pwc-archive/papers-with-abstracts huggingface dataset:
Reward-free Policy Imitation Learning for Conversational Search
Authors: Zhenduo Wang, Zhichao Xu, Qingyao Ai
Date: 2023-04-17 00:00:00
Tasks: Conversational Search, Imitation Learning, Retrieval

Existing conversational search studies mainly focused on asking better clarifying questions and/or improving search result quality. These works aim at retrieving better responses according to the search context, and their performances are evaluated on either single-turn tasks or multi-turn tasks under naive conversation policy settings. This leaves some questions about their applicability in real-world multi-turn conversations where realistically, each and every action needs to be made by the system itself, and search session efficiency is often an important concern of conversational search systems. While some recent works have identified the need for improving search efficiency in conversational search, they mostly require extensive data annotations and use hand-crafted rewards or heuristics to train systems that can achieve reasonable performance in a restricted number of turns, which has limited generalizability in practice. In this paper, we propose a reward-free conversation policy imitation learning framework, which can train a conversation policy without annotated conversation data or manually designed rewards. The trained conversation policy can be used to guide the conversational retrieval models to balance conversational search quality and efficiency. To evaluate the proposed conversational search system, we propose a new multi-turn-multi-response conversational evaluation metric named Expected Conversational Reciprocal Rank (ECRR). ECRR is designed to evaluate entire multi-turn conversational search sessions towards comprehensively evaluating both search result quality and search efficiency.
An In-depth Investigation of User Response Simulation for Conversational Search
Authors: Zhenduo Wang, Zhichao Xu, Qingyao Ai, Vivek Srikumar
Date: 2023-04-17 00:00:00
Tasks: Conversational Search, Text Generation, User Simulation

Conversational search has seen increased recent attention in both the IR and NLP communities. It seeks to clarify and solve users' search needs through multi-turn natural language interactions. However, most existing systems are trained and demonstrated with recorded or artificial conversation logs. Eventually, conversational search systems should be trained, evaluated, and deployed in an open-ended setting with unseen conversation trajectories. A key challenge is that training and evaluating such systems both require a human-in-the-loop, which is expensive and does not scale. One strategy is to simulate users, thereby reducing the scaling costs. However, current user simulators are either limited to only responding to yes-no questions from the conversational search system or unable to produce high-quality responses in general. In this paper, we show that existing user simulation systems could be significantly improved by a smaller finetuned natural language generation model. However, rather than merely reporting it as the new state-of-the-art, we consider it a strong baseline and present an in-depth investigation of simulating user response for conversational search. Our goal is to supplement existing work with an insightful hand-analysis of unsolved challenges by the baseline and propose our solutions. The challenges we identified include (1) a blind spot that is difficult to learn, and (2) a specific type of misevaluation in the standard setup. We propose a new generation system to effectively cover the training blind spot and suggest a new evaluation setup to avoid misevaluation. Our proposed system leads to significant improvements over existing systems and large language models such as GPT-4. Additionally, our analysis provides insights into the nature of user simulation to facilitate future work.
PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning
Authors: Rui Liu, Bin Yin, Ziyi Cao, Qianchen Xia, Yong Chen, Dell Zhang
Date: 2023-04-17 00:00:00
Tasks: Contrastive Learning, News Recommendation, Recommendation Systems

Personalized news recommender systems help users quickly find content of their interests from the sea of information. Today, the mainstream technology for personalized news recommendation is based on deep neural networks that can accurately model the semantic match between news items and users' interests. In this paper, we present \textbf{PerCoNet}, a novel deep learning approach to personalized news recommendation which features two new findings: (i) representing users through \emph{explicit persona analysis} based on the prominent entities in their recent news reading history could be more effective than latent persona analysis employed by most existing work, with a side benefit of enhanced explainability; (ii) utilizing the title and abstract of each news item via cross-view \emph{contrastive learning} would work better than just combining them directly. Extensive experiments on two real-world news datasets clearly show the superior performance of our proposed approach in comparison with current state-of-the-art techniques.
M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
Authors: Zepeng Huai, Yuji Yang, Mengdi Zhang, Zhongyi Zhang, YiChun Li, Wei Wu
Date: 2023-04-16 00:00:00
Tasks: Graph Neural Network, Recommendation Systems, TAG

Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products can be described by a piece of text, especially when cold-start users or items have few interactions. However, two vital issues are still under-explored: (1) From the content modeling perspective, sufficient long-text descriptions are usually scarce in a real recommender system, more often the light-weight textual features, such as a few keywords or tags, are more accessible, which is improperly modeled by existing methods. (2) From the CDR perspective, not all inter-domain interests are helpful to infer intra-domain interests. Caused by domain-specific features, there are part of signals benefiting for recommendation in the source domain but harmful for that in the target domain. Therefore, how to distill useful interests is crucial. To tackle the above two problems, we propose a metapath and multi-interest aggregated graph neural network (M2GNN). Specifically, to model the tag-based contents, we construct a heterogeneous information network to hold the semantic relatedness between users, items, and tags in all domains. The metapath schema is predefined according to domain-specific knowledge, with one metapath for one domain. User representations are learned by GNN with a hierarchical aggregation framework, where the intra-metapath aggregation firstly filters out trivial tags and the inter-metapath aggregation further filters out useless interests. Offline experiments and online A/B tests demonstrate that M2GNN achieves significant improvements over the state-of-the-art methods and current industrial recommender system in Dianping, respectively. Further analysis shows that M2GNN offers an interpretable recommendation.
PBNR: Prompt-based News Recommender System
Authors: Xinyi Li, Yongfeng Zhang, Edward C. Malthouse
Date: 2023-04-16 00:00:00
Tasks: Articles, Language Modeling, Language Modelling, Large Language Model, News Recommendation, Prompt Learning, Recommendation Systems, Transfer Learning

Online news platforms often use personalized news recommendation methods to help users discover articles that align with their interests. These methods typically predict a matching score between a user and a candidate article to reflect the user's preference for the article. Some previous works have used language model techniques, such as the attention mechanism, to capture users' interests based on their past behaviors, and to understand the content of articles. However, these existing model architectures require adjustments if additional information is taken into account. Pre-trained large language models, which can better capture word relationships and comprehend contexts, have seen a significant development in recent years, and these pre-trained models have the advantages of transfer learning and reducing the training time for downstream tasks. Meanwhile, prompt learning is a newly developed technique that leverages pre-trained language models by building task-specific guidance for output generations. To leverage textual information in news articles, this paper introduces the pre-trained large language model and prompt-learning to the community of news recommendation. The proposed model "prompt-based news recommendation" (PBNR) treats the personalized news recommendation as a text-to-text language task and designs personalized prompts to adapt to the pre-trained language model -- text-to-text transfer transformer (T5). Experimental studies using the Microsoft News dataset show that PBNR is capable of making accurate recommendations by taking into account various lengths of past behaviors of different users. PBNR can also easily adapt to new information without changing the model architecture and the training objective. Additionally, PBNR can make recommendations based on users' specific requirements, allowing human-computer interaction in the news recommendation field.
Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction
Authors: Peilin Chen, Hong Wen, Jing Zhang, Fuyu Lv, Zhao Li, Qijie Shen, Wanjie Tao, Ying Zhou, Chao Zhang
Date: 2023-04-16 00:00:00
Tasks: Click-Through Rate Prediction

Online travel platforms (OTPs), e.g., Ctrip.com or Fliggy.com, can effectively provide travel-related products or services to users. In this paper, we focus on the multi-scenario click-through rate (CTR) prediction, i.e., training a unified model to serve all scenarios. Existing multi-scenario based CTR methods struggle in the context of OTP setting due to the ignorance of the cold-start users who have very limited data. To fill this gap, we propose a novel method named Cold-Start based Multi-scenario Network (CSMN). Specifically, it consists of two basic components including: 1) User Interest Projection Network (UIPN), which firstly purifies users' behaviors by eliminating the scenario-irrelevant information in behaviors with respect to the visiting scenario, followed by obtaining users' scenario-specific interests by summarizing the purified behaviors with respect to the target item via an attention mechanism; and 2) User Representation Memory Network (URMN), which benefits cold-start users from users with rich behaviors through a memory read and write mechanism. CSMN seamlessly integrates both components in an end-to-end learning framework. Extensive experiments on real-world offline dataset and online A/B test demonstrate the superiority of CSMN over state-of-the-art methods.
Meta-optimized Contrastive Learning for Sequential Recommendation
Authors: Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Victor Sheng
Date: 2023-04-16 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Meta-Learning, Sequential Recommendation

