pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing and controlling diversity in quantum-behaved particle swarm optimization
Authors: Li-Wei Li, Jun Sun, Chao Li, Wei Fang, Vasile Palade, Xiao-Jun Wu
Date: 2023-08-09 00:00:00
Tasks: Diversity

This paper addresses the issues of controlling and analyzing the population diversity in quantum-behaved particle swarm optimization (QPSO), which is an optimization approach motivated by concepts in quantum mechanics and PSO. In order to gain an in-depth understanding of the role the diversity plays in the evolving process, we first define the genotype diversity by the distance to the average point of the particles' positions and the phenotype diversity by the fitness values for the QPSO. Then, the correlations between the two types of diversities and the search performance are tested and analyzed on several benchmark functions, and the distance-to-average-point diversity is showed to have stronger association with the search performance during the evolving processes. Finally, in the light of the performed diversity analyses, two strategies for controlling the distance-to-average-point diversities are proposed for the purpose of improving the search ability of the QPSO algorithm. Empirical studies on the QPSO with the introduced diversity control methods are performed on a set of benchmark functions from the CEC 2005 benchmark suite. The performance of the proposed methods are evaluated and compared with the original QPSO and other PSO variants.
Spectral Regularized Kernel Goodness-of-Fit Tests
Authors: Omar Hagrass, Bharath K. Sriperumbudur, Bing Li
Date: 2023-08-08 00:00:00

Maximum mean discrepancy (MMD) has enjoyed a lot of success in many machine learning and statistical applications, including non-parametric hypothesis testing, because of its ability to handle non-Euclidean data. Recently, it has been demonstrated in Balasubramanian et al.(2021) that the goodness-of-fit test based on MMD is not minimax optimal while a Tikhonov regularized version of it is, for an appropriate choice of the regularization parameter. However, the results in Balasubramanian et al. (2021) are obtained under the restrictive assumptions of the mean element being zero, and the uniform boundedness condition on the eigenfunctions of the integral operator. Moreover, the test proposed in Balasubramanian et al. (2021) is not practical as it is not computable for many kernels. In this paper, we address these shortcomings and extend the results to general spectral regularizers that include Tikhonov regularization.
Dual Intents Graph Modeling for User-centric Group Discovery
Authors: Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang
Date: 2023-08-09 00:00:00
Tasks: Representation Learning, Self-Supervised Learning

Online groups have become increasingly prevalent, providing users with space to share experiences and explore interests. Therefore, user-centric group discovery task, i.e., recommending groups to users can help both users' online experiences and platforms' long-term developments. Existing recommender methods can not deal with this task as modeling user-group participation into a bipartite graph overlooks their item-side interests. Although there exist a few works attempting to address this task, they still fall short in fully preserving the social context and ensuring effective interest representation learning. In this paper, we focus on exploring the intents that motivate users to participate in groups, which can be categorized into different types, like the social-intent and the personal interest-intent. The former refers to users joining a group affected by their social links, while the latter relates to users joining groups with like-minded people for self-enjoyment. To comprehend different intents, we propose a novel model, DiRec, that first models each intent separately and then fuses them together for predictions. Specifically, for social-intent, we introduce the hypergraph structure to model the relationship between groups and members, leading to a richer understanding of the social context. As for interest-intent, we employ novel structural refinement on the interactive graph to uncover more intricate user behaviors and group interests, realizing better representation learning of interests. Furthermore, we also observe the intent overlapping in real-world scenarios and devise a novel self-supervised learning loss that encourages such alignment for final recommendations. Extensive experiments on three public datasets show the significant improvement of DiRec over the state-of-the-art methods.
Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation
Authors: Chang Meng, Chenhao Zhai, Yu Yang, Hengyu Zhang, Xiu Li
Date: 2023-08-09 00:00:00
Tasks: Multi-Task Learning, Prediction

