pwc-archive/papers-with-abstracts huggingface dataset:
Semantic-Enhanced Relational Metric Learning for Recommender Systems
Authors: Mingming Li, Fuqing Zhu, Feng Yuan, Songlin Hu
Date: 2024-06-07 00:00:00
Tasks: Implicit Relations, Metric Learning, Recommendation Systems

Recently, relational metric learning methods have been received great attention in recommendation community, which is inspired by the translation mechanism in knowledge graph. Different from the knowledge graph where the entity-to-entity relations are given in advance, historical interactions lack explicit relations between users and items in recommender systems. Currently, many researchers have succeeded in constructing the implicit relations to remit this issue. However, in previous work, the learning process of the induction function only depends on a single source of data (i.e., user-item interaction) in a supervised manner, resulting in the co-occurrence relation that is free of any semantic information. In this paper, to tackle the above problem in recommender systems, we propose a joint Semantic-Enhanced Relational Metric Learning (SERML) framework that incorporates the semantic information. Specifically, the semantic signal is first extracted from the target reviews containing abundant item features and personalized user preferences. A novel regression model is then designed via leveraging the extracted semantic signal to improve the discriminative ability of original relation-based training process. On four widely-used public datasets, experimental results demonstrate that SERML produces a competitive performance compared with several state-of-the-art methods in recommender systems.
On conceptualisation and an overview of learning path recommender systems in e-learning
Authors: A. Fuster-López, J. M. Cruz, P. Guerrero-García, E. M. T. Hendrix, A. Košir, I. Nowak, L. Oneto, S. Sirmakessis, M. F. Pacheco, F. P. Fernandes, A. I. Pereira
Date: 2024-06-07 00:00:00
Tasks: Recommendation Systems

The use of e-learning systems has a long tradition, where students can study online helped by a system. In this context, the use of recommender systems is relatively new. In our research project, we investigated various ways to create a recommender system. They all aim at facilitating the learning and understanding of a student. We present a common concept of the learning path and its learning indicators and embed 5 different recommenders in this context.
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
Authors: Sheng Zhang, Maolin Wang, Wanyu Wang, Jingtong Gao, Xiangyu Zhao, Yu Yang, Xuetao Wei, Zitao Liu, Tong Xu
Date: 2024-06-06 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Transformer-based models have gained significant traction in sequential recommender systems (SRSs) for their ability to capture user-item interactions effectively. However, these models often suffer from high computational costs and slow inference. Meanwhile, existing efficient SRS approaches struggle to embed high-quality semantic and positional information into latent representations. To tackle these challenges, this paper introduces GLINT-RU, a lightweight and efficient SRS leveraging a single-layer dense selective Gated Recurrent Units (GRU) module to accelerate inference. By incorporating a dense selective gate, GLINT-RU adaptively captures temporal dependencies and fine-grained positional information, generating high-quality latent representations. Additionally, a parallel mixing block infuses fine-grained positional features into user-item interactions, enhancing both recommendation quality and efficiency. Extensive experiments on three datasets demonstrate that GLINT-RU achieves superior prediction accuracy and inference speed, outperforming baselines based on RNNs, Transformers, MLPs, and SSMs. These results establish GLINT-RU as a powerful and efficient solution for SRSs.
Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising
Authors: Chang Zhou, Yang Zhao, Yuelin Zou, Jin Cao, Wenhan Fan, Yi Zhao, Chiyu Cheng
Date: 2024-06-04 00:00:00
Tasks: Click-Through Rate Prediction

This paper proposes new methods to enhance click-through rate (CTR) prediction models using the Deep Interest Network (DIN) model, specifically applied to the advertising system of Alibaba's Taobao platform. Unlike traditional deep learning approaches, this research focuses on localized user behavior activation for tailored ad targeting by leveraging extensive user behavior data. Compared to traditional models, this method demonstrates superior ability to handle diverse and dynamic user data, thereby improving the efficiency of ad systems and increasing revenue.
Towards commands recommender system in BIM authoring tool using transformers
Authors: Changyu Du, Zihan Deng, Stavros Nousias, André Borrmann
Date: 2024-06-02 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