Contrastive Learning (CL) performances as a rising approach to address the challenge of sparse and noisy recommendation data. Although having achieved promising results, most existing CL methods only perform either hand-crafted data or model augmentation for generating contrastive pairs to find a proper augmentation operation for different datasets, which makes the model hard to generalize. Additionally, since insufficient input data may lead the encoder to learn collapsed embeddings, these CL methods expect a relatively large number of training data (e.g., large batch size or memory bank) to contrast. However, not all contrastive pairs are always informative and discriminative enough for the training processing. Therefore, a more general CL-based recommendation model called Meta-optimized Contrastive Learning for sequential Recommendation (MCLRec) is proposed in this work. By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation. Moreover, MCLRec utilizes a meta-learning manner to guide the updating of the model augmenters, which helps to improve the quality of contrastive pairs without enlarging the amount of input data. Finally, a contrastive regularization term is considered to encourage the augmentation model to generate more informative augmented views and avoid too similar contrastive pairs within the meta updating. The experimental results on commonly used datasets validate the effectiveness of MCLRec.
Transition Propagation Graph Neural Networks for Temporal Networks
Authors: Tongya Zheng, Zunlei Feng, Tianli Zhang, Yunzhi Hao, Mingli Song, Xingen Wang, Xinyu Wang, Ji Zhao, Chun Chen
Date: 2023-04-15 00:00:00
Tasks: Graph Mining, Link Prediction, Memorization

Researchers of temporal networks (e.g., social networks and transaction networks) have been interested in mining dynamic patterns of nodes from their diverse interactions. Inspired by recently powerful graph mining methods like skip-gram models and Graph Neural Networks (GNNs), existing approaches focus on generating temporal node embeddings sequentially with nodes' sequential interactions. However, the sequential modeling of previous approaches cannot handle the transition structure between nodes' neighbors with limited memorization capacity. Detailedly, an effective method for the transition structures is required to both model nodes' personalized patterns adaptively and capture node dynamics accordingly. In this paper, we propose a method, namely Transition Propagation Graph Neural Networks (TIP-GNN), to tackle the challenges of encoding nodes' transition structures. The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph. Based on the bilevel graph, TIP-GNN further encodes transition structures by multi-step transition propagation and distills information from neighborhoods by a bilevel graph convolution. Experimental results over various temporal networks reveal the efficiency of our TIP-GNN, with at most 7.2\% improvements of accuracy on temporal link prediction. Extensive ablation studies further verify the effectiveness and limitations of the transition propagation module. Our code is available at \url{https://github.com/doujiang-zheng/TIP-GNN}.
On User-side Fairness in Negative Sampling for Recommender Systems
Authors: Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan
Date: 2023-04-15 00:00:00
Tasks: Fairness, Recommendation Systems

Recommender systems are usually trained to discern between positive and negative instances for each user. Negative sampling plays an important role in selecting informative negative items. Since positive data is disproportionately contributed by a minority of active users, negative samplers might be affected by data imbalance thus choosing more informative negative items for active users. Consequently, users with low participation are further underrepresented in the training data, potentially causing subpar treatment from recommenders. In this paper we demonstrate empirically that active users receive more accurate recommendation than inactive users for state-of-the-art negative sampling strategies, and the degree of data imbalance influences the severity of performance disparities. We further show that the performance gain brought by sampling more negative instances for each positive item is unequally distributed across user groups. Generally, active users benefit from performance gain whereas inactive users might suffer from performance degradation. To address these shortcomings, we propose a group-wise negative ratio setup where we use the appropriate smaller negative ratio for inactive users and a bigger ratio for active users. Comprehensive experiments show our proposed group-wise ratio outperforms a single global ratio in user-side fairness and performance improvement.
Automatic Road Crack Detection Using Random Structured Forests
Authors: Yong Shi, Limeng Cui, Zhiquan Qi, Fan Meng, and Zhensong Chen
Date: 2016-05-18 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2016 5
Tasks: Crack Segmentation

Cracks are a growing threat to road conditions and
have drawn much attention to the construction of intelligent
transportation systems. However, as the key part of an intelli-
gent transportation system, automatic road crack detection has
been challenged because of the intense inhomogeneity along the
cracks, the topology complexity of cracks, the inference of noises
with similar texture to the cracks, and so on. In this paper , we
propose CrackForest, a novel road crack detection framework
based on random structured forests, to address these issues. Our
contributions are shown as follows: 1) apply the integral channel
features to redefine the tokens that constitute a crack and get
better representation of the cracks with intensity inhomogeneity;
2) introduce random structured forests to generate a high-
performance crack detector, which can identify arbitrarily com-
plex cracks; and 3) propose a new crack descriptor to characterize
cracks and discern them from noises effectively. In addition, our
method is faster and easier to parallel. Experimental results prove
the state-of-the-art detection precision of CrackForest compared
with competing methods.
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views
Authors: Jie Wen, Chengliang Liu, Shijie Deng, Yicheng Liu, Lunke Fei, Ke Yan, Yong Xu
Date: 2023-03-23 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2023 3
Tasks: Classification, Decoder, Missing Labels, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning, MULTI-VIEW LEARNING

