{"query":"http events query","domain_type":"research_paper","results":[{"position":1,"site_name":"arxiv.org","title":"Inference of Fine-Grained Event Causality from Blogs and Films","snippet":"Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a parti","url":"http://arxiv.org/abs/1708.09453v1","date":"2017-08-30","authors":["Zhichao Hu","Elahe Rahimtoroghi","Marilyn A Walker"],"venue":"arXiv","year":2017,"pdf_url":"http://arxiv.org/pdf/1708.09453v1"},{"position":2,"site_name":"doi.org","title":"HTTP Request - SQL Query Mapping Scheme for Malicious SQL Query Detection in Multitier Web Applications","url":"https://doi.org/10.5626/jok.2017.44.1.1","authors":["Yeongung Seo","Seungyoung Park"],"venue":"Journal of KIISE","year":2017,"cited_by_count":0},{"position":3,"site_name":"arxiv.org","title":"What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis","snippet":"Sound event localization and detection (SELD) is an emerging research topic that aims to unify the tasks of sound event detection and direction-of-arrival estimation. As a result, SELD inherits the challenges of both tasks, such as noise, reverberation, interference, polyphony, and non-stationarity of sound sources. Furthermore, SELD often faces an additional challenge of assigning correct corresp","url":"http://arxiv.org/abs/2107.10469v2","date":"2021-07-22","authors":["Thi Ngoc Tho Nguyen","Karn N. Watcharasupat","Zhen Jian Lee","Ngoc Khanh Nguyen","Douglas L. Jones","Woon Seng Gan"],"venue":"arXiv","year":2021,"pdf_url":"http://arxiv.org/pdf/2107.10469v2"},{"position":4,"site_name":"doi.org","title":"The HTTP QUERY Method","url":"https://doi.org/10.17487/rfc10008","authors":["J. Reschke","J.M. Snell","M. Bishop"],"year":2026,"cited_by_count":0},{"position":5,"site_name":"arxiv.org","title":"Generating Query Recommendations via LLMs","snippet":"Query recommendation systems are ubiquitous in modern search engines, assisting users in producing effective queries to meet their information needs. However, these systems require a large amount of data to produce good recommendations, such as a large collection of documents to index and query logs. In particular, query logs and user data are not available in cold start scenarios. Query logs are ","url":"http://arxiv.org/abs/2405.19749v2","date":"2024-05-30","authors":["Andrea Bacciu","Enrico Palumbo","Andreas Damianou","Nicola Tonellotto","Fabrizio Silvestri"],"venue":"arXiv","year":2024,"pdf_url":"http://arxiv.org/pdf/2405.19749v2"},{"position":6,"site_name":"doi.org","title":"HTTP Query metadata to DOI unixref","url":"https://doi.org/10.5555/httpquery","authors":["v p"],"venue":"Reports of test CrossRef tools","year":2014,"cited_by_count":0},{"position":7,"site_name":"arxiv.org","title":"Description Logics based Formalization of Wh-Queries","snippet":"The problem of Natural Language Query Formalization (NLQF) is to translate a given user query in natural language (NL) into a formal language so that the semantic interpretation has equivalence with the NL interpretation. Formalization of NL queries enables logic based reasoning during information retrieval, database query, question-answering, etc. Formalization also helps in Web query normalizati","url":"http://arxiv.org/abs/1312.6948v1","date":"2013-12-25","authors":["Sourish Dasgupta","Rupali KaPatel","Ankur Padia","Kushal Shah"],"venue":"arXiv","year":2013,"pdf_url":"http://arxiv.org/pdf/1312.6948v1"},{"position":8,"site_name":"doi.org","title":"SkeinDB: Cell-Interned MVCC and Query-Scoped Delta Responses for HTTP-Native Database Caching","snippet":"Modern web applications (dashboards, software-as-a-service (SaaS) admin consoles, multi-tenant portals) often rely on aggressive polling of list queries. This produces high fan-out read traffic, in which successive query results are frequently identical or differ only slightly. Today, HTTP caches and application-layer proxies (content delivery networks (CDNs), reverse proxies, Redis) can reduce tr","url":"https://doi.org/10.36227/techrxiv.177282490.07608916/v1","authors":["Michel Nguyen"],"year":2026,"cited_by_count":0},{"position":9,"site_name":"arxiv.org","title":"Semantic Query Reformulation in Social PDMS","snippet":"We consider social peer-to-peer data management systems (PDMS), where each peer maintains both semantic mappings between its schema and some acquaintances, and social links with peer friends. In this context, reformulating a query from a peer's schema into other peer's schemas is a hard problem, as it may generate as many rewritings as the set of mappings from that peer to the outside and transiti","url":"http://arxiv.org/abs/1111.6084v1","date":"2011-11-25","authors":["Angela Bonifati","Gianvito