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Toward an accurate mobility trajectory recovery using contrastive learning

Frontiers of information Technology & Electronic Engineering 2024年第11期25卷 1479-1496页

作者： Yushan LIU Yang CHEN Jiayun ZHANG Yu XIAO Xin WANG Shanghai Key Lab of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Department of Information and Communications Engineering Aalto UniversityEspoo FI-02150Finland

Human mobility trajectories are fundamental resources for analyzing mobile behaviors in urban computing ***,these trajectories,typically collected from location-based services,often suffer from sparsity and irregularity in *** support the development of mobile applications,there is a need to recover or estimate missing locations of unobserved time slots in these trajectories at a fine-grained spatial–temporal *** methods for trajectory recovery rely on either individual user trajectories or collective mobility patterns from all *** potential to combine individual and collective patterns for precise trajectory recovery remains ***,current methods are sensitive to the heterogeneous temporal distributions of the observable trajectory *** this paper,we propose CLMove(where CL stands for contrastive learning),a novel model designed to capture multilevel mobility patterns and enhance robustness in trajectory *** features a two-stage location encoder that captures collective and individual mobility *** graph neural network based networks in CLMove explore location transition patterns within a single trajectory and across various user *** also design a trajectory-level contrastive learning task to improve the robustness of the *** experimental results on three representative real-world datasets demonstrate that our CLMove model consistently outperforms state-of-the-art methods in terms of trajectory recovery accuracy.

关键词： Human mobility Mobility trajectory recovery Contrastive learning

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Weakly-Supervised Graph Classification with Even a Single Key Subgraph Per Class 24

Weakly-Supervised Graph Classification with Even a Single Ke...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Lu Zhang, Chenbo Guan, Jihong Zhou, Shuigeng School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Tongji University Department of Computer Science & Technology China

ISBN: (纸本)9798331506681

Traditional graph classification requires large amounts of labeled data, which is expensive and time-consuming to acquire, especially in some special scenarios that domain knowledge is indispensable for labeling graphs. Observing that some key subgraphs can determine the properties of graphs (e.g. the toxicity of drug molecules depend on some toxic functional groups), in this paper we explore to classify graphs using unlabeled graphs plus a small number of key subgraphs for each class, which is called weakly-supervised graph classification. To this end, we develop the WeGraph method, where the graph classifier is trained with subgraph-based self-supervised learning and divergence- minimization based fine-tuning. Moreover, we design a key subgraph extraction algorithm to iteratively extract and update the key subgraphs, which makes the training process a closed loop. We conduct extensive experiments on different types of graph datasets to evaluate the effectiveness of WeGraph. Experimental results show that WeGraph can achieve high performance even when only one key subgraph is provided for each class. © 2024 IEEE.

关键词： Self-supervised learning

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Supervised Relation Extraction Based on labels Contrastive Learning 16

Supervised Relation Extraction Based on Labels Contrastive L...

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16th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Wang, Yuanru Zhao, Yanhui Cui, Rong-Yi Jin, Guo-Zhe Yanbian University Intelligent Information Processing Lab. Department of Computer Science & Technology Yanji China

ISBN: (纸本)9798350330755

Relation extraction is an important task in natural language processing. Existing relation extraction tasks usually use data augmentation to construct positive and negative samples for contrastive learning training. And previous works lack the accurate selection of positive and negative samples and the full use of relation labels. To overcome these defects, for supervised relational extraction, we propose a labels Contrastive Learning framework (labelsCL) that considers both global and local perspectives, which makes full use of labels for design. Specifically, from a global perspective, we use label and cosine similarity to select the most easily misclassified samples as positive and negative examples for training. From a local perspective, positive and negative samples are selected according to batch and label, and this way of sample selection increases the randomness of sample selection. The contrastive learning framework labelsCL proposed by us makes full use of labels and synthesizes two perspectives to construct contrastive learning labelsCL, which increases the tolerance of the framework. Finally, the experimental results demonstrate the effectiveness of our proposed framework, which is significantly improved on the Semeval-2010 Task 8 dataset compared with the baselines. © 2023 IEEE.

关键词： Extraction

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Impact of COVID-19 on Residential and Non-residential Power Consumption: A Data-Driven Study 8

Impact of COVID-19 on Residential and Non-residential Power ...