The complexity of BIM software presents significant barriers to the widespread adoption of BIM and model-based design within the Architecture, Engineering, and Construction (AEC) sector. End-users frequently express concerns regarding the additional effort required to create a sufficiently detailed BIM model when compared with conventional 2D drafting. This study explores the potential of sequential recommendation systems to accelerate the BIM modeling process. By treating BIM software commands as recommendable items, we introduce a novel end-to-end approach that predicts the next-best command based on user historical interactions. Our framework extensively preprocesses real-world, large-scale BIM log data, utilizes the transformer architectures from the latest large language models as the backbone network, and ultimately results in a prototype that provides real-time command suggestions within the BIM authoring tool Vectorworks. Subsequent experiments validated that our proposed model outperforms the previous study, demonstrating the immense potential of the recommendation system in enhancing design efficiency.
CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)
Authors: Sajida Mhammedi, Hakim El Massari, Noreddine Gherabi, Amnai Mohamed
Date: 2024-05-31 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. However, due to data overloading, recommender systems struggle to handle large volumes of data reliably and accurately before offering suggestions. The main purpose of this work is to address the recommender system's data sparsity and accuracy problems by using the matrix factorization algorithm of collaborative filtering based on the dimensional reduction method and, more precisely, the Nonnegative Matrix Factorization (NMF) combined with ontology. We tested the method and compared the results to other classic methods. The findings showed that the implemented approach efficiently reduces the sparsity of CF suggestions, improves their accuracy, and gives more relevant items as recommendations.
GNNDLD: Graph Neural Network with Directional Label Distribution
Authors: Chandramani Chaudhary, Nirmal Kumar Boran, N Sangeeth, and Virendra Singh
Date: 2024-02-26 00:00:00
Proceeding: International Conference on Agents and Artificial Intelligence, ICAART 2024 2
Tasks: Graph Neural Network, Node Classification

By leveraging graph structure, Graph Neural Networks (GNN) have emerged as a useful model for graph-based
datasets. While it is widely assumed that GNNs outperform basic neural networks, recent research shows that
for some datasets, neural networks outperform GNNs. Heterophily is one of the primary causes of GNN
performance degradation, and many models have been proposed to handle it. Furthermore, some intrinsic
information in graph structure is often overlooked, such as edge direction. In this work, we propose GNNDLD,
a model which exploits the edge direction and label distribution around a node in varying neighborhoods
(hop-wise). We combine features from all layers to retain both low-pass frequency and high-pass frequency
components of a node because different layers of neural networks provide different types of information. In
addition, to avoid oversmoothing, we decouple the node feature aggregation and transformation operations.
By combining all of these concepts, we present a simple yet very efficient model. Experiments on six standard
real-world datasets show the superiority of GNNDLD over the state-of-the-art models in both homophily and
heterophily.
S2LD: Sparse-to-Local-Dense Matching for Geometry-Guided Correspondence Estimation
Authors: Shenghao Li, Qunfei Zhao, Zeyang Xia
Date: 2023-06-22 00:00:00
Proceeding: IEEE Transactions on Image Processing 2023 6
Tasks: Homography Estimation, Keypoint detection and image matching, Pose Estimation, Visual Localization, Visual Odometry

Establishing reliable correspondences between two views is one of the most important components of various vision tasks. This paper proposes a novel sparse-to-local-dense (S2LD) matching method to conduct fully differentiable correspondence estimation with the prior from epipolar geometry. The sparse-to-local-dense matching asymmetrically establishes correspondences with consistent sub-pixel coordinates while reducing the computation of matching. The salient features are explicitly located, and the description is conditioned on both views with the global receptive field provided by the attention mechanism. The correspondences are progressively established in multiple levels to reduce the underlying re-projection error. We further propose a 3D noise-aware regularizer with differentiable triangulation. Additional guidance from 3D space is encoded by the regularizer in training to handle the supervision noise caused by the errors in camera poses and depth maps. The proposed method demonstrates outstanding matching accuracy and geometric estimation capability on multiple datasets and tasks.
Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning
Authors: Gaoyang Li, Haoran Wang, Mingzi Zhang, Simon Tupin, Aike Qiao, Youjun Liu, Makoto Ohta, Hitomi Anzai
Date: 2021-01-22 00:00:00
Proceeding: journal 2021 1
Tasks: Computational Efficiency, Deep Learning

The clinical treatment planning of coronary heart disease requires hemodynamic parameters
to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the
simulation of cardiovascular hemodynamics. However, for the patient-specific model, the
complex operation and high computational cost of CFD hinder its clinical application. To deal
with these problems, we develop cardiovascular hemodynamic point datasets and a dual
sampling channel deep learning network, which can analyze and reproduce the relationship
between the cardiovascular geometry and internal hemodynamics. The statistical analysis
shows that the hemodynamic prediction results of deep learning are in agreement with the
conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2
million nodes, prediction accuracy of around 90%, computational efficiency to predict car-
diovascular hemodynamics within 1 second, and universality for evaluating complex arterial
system, our deep learning method can meet the needs of most situations.
A 510-nW Wake-Up Keyword-Spotting Chip Using Serial-FFT-Based MFCC and Binarized Depthwise Separable CNN in 28-nm CMOS
Authors: Weiwei Shan, Minhao Yang, Tao Wang, Yicheng Lu, Hao Cai, Lixuan Zhu, Jiaming Xu, Chengjun Wu, Longxing Shi, Senior Member, and Jun Yang, Member, IEEE
Date: 2021-01-01 00:00:00
Proceeding: journal 2021 1
Tasks: Keyword Spotting