Multi-behavior recommendation algorithms aim to leverage the multiplex interactions between users and items to learn users' latent preferences. Recent multi-behavior recommendation frameworks contain two steps: fusion and prediction. In the fusion step, advanced neural networks are used to model the hierarchical correlations between user behaviors. In the prediction step, multiple signals are utilized to jointly optimize the model with a multi-task learning (MTL) paradigm. However, recent approaches have not addressed the issue caused by imbalanced data distribution in the fusion step, resulting in the learned relationships being dominated by high-frequency behaviors. In the prediction step, the existing methods use a gate mechanism to directly aggregate expert information generated by coupling input, leading to negative information transfer. To tackle these issues, we propose a Parallel Knowledge Enhancement Framework (PKEF) for multi-behavior recommendation. Specifically, we enhance the hierarchical information propagation in the fusion step using parallel knowledge (PKF). Meanwhile, in the prediction step, we decouple the representations to generate expert information and introduce a projection mechanism during aggregation to eliminate gradient conflicts and alleviate negative transfer (PME). We conduct comprehensive experiments on three real-world datasets to validate the effectiveness of our model. The results further demonstrate the rationality and effectiveness of the designed PKF and PME modules. The source code and datasets are available at https://github.com/MC-CV/PKEF.
DiVa: An Iterative Framework to Harvest More Diverse and Valid Labels from User Comments for Music
Authors: Hongru Liang, Jingyao Liu, Yuanxin Xiang, Jiachen Du, Lanjun Zhou, Shushen Pan, Wenqiang Lei
Date: 2023-08-09 00:00:00
Tasks: valid

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.
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Authors: Yunfeng Zhao, Xu Yan, Xiaoqiang Gui, Shuguang Han, Xiang-Rong Sheng, Guoxian Yu, Jufeng Chen, Zhao Xu, Bo Zheng
Date: 2023-08-09 00:00:00
Tasks: Prediction, Recommendation Systems, Selection bias

Conversion rate (CVR) prediction is an essential task for large-scale e-commerce platforms. However, refund behaviors frequently occur after conversion in online shopping systems, which drives us to pay attention to effective conversion for building healthier shopping services. This paper defines the probability of item purchasing without any subsequent refund as an effective conversion rate (ECVR). A simple paradigm for ECVR prediction is to decompose it into two sub-tasks: CVR prediction and post-conversion refund rate (RFR) prediction. However, RFR prediction suffers from data sparsity (DS) and sample selection bias (SSB) issues, as the refund behaviors are only available after user purchase. Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training. Previous studies mainly focus on tackling DS and SSB or delayed feedback for a single event. To jointly tackle these issues in ECVR prediction, we propose an Entire space CAscade Delayed feedback modeling (ECAD) method. Specifically, ECAD deals with DS and SSB by constructing two tasks including CVR prediction and conversion \& refund rate (CVRFR) prediction using the entire space modeling framework. In addition, it carefully schedules auxiliary tasks to leverage both conversion and refund time within data to alleviate CDF. Experimental results on the offline industrial dataset and online A/B testing demonstrate the effectiveness of ECAD. In addition, ECAD has been deployed in one of the recommender systems in Alibaba, contributing to a significant improvement of ECVR.
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark
Authors: Hung Phan, Ali Jannesari
Date: 2023-08-09 00:00:00
Tasks: Code Search, Code Translation, Machine Translation, NMT, Retrieval, Translation

Neural Machine Translation (NMT) is widely applied in software engineering tasks. The effectiveness of NMT for code retrieval relies on the ability to learn from the sequence of tokens in the source language to the sequence of tokens in the target language. While NMT performs well in pseudocode-to-code translation, it might have challenges in learning to translate from natural language query to source code in newly curated real-world code documentation/ implementation datasets. In this work, we analyze the performance of NMT in natural language-to-code translation in the newly curated CAT benchmark that includes the optimized versions of three Java datasets TLCodeSum, CodeSearchNet, Funcom, and a Python dataset PCSD. Our evaluation shows that NMT has low accuracy, measured by CrystalBLEU and Meteor metrics in this task. To alleviate the duty of NMT in learning complex representation of source code, we propose ASTTrans Representation, a tailored representation of an Abstract Syntax Tree (AST) using a subset of non-terminal nodes. We show that the classical approach NMT performs significantly better in learning ASTTrans Representation over code tokens with up to 36% improvement on Meteor score. Moreover, we leverage ASTTrans Representation to conduct combined code search processes from the state-of-the-art code search processes using GraphCodeBERT and UniXcoder. Our NMT models of learning ASTTrans Representation can boost the Mean Reciprocal Rank of these state-of-the-art code search processes by up to 3.08% and improve 23.08% of queries' results over the CAT benchmark.
Unified Matrix Factorization with Dynamic Multi-view Clustering
Authors: Shangde Gao, Ke Liu, Yichao Fu
Date: 2023-08-09 00:00:00
Tasks: Clustering, Collaborative Filtering, Recommendation Systems