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8th Asia Conference on Power and Electrical Engineering, ACPEE 2023

作者： Zhang, Qifan Cao, Xiu Chen, Yang Cao, Yixin Gao, Min Fudan University Shanghai Key Lab of Intelligent Information Processing Department of Computer Science Shanghai China

ISBN: (纸本)9798350345520

In the past few years, the COVID-19 pandemic has affected all aspects of people's daily life. In particular, people in large cities have also been greatly affected. How to accurately and timely evaluate and analyze the impact of COVID-19 on large cities in China is increasingly significant. Based on the four-year power consumption data of a large city in China, this paper examines COVID-19's impact from the perspective of residential and non-residential power *** address the discrepancies in climate and date information, we created COVID-19-Power, a framework that predicts the predicted power consumption of the city in 2020 without the outbreak of COVID-19, and then analyzes the difference in power consumption between the real situation and predicted results. For residential power consumption, we analyze the respective users' behavior citywide, in different districts, and with different historical power consumption habits clustered by the K-means algorithm. On the other hand, for non-residential consumption, we analyze the users citywide, in different regions and industries. The results showed that while the impact of the pandemic on resident power consumption has been small, it is significant for non-residential power consumption, which is independent of geographical location. The regional differences were mainly reflected in the main types of industries. Through these research results, we can understand in detail the impact of the pandemic on residents' lives and industries in China's large cities;Power enterprises can more precisely understand the impact of the pandemic on electrical load, so that power enterprises can adjust power supply strategies more effectively and pertinently. © 2023 IEEE.

关键词： COVID-19

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Slot and Intent Detection Resources for Bavarian and Lithuanian: Assessing Translations vs Natural Queries to Digital Assistants 30

Slot and Intent Detection Resources for Bavarian and Lithuan...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Winkler, Miriam Juozapaityte, Virginija van der Goot, Rob Plank, Barbara MaiNLP Lab Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9782493814104

Digital assistants perform well in high-resource languages like English, where tasks like slot and intent detection (SID) are well-supported. Many recent SID datasets start including multiple language varieties. However, it is unclear how realistic these translated datasets are. Therefore, we extend one such dataset, namely xSID-0.4 (van der Goot et al., 2021a;Aepli et al., 2023), to include two underrepresented languages: Bavarian, a German dialect, and Lithuanian, a Baltic language. Both language variants have limited speaker populations and are often not included in multilingual projects. In addition to translations we provide "natural" queries to digital assistants generated by native speakers. We further include utterances from another dataset for Bavarian to build the richest SID dataset available today for a low-resource dialect without standard orthography. We then set out to evaluate models trained on English in a zero-shot scenario on our target language variants. Our evaluation reveals that translated data can produce overly optimistic scores. However, the error patterns in translated and natural datasets are highly similar. Cross-dataset experiments demonstrate that data collection methods influence performance, with scores lower than those achieved with single-dataset translations. This work contributes to enhancing SID datasets for underrepresented languages, yielding NaLiBaSID, a new evaluation dataset for Bavarian and Lithuanian. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Translation (languages)

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IndirectQA: Understanding Indirect Answers to Implicit Polar Questions in French and Spanish 30

IndirectQA: Understanding Indirect Answers to Implicit Polar...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Müller, Christin Plank, Barbara MaiNLP Lab Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9782493814104

Polar questions are common in dialogue and expect exactly one of two answers (yes/no). It is however not uncommon for speakers to bypass these expected choices and answer, for example, "Islands are generally by the sea" to the question: "An island? By the sea?". While such answers are natural in spoken dialogues, conversational systems still struggle to interpret them. Seminal work to interpret indirect answers were made in recent years-but only for English and with strict question formulations. In this work, we present a new corpus for French and Spanish-IndirectQA -where we mine subtitle data for indirect answers to study the labeling task with six different labels, while broadening polar questions to include also implicit polar questions (statements that trigger a yes/no-answer which are not necessarily formulated as a question). We opted for subtitles since they are a readily available source of conversation in various languages, but also come with peculiarities and challenges which we will discuss. Overall, we provide the first results on French and Spanish. They show that the task is challenging: the baseline accuracy scores drop from 61.43 on English to 44.06 for French and Spanish. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Annotation Corpora Indirect Answers Natural Language Understanding

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CIRCLE: ScRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning

CIRCLE: ScRNA-seq Data Clustering by Cluster-aware Iterative...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Jinxian Jiang, Weikang Guan, Jihong Zhou, Shuigeng Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Shanghai200433 China Tongji University Department of Computer Science and Technolog Shanghai201804 China

ISBN: (纸本)9798350386226

Single-cell RNA sequencing (scRNA-seq) enables researchers to analyze gene expression at single-cell level. One important task in scRNA-seq data analysis is unsupervised clustering, which helps identify distinct cell types, laying down the foundation for other downstream analysis tasks. In this paper, we propose a novel method called Cluster-aware IteRative Contrastive LEarning (CIRCLE in short) for scRNA-seq data clustering, which uses an iterative representation learning and clustering framework to progressively learn the clustering structure of scRNA-seq data with a cluster-aware contrastive loss. CIRCLE consists of a Transformer encoder, a clustering head, a projection head and a contrastive loss. First, CIRCLE extracts the feature vectors of the original and augmented data by the Transformer-encoder. Then, it computes the clustering centroids by K-means and employs the student's t-distribution to assign pseudo-labels to all cells in the clustering head. The projection-head uses a Multi-Layer Perceptron (MLP) to obtain projections of the augmented data. At last, both pseudo-labels and projections are used in the contrastive loss to guide the model training. Such a process goes iteratively so that the clustering result gets better and better. Extensive experiments on 25 real-world scRNA-seq datasets show that CIRCLE outperforms the state-of-the-art (SOTA) methods in most cases. Concretely, CIRCLE surpasses the existing methods by from 14% to 280%, and from 5% to 133% on average in terms of performance metrics ARI and NMI respectively. Source code is available at https://***/Alunethy/CIRCLE. © 2024 IEEE.

