This data collection contains diachronic Word Usage Graphs (WUGs) for Swedish. Uses were sampled for the target words from the DWUG SV dataset and from the same source corpora. DWUG SV Resampled can thus be seen as a small-scale replication of DWUG SV. Find a description of the data format, code to process the data and further datasets on the WUGsite.

Please find more information on the provided data in the papers referenced below.

Reference

Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray. 2021. DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

This data collection contains diachronic Word Usage Graphs (WUGs) for Spanish. Find a description of the data format, code to process the data and further datasets on the WUGsite.

Please find more information on the provided data in the papers referenced below.

The annotation was funded by

ANID FONDECYT grant 11200290, U-Inicia VID Project UI-004/20,

ANID - Millennium Science Initiative Program - Code ICN17 002 and

SemRel Group (DFG Grants SCHU 2580/1 and SCHU 2580/2).

Version: 4.0.1, 7.1.2025. Full data. Quoting issues in uses resolved. Target word and target sentence indices corrected. One corrected context for word 'metro'. Judgments anonymized. Annotator 'gecsa' removed. Issues with special characters in filenames resolved.

Reference

Frank D. Zamora-Reina, Felipe Bravo-Marquez, Dominik Schlechtweg. 2022. LSCDiscovery: A shared task on semantic change discovery and detection in Spanish. In Proceedings of the 3rd International Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics.

Dominik Schlechtweg, Tejaswi Choppa, Wei Zhao, Michael Roth. 2025. The CoMeDi Shared Task: Median Judgment Classification & Mean Disagreement Ranking with Ordinal Word-in-Context Judgments. In Proceedings of the 1st Workshop on Context and Meaning--Navigating Disagreements in NLP Annotations.

Market leader Facebook was the first social network to surpass one billion registered accounts and currently sits at more than three billion monthly active users. Meta Platforms owns four of the biggest social media platforms, all with more than one billion monthly active users each: Facebook (core platform), WhatsApp, Facebook Messenger, and Instagram. In the third quarter of 2023, Facebook reported around four billion monthly core Family product users. The United States and China account for the most high-profile social platforms Most top ranked social networks with more than 100 million users originated in the United States, but services like Chinese social networks WeChat, QQ or video sharing app Douyin have also garnered mainstream appeal in their respective regions due to local context and content. Douyin’s popularity has led to the platform releasing an international version of its network: a little app called TikTok. How many people use social media? The leading social networks are usually available in multiple languages and enable users to connect with friends or people across geographical, political, or economic borders. In 2025, social networking sites are estimated to reach 5.42 billion users and these figures are still expected to grow as mobile device usage and mobile social networks increasingly gain traction in previously underserved markets.

A critical issue in intelligent building control is detecting energy consumption anomalies based on intelligent device status data. The building field is plagued by energy consumption anomalies caused by a number of factors, many of which are associated with one another in apparent temporal relationships. For the detection of abnormalities, most traditional detection methods rely solely on a single variable of energy consumption data and its time series changes. Therefore, they are unable to examine the correlation between the multiple characteristic factors that affect energy consumption anomalies and their relationship in time. The outcomes of anomaly detection are one-sided. To address the above problems, this paper proposes an anomaly detection method based on multivariate time series. Firstly, in order to extract the correlation between different feature variables affecting energy consumption, this paper introduces a graph convolutional network to build an anomaly detection framework. Secondly, as different feature variables have different influences on each other, the framework is enhanced by a graph attention mechanism so that time series features with higher influence on energy consumption are given more attention weights, resulting in better anomaly detection of building energy consumption. Finally, the effectiveness of this paper’s method and existing methods for detecting energy consumption anomalies in smart buildings are compared using standard data sets. The experimental results show that the model has better detection accuracy.

The size of the Graph Database Market was valued at USD 19942.01 million in 2023 and is projected to reach USD 64282.28 million by 2032, with an expected CAGR of 18.20% during the forecast period. A Graph Database is a type of NoSQL database designed to represent and store data in the form of graphs, consisting of nodes, edges, and properties. This database model is optimized for handling data that is highly interconnected, allowing for the representation of relationships and networks with ease. The nodes in a graph database represent entities such as people, places, or events, while the edges represent the relationships or connections between these entities. Properties can be attached to both nodes and edges to store additional information, providing a rich structure for complex data sets. Unlike traditional relational databases, which use tables to organize data in rows and columns, graph databases use graph theory to model the relationships between data points, which enables more efficient querying and analysis, especially for large and complex data structures. This growth is attributed to factors such as increased data complexity, need for real-time insights, and advancements in AI and ML. Graph databases provide efficient storage and analysis of highly interconnected data, making them valuable for fraud detection, social network analysis, and recommendation systems. Key players include Oracle Corporation, IBM Corporation, and Amazon Web Services, Inc. Recent developments include: June 2021: Neo4j has released its most recent graph database version, 4.3. Graph data analysis, relationship asset indexes, new smart 10 scheduling, and parallelized backup are some of the features included in the most recent version of the graph database., April 2021: The MarkLogic Data Hub Central low-code/no-code user interface was introduced by MarkLogic Corp. With the ease and agility of using the data infrastructure, MarkLogic's launch provides organizations with a clear roadmap for cloud modernization., October 2020: Microsoft Corporation unveiled a brand-new artificial intelligence platform that can caption and describe photos. Azure Cognitive Services offers the system..

