptor and the diversified image generator (including translator, style encoder, and style generator modules) via multi-domain MCMC teaching, in which the descriptor guides the diversified image generator to shift its probability density toward the data distribution, while the diversified image generator uses its randomly translated images to initialize the descriptor's Langevin dynamics process for efficient sampling.
A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction
Authors: Chengliang Liu, Binhua Huang, YiWen Liu, Yuanzhe Su, Ke Mai, Yupo Zhang, Zhengkun Yi, Xinyu Wu
Date: 2023-06-26 00:00:00
Tasks: Contrastive Learning, Prediction

In this paper, we investigate the effectiveness of contrastive learning methods for predicting grasp outcomes in an unsupervised manner. By utilizing a publicly available dataset, we demonstrate that contrastive learning methods perform well on the task of grasp outcomes prediction. Specifically, the dynamic-dictionary-based method with the momentum updating technique achieves a satisfactory accuracy of 81.83% using data from one single tactile sensor, outperforming other unsupervised methods. Our results reveal the potential of contrastive learning methods for applications in the field of robot grasping and highlight the importance of accurate grasp prediction for achieving stable grasps.
A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation
Authors: Siqi Lai, Weijia Zhang, Hao liu
Date: 2023-06-26 00:00:00
Tasks: Memorization

Vehicle Energy Consumption (VEC) estimation aims to predict the total energy required for a given trip before it starts, which is of great importance to trip planning and transportation sustainability. Existing approaches mainly focus on extracting statistically significant factors from typical trips to improve the VEC estimation. However, the energy consumption of each vehicle may diverge widely due to the personalized driving behavior under varying travel contexts. To this end, this paper proposes a preference-aware meta-optimization framework Meta-Pec for personalized vehicle energy consumption estimation. Specifically, we first propose a spatiotemporal behavior learning module to capture the latent driver preference hidden in historical trips. Moreover, based on the memorization of driver preference, we devise a selection-based driving behavior prediction module to infer driver-specific driving patterns on a given route, which provides additional basis and supervision signals for VEC estimation. Besides, a driver-specific meta-optimization scheme is proposed to enable fast model adaption by learning and sharing transferable knowledge globally. Extensive experiments on two real-world datasets show the superiority of our proposed framework against ten numerical and data-driven machine learning baselines. The source code is available at https://github.com/usail-hkust/Meta-Pec.
Anomaly Detection with Score Distribution Discrimination
Authors: Minqi Jiang, Songqiao Han, Hailiang Huang
Date: 2023-06-26 00:00:00
Tasks: Anomaly Detection

Recent studies give more attention to the anomaly detection (AD) methods that can leverage a handful of labeled anomalies along with abundant unlabeled data. These existing anomaly-informed AD methods rely on manually predefined score target(s), e.g., prior constant or margin hyperparameter(s), to realize discrimination in anomaly scores between normal and abnormal data. However, such methods would be vulnerable to the existence of anomaly contamination in the unlabeled data, and also lack adaptation to different data scenarios. In this paper, we propose to optimize the anomaly scoring function from the view of score distribution, thus better retaining the diversity and more fine-grained information of input data, especially when the unlabeled data contains anomaly noises in more practical AD scenarios. We design a novel loss function called Overlap loss that minimizes the overlap area between the score distributions of normal and abnormal samples, which no longer depends on prior anomaly score targets and thus acquires adaptability to various datasets. Overlap loss consists of Score Distribution Estimator and Overlap Area Calculation, which are introduced to overcome challenges when estimating arbitrary score distributions, and to ensure the boundness of training loss. As a general loss component, Overlap loss can be effectively integrated into multiple network architectures for constructing AD models. Extensive experimental results indicate that Overlap loss based AD models significantly outperform their state-of-the-art counterparts, and achieve better performance on different types of anomalies.
Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction
Authors: Chanjun Park, Seonmin Koo, Seolhwa Lee, Jaehyung Seo, Sugyeong Eo, Hyeonseok Moon, Heuiseok Lim
Date: 2023-06-26 00:00:00
Tasks: Grammatical Error Correction