关键词： Contrastive Learning

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A Power Consumption Forecasting Method Based on Knowledge Embedding Under the Influence of the COVID-19 Pandemic 8th

A Power Consumption Forecasting Method Based on Knowledge E...

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8th China National Conference on Big Data and Social Computing, BDSC 2023

作者： Zhang, Qifan Cao, Xiu Shanghai Key Lab of Intelligent Information Processing Department of Computer Science Fudan University Shanghai200433 China

ISBN: (纸本)9789819939244

This paper addresses the new challenges of power consumption forecasting under the COVID-19 pandemic by proposing a knowledge embedding-based method (PEC-19). This method constructs a multi-source enterprise knowledge graph by analyzing and combining the specific impact of COVID-19 on power consumption, and designs the Connection Count Calculation of PEC-19 (3CP) algorithm and Similarity Degree Calculation of PEC-19 (SDCP) algorithm based on the graph to measure the similarity between different users affected by the COVID-19 pandemic and enable knowledge transfer and embedding among them. Moreover, the PEC-19 framework introduces a SEIR-based caṇse number prediction module and a comprehensive feature extraction module to account for the trend of the COVID-19 pandemic and increase the variety of time series features. Compared with existing baseline methods, the PEC-19 framework can significantly improve the power consumption forecasting performance in both univariate and multivariate time series forecasting tasks, and each module contributes differently to the proposed method. The framework also has some applicability and provides a new solution for various public health emergencies, which helps to reduce power operation cost, coordinate supply and demand, and conduct accurate scheduling. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Exploring the Potential of Large Language Models (LLMs) for Low-resource Languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali Language 30

Exploring the Potential of Large Language Models (LLMs) for ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Subedi, Bipesh Regmi, Sunil Bal, Bal Krishna Acharya, Praveen Information and Language Processing Research Lab Department of Computer Science & Engineering Kathmandu University Nepal School of Computing Dublin City University Ireland

ISBN: (纸本)9782493814104

Large Language Models (LLMs) have made significant advancements in Natural Language processing (NLP) by excelling in various NLP tasks. This study specifically focuses on evaluating the performance of LLMs for Named Entity Recognition (NER) and Part-of-Speech (POS) tagging for a low-resource language, Nepali. The aim is to study the effectiveness of these models for languages with limited resources by conducting experiments involving various parameters and fine-tuning and evaluating two datasets namely, ILPRL and EBIQUITY. In this work, we have experimented with eight LLMs for Nepali NER and POS tagging. While some prior works utilized larger datasets than ours, our contribution lies in presenting a comprehensive analysis of multiple LLMs in a unified setting. The findings indicate that NepBERTa, trained solely in the Nepali language, demonstrated the highest performance with F1-scores of 0.76 and 0.90 in ILPRL dataset. Similarly, it achieved 0.79 and 0.97 in EBIQUITY dataset for NER and POS respectively. This study not only highlights the potential of LLMs in performing classification tasks for low-resource languages but also compares their performance with that of alternative approaches deployed for the tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Effect of Pectoral Muscles on CNN based Mammographic Cancer Detection 18

Effect of Pectoral Muscles on CNN based Mammographic Cancer ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Dhimann, Santoresh Kumari Sawhney, Tinny Yadav, Rakesh Kumar Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India University of Jammu Digital Signal Processing Lab Department of Electronics Jammu India

ISBN: (纸本)9789380544519

Cancer is attributed to abnormal growth of tissue cells in any part of the living body. The number of cancer patients is increasing worldwide. The reason of the development of abnormal cells within the body is still unknown to the researchers. The most common is breast cancer and it is affective affecting one out of every ten women. The reason of breast lesions formation may be calcification or mass deposit and digital mammography is generally used for verifying it. It is a cheap, non-toxic, and user-friendly test, but needs expert opinion for ascertaining the presence of cancer. The accuracy of mammographic detection varies between 80% and 90%. Recently, Convolutional neural networks (CNNs) have shown their application in automatic detection of the cancer, but inclusion of pectoral muscle in the mammogram reduces the efficiency of the CNN system as the texture of the muscle resembles with cancerous tissues. Further, CNN based detection requires high resolution images which in turn requires state of art technology-based machines, generally, not available in rural area-based clinics. The focus of the proposed research is to investigate effect of low-quality blurred input mammograms on the detection of pectoral muscle boundary, so that the pectoral muscle removed mammograms can be applied to CNN system for automatic prediction of cancerous images. The investigations carried out have shown that low quality blurred images can also be employed for detecting cancerous tissues provided the pectoral muscle is removed from the mammograms before using CNN based classifier. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cancer detection CNN Hough lines mammogram pectoral muscle

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