In 2023, nearly ** percent of the population of the United States used cannabis within the past year. The graph shows the percentage of the population in the U.S. who consumed cannabis in the past year from 2002 to 2023.

Introduction

Atlassian Confluence Usage Statistics: As of 2024, Atlassian Confluence has solidified its position as a leading platform for collaboration and knowledge management across various industries. Over 40,000 organizations worldwide utilize Confluence, encompassing millions of active users each month. The platform supports up to 150,000 users on a single Confluence Cloud site, catering to the needs of large enterprises.

In 2022, users created over 58 million pages and viewed more than 3 billion pages, reflecting the platform's extensive use. Additionally, the Atlassian Marketplace surpassed USD 4 billion in lifetime sales, offering over 5,700 apps and integrations to enhance Confluence's capabilities. These statistics underscore Confluence's significant role in facilitating digital transformation and streamlining collaboration within organizations.

This article addresses the key Atlassian Confluence usage statistics in 2024, such as user growth, market share, financial performance, and enterprise adoption.

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

The above statistic gives information on the average duration of daily internet usage worldwide as of the first quarter of 2019, sorted by age group and device. During the survey period, it was found that the average duration of daily mobile internet usage among internet users aged 25 to 34 years amounted to 3 hours and 45 minutes.

East Baton Rouge Parish Library computer usage statistics are organized by branch, year, and month. This dataset only includes the count for library patrons who have logged in to the Library’s public computers, located at any of the 14 locations.

In 2023, virtual private network (VPN) usage stood at approximately ** percent in the United States, the majority of respondents using a VPN doing so for personal devices only. By contrast, ** percent of American adults did not use a VPN or were unaware of them.

The United States consumed approximately 58.3 million metric tons of paper and paperboard in 2023. This was a decrease in comparison to the previous year. Nevertheless, when compared to 2000 levels, paper and paperboard consumption in the North American country has declined by roughly 38 percent. The United States is the second-largest consumer of paper and paperboard worldwide, behind China.

Romania - Last internet use: in last 3 months was 91.29% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Romania - Last internet use: in last 3 months - last updated from the EUROSTAT on June of 2025. Historically, Romania - Last internet use: in last 3 months reached a record high of 91.29% in December of 2024 and a record low of 36.00% in December of 2010.

The DUPS (Diachronic Usage Pair Similarity) dataset contains similarity judgements of English word usage pairs from different time periods, as described in the paper below.

The WUG version of the DUPS dataset (version 2.0.0) contains diachronic Word Usage Graphs constructed from the similarity judgements of English word usage pairs contained in DUPS. In a word usage graph, the usages of a word are represented as nodes connected by edges weighted according to (human-annotated) semantic proximity. A description of the data format as well as the code used to generate the graphs from DUPS can be found at https://www.ims.uni-stuttgart.de/data/wugs.

Both versions of the DUPS dataset can be downloaded from the Files section of this web page.

Please cite this paper if you use any version of the dataset in your work:

Mario Giulianelli, Marco Del Tredici, and Raquel Fernández. 2020. Analysing Lexical Semantic Change with Contextualised Word Representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL-2020). Association for Computational Linguistics.

Use of a virtual private network (VPN) is more prevalent among younger Russians. Of those aged between 18 and 24 years, over ** percent of survey respondents stated that they used a VPN regularly in March 2025. VPN services were increasingly used in Russia following the restrictions placed on social media and foreign media sites in the aftermath of Russia's invasion of Ukraine. Comparatively, less than ********* of those aged 55 years and older in Russia resorted to a VPN regularly or sometimes.

As of September 2023, YouTube as the most popular social media platform for global users, with 97 percent of respondents reporting to use the popular video platform. YouTube was also the most popular social media among Gen Z users, with 96 percent of respondents in this age group reporting to have used the video platform as of the examined period. Facebook's usage kept steady among among the general digital population, with around eight in 10 reporting to have used the platform. In comparison, the social media's popularity was in free fall among gen Z users with only four in 10 among those surveyed reporting to engage with the Meta-powered platform.

Learning powerful discriminative features is the key for machine fault diagnosis. Most existing methods based on convolutional neural network (CNN) have achieved promising results. However, they primarily focus on global features derived from sample signals and fail to explicitly mine relationships between signals. In contrast, graph convolutional network (GCN) is able to efficiently mine data relationships by taking graph data with topological structure as input, making them highly effective for feature representation in non-Euclidean space. In this article, to make good use of the advantages of CNN and GCN, we propose a graph attentional convolutional neural network (GACNN) for effective intelligent fault diagnosis, which includes two subnetworks of fully CNN and GCN to extract the multilevel features information, and uses Efficient Channel Attention (ECA) attention mechanism to reduce information loss. Extensive experiments on three datasets show that our framework improves the representation ability of features and fault diagnosis performance, and achieves competitive accuracy against other approaches. And the results show that GACNN can achieve superior performance even under a strong background noise environment.

Twitter user statistics show a varying degree of how often users login to the platform. Here’s what it looks like.

Hungary - Internet use: never was 5.33% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Hungary - Internet use: never - last updated from the EUROSTAT on June of 2025. Historically, Hungary - Internet use: never reached a record high of 33.00% in December of 2010 and a record low of 5.33% in December of 2024.

In 2022, 34 percent of respondents from the United States indicated using Apple's voice assistant Siri. Apple's voice assistant, Siri is used least among the respondents.