We propose a sub-µW always-ON keyword spotting (µKWS) chip for audio wake-up systems. It is mainly
composed of a neural network (NN) and a feature extraction
(FE) circuit. For significantly reducing the memory footprint and
computational load, four techniques are used to achieve ultralow-power consumption: 1) a serial-FFT-based Mel-frequency
cepstrum coefficient circuit is designed for FE, instead of the
common parallel FFT. 2) A small-sized binarized depthwise
separable convolutional NN (DSCNN) is designed as the classifier.
3) A framewise incremental computation technique is devised in
contrast to the conventional whole-word processing. 4) Reduced
computation allows a low system clock frequency, which enables
near-threshold voltage operation, and low leakage memory blocks
are designed to minimize the leakage power. Implemented in
28-nm CMOS technology, this µKWS consumes 0.51 µW at a
40-kHz frequency and a 0.41-V supply, with an area of 0.23 mm2.
Using the Google speech command data set, 97.3% accuracy is
reached for a one-word KWS task and 94.6% for a two-word
task.
A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations
Authors: Jinqiang Wang, Huansheng Ning, Yi Peng, Qikai Wei, Daniel Tesfai, Wenwei Mao, Tao Zhu, Runhe Huang
Date: 2024-06-14 00:00:00
Tasks: Diagnostic, Survey

Large Language Models (LLMs) have demonstrated surprising performance across various natural language processing tasks. Recently, medical LLMs enhanced with domain-specific knowledge have exhibited excellent capabilities in medical consultation and diagnosis. These models can smoothly simulate doctor-patient dialogues and provide professional medical advice. Most medical LLMs are developed through continued training of open-source general LLMs, which require significantly fewer computational resources than training LLMs from scratch. Additionally, this approach offers better patient privacy protection than API-based solutions. Given the above advantages, this survey systematically summarizes how to train medical LLMs based on open-source general LLMs from a more fine-grained perspective. It covers (a) how to acquire training corpus and construct customized medical training sets, (b) how to choose an appropriate training paradigm, (c) how to choose a suitable evaluation benchmark, and (d) existing challenges and promising research directions are discussed. This survey can provide guidance for the development of LLMs focused on various medical applications, such as medical education, diagnostic planning, and clinical assistants. Related resources and supplemental information can be found on the GitHub repository.
Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect Classification
Authors: Seungmi Oh
Date: 2024-05-28 00:00:00
Proceeding: IEEE Access 2024 5
Tasks: Anomaly Detection, Classification, Density Estimation

We propose a novel deep learning-based anomaly detection (AD) system that combines a pixelwise classification network with conditional normalizing flow (CNF) networks by sharing feature extractors. We trained the pixelwise classification network using synthetic abnormal data to fine-tune a pretrained feature extractor of the CNF networks, thereby learning the discriminative features of the in-domain data. After that, we trained the CNF networks using normal data with the fine-tuned feature extractor to estimate the density of normal data. During inference, we detected anomalies by calculating the weighted average of the anomaly scores from the pixelwise classification and CNF networks. Because the proposed system not only has learned the properties of in-domain data but also aggregated the anomaly scores of the classification and CNF networks, it showed significantly improved performance compared to existing methods in experiments using the MvTecAD and BTAD datasets. Moreover, the proposed system does not increase computations intensively since the classification and the density estimation systems share feature extractors.
RAST: Restorable arbitrary style transfer via multi-restoration
Authors: Yingnan Ma, Chenqiu Zhao, Xudong Li, Anup Basu
Date: 2023-12-31 00:00:00
Proceeding: Conference 2023 12
Tasks: Style Transfer

Arbitrary style transfer aims at reproducing the target image with provided artistic or photo-realistic styles. Even though existing approaches can successfully transfer style information, arbitrary style transfer still faces many challenges, such as the content leak issue. To be specific, the embedding of artistic style can lead to content changes. In this paper, we solve the content leak problem from the perspective of image restoration. In particular, an iterative architecture is proposed to achieve the restorable arbitrary style transfer (RAST), which can realize the transmission of both content and style information through the multi-restorations. We control the content-style balance in stylized images by the accuracy of image restoration. In order to ensure the effectiveness of the proposed RAST architecture, we design two novel loss functions: multi-restoration loss and style difference loss. In addition, we propose a new quantitative evaluation method to measure content preservation performance and style embedding performance. Comprehensive experiments comparing with state-of-the-art methods demonstrate that our proposed architecture can produce stylized images with superior performance on content preservation and style embedding.
RAST: Restorable Arbitrary Style Transfer
Authors: Yingnan Ma, Chenqiu Zhao, BINGRAN HUANG, Xudong Li, Anup Basu
Date: 2024-01-22 00:00:00
Proceeding: journal 2024 1
Tasks: Style Transfer