Matrix factorization (MF) is a classical collaborative filtering algorithm for recommender systems. It decomposes the user-item interaction matrix into a product of low-dimensional user representation matrix and item representation matrix. In typical recommendation scenarios, the user-item interaction paradigm is usually a two-stage process and requires static clustering analysis of the obtained user and item representations. The above process, however, is time and computationally intensive, making it difficult to apply in real-time to e-commerce or Internet of Things environments with billions of users and trillions of items. To address this, we propose a unified matrix factorization method based on dynamic multi-view clustering (MFDMC) that employs an end-to-end training paradigm. Specifically, in each view, a user/item representation is regarded as a weighted projection of all clusters. The representation of each cluster is learnable, enabling the dynamic discarding of bad clusters. Furthermore, we employ multi-view clustering to represent multiple roles of users/items, effectively utilizing the representation space and improving the interpretability of the user/item representations for downstream tasks. Extensive experiments show that our proposed MFDMC achieves state-of-the-art performance on real-world recommendation datasets. Additionally, comprehensive visualization and ablation studies interpretably confirm that our method provides meaningful representations for downstream tasks of users/items.
Changes in Policy Preferences in German Tweets during the COVID Pandemic
Authors: Felix Biessmann
Date: 2023-07-31 00:00:00
Tasks: text-classification, Text Classification

Online social media have become an important forum for exchanging political opinions. In response to COVID measures citizens expressed their policy preferences directly on these platforms. Quantifying political preferences in online social media remains challenging: The vast amount of content requires scalable automated extraction of political preferences -- however fine grained political preference extraction is difficult with current machine learning (ML) technology, due to the lack of data sets. Here we present a novel data set of tweets with fine grained political preference annotations. A text classification model trained on this data is used to extract policy preferences in a German Twitter corpus ranging from 2019 to 2022. Our results indicate that in response to the COVID pandemic, expression of political opinions increased. Using a well established taxonomy of policy preferences we analyse fine grained political views and highlight changes in distinct political categories. These analyses suggest that the increase in policy preference expression is dominated by the categories pro-welfare, pro-education and pro-governmental administration efficiency. All training data and code used in this study are made publicly available to encourage other researchers to further improve automated policy preference extraction methods. We hope that our findings contribute to a better understanding of political statements in online social media and to a better assessment of how COVID measures impact political preferences.
Who should I Collaborate with? A Comparative Study of Academia and Industry Research Collaboration in NLP
Authors: Hussain Sadiq Abuwala, Bohan Zhang, Mushi Wang
Date: 2023-07-21 00:00:00

The goal of our research was to investigate the effects of collaboration between academia and industry on Natural Language Processing (NLP). To do this, we created a pipeline to extract affiliations and citations from NLP papers and divided them into three categories: academia, industry, and hybrid (collaborations between academia and industry). Our empirical analysis found that there is a trend towards an increase in industry and academia-industry collaboration publications and that these types of publications tend to have a higher impact compared to those produced solely within academia.
D-Net: A Generalised and Optimised Deep Network for Monocular Depth Estimation
Authors: Joshua Luke Thompson, Son Lam Phung, Abdesselam Bouzerdoum
Date: 2021-09-29 00:00:00
Proceeding: IEEE Access 2021 9
Tasks: Depth Estimation, Monocular Depth Estimation, Scene Understanding