View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery. In the past years, many efforts have been made to address the incomplete multi-view learning or incomplete multi-label learning problem. However, few works can simultaneously handle the challenging case with both two incomplete issues. In this paper, we propose a new incomplete multi-view multi-label learning network to address this challenging issue. The proposed method is composed of four major parts: view-specific deep feature extraction network, weighted representation fusion module, classification module, and view-specific deep decoder network. By respectively integrating the view missing information and label missing information into the weighted fusion module and classification module, the proposed method can effectively reduce the negative influence caused by such two incomplete issues and sufficiently explore the available data and label information to obtain the most discriminative feature extractor and classifier. Furthermore, our method can be trained in both supervised and semi-supervised manners, which has important implications for flexible deployment. Experimental results on five benchmarks in supervised and semi-supervised cases demonstrate that the proposed method can greatly enhance the classification performance on the difficult incomplete multi-view multi-label classification tasks with missing labels and missing views.
Improving the previous state-of-the-art Frisian ASR by fine-tuning XLS-R
Authors: Dragoș Alexandru Bălan, Golshid Shekoufandeh
Date: 2023-03-31 00:00:00
Proceeding: - 2023 3
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech-to-Text

Automatic Speech Recognition (ASR), a system that converts human speech to text, has a major role in digitizing human communication. Despite their significance, most of these systems are designed for higher-resourced languages, like English, Mandarin, or Spanish, leaving lower-resourced languages, such as Frisian, underrepresented. To address this issue, our paper introduces a fine-tuned ASR model based on the Wav2Vec 2.0 XLS-R architecture, trained on the Common Voice corpus version 12.0, to transcribe Frisian speech. With a learning rate of 8e-5, our proposed ASR system has achieved a 15.99% word error rate (WER), surpassing the previous state-of-the-art of 16.25% and serving as a benchmark for future research in this field.
ASIC Implementation of Denoising Filters for Pacemakers
Authors: Charan S, Dr. Reena Monica P
Date: 2023-04-16 00:00:00
Tasks: Denoising, Rhythm

Cardiac Pacemakers are used to regulate the hearts rhythm and prevent abnormal heart beats. Patients undergo a minimal surgery to get the pacemaker implanted in the body, whereas these devices do have their limitations such as battery life, power consumption and integrated circuit area which makes it difficult for elderly and children to undergo this surgery. This paper focuses on developing an optimised low pass filter ASIC design for implantable QRS complex detector for cardiac pacemaker circuits using filter optimisation techniques such as Pipelining and Folding of filters. The folded low pass filter design consumes an overall power of 0.7575 mW and comprises a total of 1361 standard cells. The number of adder block units in our work reduces area consumption by a factor of 48.37%.
Personalized Federated Learning with Local Attention
Authors: Sicong Liang, Junchao Tian, Shujun Yang, Yu Zhang
Date: 2023-04-02 00:00:00
Tasks: Federated Learning, image-classification, Image Classification, object-detection, Object Detection, Personalized Federated Learning

Federated Learning (FL) aims to learn a single global model that enables the central server to help the model training in local clients without accessing their local data. The key challenge of FL is the heterogeneity of local data in different clients, such as heterogeneous label distribution and feature shift, which could lead to significant performance degradation of the learned models. Although many studies have been proposed to address the heterogeneous label distribution problem, few studies attempt to explore the feature shift issue. To address this issue, we propose a simple yet effective algorithm, namely \textbf{p}ersonalized \textbf{Fed}erated learning with \textbf{L}ocal \textbf{A}ttention (pFedLA), by incorporating the attention mechanism into personalized models of clients while keeping the attention blocks client-specific. Specifically, two modules are proposed in pFedLA, i.e., the personalized single attention module and the personalized hybrid attention module. In addition, the proposed pFedLA method is quite flexible and general as it can be incorporated into any FL method to improve their performance without introducing additional communication costs. Extensive experiments demonstrate that the proposed pFedLA method can boost the performance of state-of-the-art FL methods on different tasks such as image classification and object detection tasks.