The objective of arbitrary style transfer is to apply a given artistic or photo-realistic style to a target image.
Although current methods have shown some success in transferring style, arbitrary style transfer still has
several issues, including content leakage. Embedding an artistic style can result in unintended changes to the
image content. This article proposes an iterative framework called Restorable Arbitrary Style Transfer (RAST)
to effectively ensure content preservation and mitigate potential alterations to the content information. RAST
can transmit both content and style information through multi-restorations and balance the content-style
tradeoff in stylized images using the image restoration accuracy. To ensure RAST’s effectiveness,we introduce
two novel loss functions: multi-restoration loss and style difference loss.We also propose a new quantitative
evaluation method to assess content preservation and style embedding performance. Experimental results
show that RAST outperforms state-of-the-art methods in generating stylized images that preserve content
and embed style accurately.
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
Authors: Jiasheng Zhang, Jialin Chen, Menglin Yang, Aosong Feng, Shuang Liang, Jie Shao, Rex Ying
Date: 2024-06-17 00:00:00
Tasks: Dynamic graph embedding, Edge Classification, Graph Learning, Link Prediction, Retrieval, Text Generation

Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios, where each node and edge are associated with text descriptions, and both the graph structure and text descriptions evolve over time. Despite their broad applicability, there is a notable scarcity of benchmark datasets tailored to DyTAGs, which hinders the potential advancement in many research fields. To address this gap, we introduce Dynamic Text-attributed Graph Benchmark (DTGB), a collection of large-scale, time-evolving graphs from diverse domains, with nodes and edges enriched by dynamically changing text attributes and categories. To facilitate the use of DTGB, we design standardized evaluation procedures based on four real-world use cases: future link prediction, destination node retrieval, edge classification, and textual relation generation. These tasks require models to understand both dynamic graph structures and natural language, highlighting the unique challenges posed by DyTAGs. Moreover, we conduct extensive benchmark experiments on DTGB, evaluating 7 popular dynamic graph learning algorithms and their variants of adapting to text attributes with LLM embeddings, along with 6 powerful large language models (LLMs). Our results show the limitations of existing models in handling DyTAGs. Our analysis also demonstrates the utility of DTGB in investigating the incorporation of structural and textual dynamics. The proposed DTGB fosters research on DyTAGs and their broad applications. It offers a comprehensive benchmark for evaluating and advancing models to handle the interplay between dynamic graph structures and natural language. The dataset and source code are available at https://github.com/zjs123/DTGB.
ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generation
Authors: In-Chang Baek, Tae-Hwa Park, Jin-Ha Noh, Cheong-mok Bae, Kyung-Joong Kim
Date: 2024-06-07 00:00:00
Tasks: Deep Reinforcement Learning, Language Modeling, Language Modelling, Large Language Model

Driven by the rapid growth of machine learning, recent advances in game artificial intelligence (AI) have significantly impacted productivity across various gaming genres. Reward design plays a pivotal role in training game AI models, wherein researchers implement concepts of specific reward functions. However, despite the presence of AI, the reward design process predominantly remains in the domain of human experts, as it is heavily reliant on their creativity and engineering skills. Therefore, this paper proposes ChatPCG, a large language model (LLM)-driven reward design framework.It leverages human-level insights, coupled with game expertise, to generate rewards tailored to specific game features automatically. Moreover, ChatPCG is integrated with deep reinforcement learning, demonstrating its potential for multiplayer game content generation tasks. The results suggest that the proposed LLM exhibits the capability to comprehend game mechanics and content generation tasks, enabling tailored content generation for a specified game. This study not only highlights the potential for improving accessibility in content generation but also aims to streamline the game AI development process.
Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground Plane Initialization
Authors: Wonho Song, Minho Oh, Jaeyoung Lee, Hyun Myung
Date: 2024-06-14 00:00:00
Tasks: Autonomous Driving, Pose Estimation

With the rapid development of autonomous driving and SLAM technology, the performance of autonomous systems using multimodal sensors highly relies on accurate extrinsic calibration. Addressing the need for a convenient, maintenance-friendly calibration process in any natural environment, this paper introduces Galibr, a fully automatic targetless LiDAR-camera extrinsic calibration tool designed for ground vehicle platforms in any natural setting. The method utilizes the ground planes and edge information from both LiDAR and camera inputs, streamlining the calibration process. It encompasses two main steps: an initial pose estimation algorithm based on ground planes (GP-init), and a refinement phase through edge extraction and matching. Our approach significantly enhances calibration performance, primarily attributed to our novel initial pose estimation method, as demonstrated in unstructured natural environments, including on the KITTI dataset and the KAIST quadruped dataset.