Companion Proceedings of the Web Conference 2021最新文献_第9页

Characterizing Opinion Dynamics and Group Decision Making in Wikipedia Content Discussions 维基百科内容讨论中意见动态和群体决策的特征

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452354

Khandaker Tasnim Huq

{"title":"Characterizing Opinion Dynamics and Group Decision Making in Wikipedia Content Discussions","authors":"Khandaker Tasnim Huq","doi":"10.1145/3442442.3452354","DOIUrl":"https://doi.org/10.1145/3442442.3452354","url":null,"abstract":"Wikipedia, the online encyclopedia, is a trusted source of knowledge for millions of individuals worldwide. As everyone can start a new article, it is often necessary to decide whether certain entries meet the standards for inclusion set forth by the community. These decisions (which are known as “Article for Deletion”, or AfD) are taken by groups of editors in a deliberative fashion, and are known for displaying a number of common biases associated to group decision making. Here, we present an analysis of 1,967,768 AfD discussions between 2005 and 2018. We perform a signed network analysis to capture the dynamics of agreement and disagreement among editors. We measure the preference of each editor for voting toward either inclusion or deletion. We further describe the evolution of individual editors and their voting preferences over time, finding four major opinion groups. Finally, we develop a predictive model of discussion outcomes based on latent factors. Our results shed light on an important, yet overlooked, aspect of curation dynamics in peer production communities, and could inform the design of improved processes of collective deliberation on the web.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134517374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parallelizing DNN Training on GPUs: Challenges and Opportunities gpu上并行DNN训练:挑战与机遇

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452055

Weizheng Xu, Youtao Zhang, Xulong Tang

{"title":"Parallelizing DNN Training on GPUs: Challenges and Opportunities","authors":"Weizheng Xu, Youtao Zhang, Xulong Tang","doi":"10.1145/3442442.3452055","DOIUrl":"https://doi.org/10.1145/3442442.3452055","url":null,"abstract":"In recent years, Deep Neural Networks (DNNs) have emerged as a widely adopted approach in many application domains. Training DNN models is also becoming a significant fraction of the datacenter workload. Recent evidence has demonstrated that modern DNNs are becoming more complex and the size of DNN parameters (i.e., weights) is also increasing. In addition, a large amount of input data is required to train the DNN models to reach target accuracy. As a result, the training performance becomes one of the major challenges that limit DNN adoption in real-world applications. Recent works have explored different parallelism strategies (i.e., data parallelism and model parallelism) and used multi-GPUs in datacenters to accelerate the training process. However, naively adopting data parallelism and model parallelism across multiple GPUs can lead to sub-optimal executions. The major reasons are i) the large amount of data movement that prevents the system from feeding the GPUs with the required data in a timely manner (for data parallelism); and ii) low GPU utilization caused by data dependency between layers that placed on different devices (for model parallelism). In this paper, we identify the main challenges in adopting data parallelism and model parallelism on multi-GPU platforms. Then, we conduct a survey including recent research works targeting these challenges. We also provide an overview of our work-in-progress project on optimizing DNN training on GPUs. Our results demonstrate that simple-yet-effective system optimizations can further improve the training scalability compared to prior works.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133663077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

GOAT at the FinSim-2 task: Learning Word Representations of Financial Data with Customized Corpus 在FinSim-2任务中的山羊:使用自定义语料库学习金融数据的单词表示

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451385

Yulong Pei, Qian Zhang

{"title":"GOAT at the FinSim-2 task: Learning Word Representations of Financial Data with Customized Corpus","authors":"Yulong Pei, Qian Zhang","doi":"10.1145/3442442.3451385","DOIUrl":"https://doi.org/10.1145/3442442.3451385","url":null,"abstract":"In this paper, we present our approaches for the FinSim 2021 Shared Task on Learning Semantic Similarities for the Financial Domain. The aim of the FinSim shared task is to automatically classify a given list of terms from the financial domain into the most relevant hypernym (or top-level) concept in an external ontology. Two different word representations have been compared in our study, i.e., customized word2vec provided by the shared task and FinBERT. We first create a customized corpus from the given prospectuses and relevant articles from Investopedia. Then we train the domain-specific word2vec embeddings using the customized data with customized word2vec and FinBERT as the initialized embeddings respectively. Our experimental results demonstrate that these customized word embeddings can effectively improve the classification performance and achieve better results than the direct utilization of the provided word embeddings. The class imbalance issue of the given data is also explored. We empirically study the classification performance by employing several different strategies for imbalanced classification problems. Our system ranks 2nd on both Average Accuracy and Mean Rank metrics.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131155742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Do I Trust this Stranger? Generalized Trust and the Governance of Online Communities 我该相信这个陌生人吗?广义信任与网络社区治理

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452338

Jérôme Hergueux, Y. Algan, Y. Benkler, M. F. Morell

引用次数: 1

EMR: Scalable Clustering of Big HR Data using Evolutionary MapReduce EMR:使用进化MapReduce的大人力资源数据的可扩展聚类

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3453543

M. Bohlouli, Zhonghua He

{"title":"EMR: Scalable Clustering of Big HR Data using Evolutionary MapReduce","authors":"M. Bohlouli, Zhonghua He","doi":"10.1145/3442442.3453543","DOIUrl":"https://doi.org/10.1145/3442442.3453543","url":null,"abstract":"Nowadays, the volume and variety of generated data, how to process it and accordingly create value through scalable analytics are main challenges to industries and real-world practices such as talent analytics. For instance, large enterprises and job centres have to progress data intensive matching of job seekers to various job positions at the same time. In other words, it should result in the large scale assignment of best-fit (right) talents (Person) with right expertise (Profession) to the right job (Position) at the right time (Period). We call this definition as a 4P rule in this paper. All enterprises should consider 4P rule in their daily recruitment processes towards efficient workforce development strategies. Such consideration demands integrating large volumes of disparate data from various sources and strongly needs the use of scalable algorithms and analytics. The diversity of the data in human resource management requires speeding up analytical processes. The main challenge here is not only how and where to store the data, but also the analysing it towards creating value (knowledge discovery). In this paper, we propose a generic Career Knowledge Representation (CKR) model in order to be able to model most competences that exist in a wide variety of careers. A regenerated job qualification data of 15 million employees with 84 dimensions (competences) from real HRM data has been used in test and evaluation of proposed Evolutionary MapReduce K-Means method in this research. This proposed EMR method shows faster and more accurate experimental results in comparison to similar approaches and has been tested with real large scale datasets and achieved results are already discussed.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114956850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the quality of health-related Wikipedia articles with generic and specific metrics 使用通用和特定指标评估与健康相关的维基百科文章的质量

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452355

Luis Couto, C. Lopes

引用次数: 3

DL Inference and Training Optimization Towards Speed and Scale 面向速度和规模的深度学习推理和训练优化

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452297

Minjia Zhang

引用次数: 0

FAIR Linked Data - Towards a Linked Data Backbone for Users and Machines 公平关联数据——面向用户和机器的关联数据骨干

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451364

Johannes Frey, Sebastian Hellmann

引用次数: 11

taka at the FinSBD-3 task: Tables and Figures Extraction using Object Detection Techniques FinSBD-3任务:使用目标检测技术提取表格和图形

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3451379

Tien-Dung Le

引用次数: 1

Towards Open-domain Vision and Language Understanding with Wikimedia 通过维基媒体实现开放领域视觉和语言理解

Companion Proceedings of the Web Conference 2021 Pub Date : 2021-04-19 DOI: 10.1145/3442442.3452346

David Semedo

引用次数: 0