Depth estimation is an essential component in computer vision systems for achieving 3D scene understanding. Efficient and accurate depth map estimation has numerous applications including self-driving vehicles and virtual reality tools. This paper presents a new deep network, called D-Net, for depth estimation from a single RGB image. The proposed network can be trained end-to-end, and its structure can be customised to meet different requirements in model size, speed, and prediction accuracy. Our approach gathers strong global and local contextual features at multiple resolutions, and then transfers these to high resolutions for clearer depth maps. For the encoder backbone, D-Net can utilise many state-of-the-art models including EfficientNet, HRNet and Swin Transformer to obtain dense depth maps. The proposed D-net is designed to have minimal parameters and reduced computational complexity. Extensive evaluations on the NYUv2 and KITTI benchmark datasets show that our model is highly accurate across multiple backbones, and it achieves state-of-the-art performance on both benchmarks when combined with the Swin Transformer and HRNets.
The farthest point strategy for progressive image sampling
Authors: Eldar, Yuval, Michael Lindenbaum, Moshe Porat, and Yehoshua Y. Zeevi
Date: 1994-09-13 00:00:00
Proceeding: Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5) 1994 9

A new method of "farthest point strategy" (FPS) for progressive image acquisition is presented. Its main advantage is in retaining its uniformity with the increased density, providing an efficient means for sparse image sampling and display. In contrast to previously presented stochastic approaches, the FPS guarantees the uniformity in a deterministic min-max sense. Within this uniformity criterion, the sampling points are irregularly spaced, exhibiting superior antialiasing properties. A straightforward modification of the FPS yields an image-dependent adaptive sampling scheme. An efficient, O(N log(N)), algorithm for both versions is introduced, and several applications of the FPS are discussed.
MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles
Authors: Hiba Kobeissi, Javiera Jilberto, M. Çağatay Karakan, Xining Gao, Samuel J. DePalma, Shoshana L. Das, Lani Quach, Jonathan Urquia, Brendon M. Baker, Christopher S. Chen, David Nordsletten, Emma Lejeune
Date: 2023-08-08 00:00:00
Tasks: Drug Discovery

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute," a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, requires little to no parameter tuning, and runs quickly on a personal computer. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community.
VDDT: Improving Vessel Detection with Deformable Transfomer
Authors: Siyu Chen, Yiling Liu, Jinhe Su, Ruixin Zheng, Zhihui Chen, Lefan Wang
Date: 2023-03-15 00:00:00
Proceeding: EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering 2023 3
Tasks: Decoder, Object, object-detection, Object Detection, Vessel Detection

Vessel detection has received wide attention in object detection, and the recently proposed DETR has successfully achieved true end-to-end object detection and has shown good performance. However, DETR is not sensitive to detect small objects, resulting in its unsatisfactory performance in vessel detection. In this paper, we use Deformable DETR as the baseline model and modify it on top of that. Firstly, we add reference point information to object queries to make the features learned by object queries richer to improve the performance of the detector. Secondly, we use multi-layer perceptron instead of multi-head self-attention to reduce the computational effort of the decoder. In addition, we collected 85 videos annotated with 4563 images and used these images to make a vessel dataset. The experimental data on our vessel dataset shows that VDDT performs better compared to the baseline.
Machine Unlearning in Gradient Boosting Decision Trees
Authors: Huawei Lin, Jun Woo Chung, Yingjie Lao, Weijie Zhao
Date: 2023-08-04 00:00:00
Proceeding: KDD 2023 8
Tasks: Machine Unlearning

Various machine learning applications take users' data to train the models. Recently enforced legislation requires companies to remove users' data upon requests, i.e.,the right to be forgotten. In the context of machine learning, the trained model potentially memorizes the training data. Machine learning algorithms have to be able to unlearn the user data that are requested to delete to meet the requirement. Gradient Boosting Decision Trees (GBDT) is a widely deployed model in many machine learning applications. However, few studies investigate the unlearning on GBDT. This paper proposes a novel unlearning framework for GBDT. To the best of our knowledge, this is the first work that considers machine unlearning on GBDT. It is not straightforward to transfer the unlearning methods of DNN to GBDT settings. We formalized the machine unlearning problem and its relaxed version. We propose an unlearning framework that efficiently and effectively unlearns a given collection of data without retraining the model from scratch. We introduce a collection of techniques, including random split point selection and random partitioning layers training, to the training process of the original tree models to ensure that the trained model requires few subtree retrainings during the unlearning. We investigate the intermediate data and statistics to store as an auxiliary data structure during the training so that we can immediately determine if a subtree is required to be retrained without touching the original training dataset. Furthermore, a lazy update technique is proposed as a trade-off between unlearning time and model functionality. We experimentally evaluate our proposed methods on public datasets. The empirical results confirm the effectiveness of our framework.