Data-centric AI approach aims to enhance the model performance without modifying the model and has been shown to impact model performance positively. While recent attention has been given to data-centric AI based on synthetic data, due to its potential for performance improvement, data-centric AI has long been exclusively validated using real-world data and publicly available benchmark datasets. In respect of this, data-centric AI still highly depends on real-world data, and the verification of models using synthetic data has not yet been thoroughly carried out. Given the challenges above, we ask the question: Does data quality control (noise injection and balanced data), a data-centric AI methodology acclaimed to have a positive impact, exhibit the same positive impact in models trained solely with synthetic data? To address this question, we conducted comparative analyses between models trained on synthetic and real-world data based on grammatical error correction (GEC) task. Our experimental results reveal that the data quality control method has a positive impact on models trained with real-world data, as previously reported in existing studies, while a negative impact is observed in models trained solely on synthetic data.
Transcending Traditional Boundaries: Leveraging Inter-Annotator Agreement (IAA) for Enhancing Data Management Operations (DMOps)
Authors: Damrin Kim, NamHyeok Kim, Chanjun Park, Harksoo Kim
Date: 2023-06-26 00:00:00
Tasks: Management

This paper presents a novel approach of leveraging Inter-Annotator Agreement (IAA), traditionally used for assessing labeling consistency, to optimize Data Management Operations (DMOps). We advocate for the use of IAA in predicting the labeling quality of individual annotators, leading to cost and time efficiency in data production. Additionally, our work highlights the potential of IAA in forecasting document difficulty, thereby boosting the data construction process's overall efficiency. This research underscores IAA's broader application potential in data-driven research optimization and holds significant implications for large-scale data projects prioritizing efficiency, cost reduction, and high-quality data.
Inter-Annotator Agreement in the Wild: Uncovering Its Emerging Roles and Considerations in Real-World Scenarios
Authors: NamHyeok Kim, Chanjun Park
Date: 2023-06-26 00:00:00

Inter-Annotator Agreement (IAA) is commonly used as a measure of label consistency in natural language processing tasks. However, in real-world scenarios, IAA has various roles and implications beyond its traditional usage. In this paper, we not only consider IAA as a measure of consistency but also as a versatile tool that can be effectively utilized in practical applications. Moreover, we discuss various considerations and potential concerns when applying IAA and suggest strategies for effectively navigating these challenges.
Few-Shot Continual Learning via Flat-to-Wide Approaches
Authors: Muhammad Anwar Ma'sum, Mahardhika Pratama, Edwin Lughofer, Lin Liu, Habibullah, Ryszard Kowalczyk
Date: 2023-06-26 00:00:00
Tasks: Continual Learning, Data Augmentation

Existing approaches on continual learning call for a lot of samples in their training processes. Such approaches are impractical for many real-world problems having limited samples because of the overfitting problem. This paper proposes a few-shot continual learning approach, termed FLat-tO-WidE AppRoach (FLOWER), where a flat-to-wide learning process finding the flat-wide minima is proposed to address the catastrophic forgetting problem. The issue of data scarcity is overcome with a data augmentation approach making use of a ball generator concept to restrict the sampling space into the smallest enclosing ball. Our numerical studies demonstrate the advantage of FLOWER achieving significantly improved performances over prior arts notably in the small base tasks. For further study, source codes of FLOWER, competitor algorithms and experimental logs are shared publicly in \url{https://github.com/anwarmaxsum/FLOWER}.
Smart Transformation of EFL Teaching and Learning Approaches
Authors: Md. Russell Talukder
Date: 2023-06-25 00:00:00
Tasks: Attribute

The calibration of the EFL teaching and learning approaches with Artificial Intelligence can potentially facilitate a smart transformation, fostering a personalized and engaging experience in teaching and learning among the stakeholders. The paper focuses on developing an EFL Big Data Ecosystem that is based on Big Data, Analytics, Machine Learning and cluster domain of EFL teaching and learning contents. Accordingly, the paper uses two membranes to construe its framework, namely (i) Open Big Data Membrane that stores random data collected from various source domains and (ii) Machine Learning Membrane that stores specially prepared structured and semi-structured data. Theoretically, the structured and semi structured data are to be prepared skill-wise, attribute-wise, method-wise, and preference-wise to accommodate the personalized preferences and diverse teaching and learning needs of different individuals. The ultimate goal is to optimize the learning experience by leveraging machine learning to create tailored content that aligns with the diverse teaching and learning needs of the EFL communities.
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
Date: 2023-06-25 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Causal Inference

The aim of this paper is to make clear and precise the relationship between the Rubin causal model (RCM) and structural causal model (SCM) frameworks for causal inference. Adopting a neutral logical perspective, and drawing on previous work, we show what is required for an RCM to be representable by an SCM. A key result then shows that every RCM -- including those that violate algebraic principles implied by the SCM framework -- emerges as an abstraction of some representable RCM. Finally, we illustrate the power of this conciliatory perspective by pinpointing an important role for SCM principles in classic applications of RCMs; conversely, we offer a characterization of the algebraic constraints implied by a graph, helping to substantiate further comparisons between the two frameworks.