Summa","Esther Pacitti","Fady Draidi"],"venue":"arXiv","year":2011,"pdf_url":"http://arxiv.org/pdf/1111.6084v1"},{"position":10,"site_name":"doi.org","title":"request: High Level 'HTTP' Client","url":"https://doi.org/10.32614/cran.package.request","authors":["Scott Chamberlain"],"venue":"CRAN: Contributed Packages","year":2016,"cited_by_count":0},{"position":11,"site_name":"arxiv.org","title":"Real-time Log Query Interface for large datasets using Apache Spark","snippet":"Log Query Interface is an interactive web application that allows users to query the very large data logs of MobileInsight easily and efficiently. With this interface, users no longer need to talk to the database through command line queries, nor to install the MobileInsight client locally to fetch data. Users can simply select/type the query message through our web based system which queries the ","url":"http://arxiv.org/abs/1709.08001v1","date":"2017-09-23","authors":["Sandeep Sandha","Xin Xu","Yue Xin","Zhehan Li"],"venue":"arXiv","year":2017,"pdf_url":"http://arxiv.org/pdf/1709.08001v1"},{"position":12,"site_name":"doi.org","title":"Interactive Query based Abnormal Events Synopsis Generation in Surveillance Video","snippet":"Abstract Video summarization plays a crucial role in video analytics especially in surveillance scenarios as it represents the shorter version of large video. This short version contains frames that are unique in nature and the uniqueness is determined on the basis of either the user’s interest or the nature of contents. In case of surveillance system, summarization of abnormal events enables the ","url":"https://doi.org/10.21203/rs.3.rs-10481768/v1","authors":["Judi Vennila Thangaswamy","Balamurugan Vaniappan"],"year":2026,"cited_by_count":0},{"position":13,"site_name":"arxiv.org","title":"Creating A Model HTTP Server Program Using java","snippet":"HTTP Server is a computer programs that serves webpage content to clients. A webpage is a document or resource of information that is suitable for the World Wide Web and can be accessed through a web browser and displayed on a computer screen. This information is usually in HTML format, and may provide navigation to other webpage's via hypertext links. WebPages may be retrieved from a local comput","url":"http://arxiv.org/abs/1003.1497v1","date":"2010-03-07","authors":["Bala Dhandayuthapani Veerasamy"],"venue":"arXiv","year":2010,"pdf_url":"http://arxiv.org/pdf/1003.1497v1"},{"position":14,"site_name":"doi.org","title":"A High-Level Query Language for Events","url":"https://doi.org/10.1109/scw.2006.2","authors":["Francois Bry","Michael Eckert"],"venue":"2006 IEEE Services Computing Workshops","year":2006,"cited_by_count":9},{"position":15,"site_name":"arxiv.org","title":"Bitvector-aware Query Optimization for Decision Support Queries (extended version)","snippet":"Bitvector filtering is an important query processing technique that can significantly reduce the cost of execution, especially for complex decision support queries with multiple joins. Despite its wide application, however, its implication to query optimization is not well understood. In this work, we study how bitvector filters impact query optimization. We show that incorporating bitvector filte","url":"http://arxiv.org/abs/2005.03328v1","date":"2020-05-07","authors":["Bailu Ding","Surajit Chaudhuri","Vivek Narasayya"],"venue":"arXiv","year":2020,"pdf_url":"http://arxiv.org/pdf/2005.03328v1"},{"position":16,"site_name":"doi.org","title":"Fig. 1. Query form to search for protein sequences containing exact matches of short peptides using the PepString server (http://pepstring.eimb.ru)","url":"https://doi.org/10.32607/20758251-2025-17-3-67-76-132835","authors":[],"cited_by_count":0},{"position":17,"site_name":"arxiv.org","title":"Online Sketch-based Query Optimization","snippet":"Cost-based query optimization remains a critical task in relational databases even after decades of research and industrial development. Query optimizers rely on a large range of statistical synopses -- including attribute-level histograms and table-level samples -- for accurate cardinality estimation. As the complexity of selection predicates and the number of join predicates increase, two proble","url":"http://arxiv.org/abs/2102.02440v1","date":"2021-02-04","authors":["Yesdaulet Izenov","Asoke Datta","Florin Rusu","Jun Hyung Shin"],"venue":"arXiv","year":2021,"pdf_url":"http://arxiv.org/pdf/2102.02440v1"},{"position":18,"site_name":"arxiv.org","title":"Bayesian Decision Making to Localize Visual Queries in 2D","snippet":"This report describes our approach for the EGO4D 2023 Visual Query 2D Localization Challenge. Our method aims to reduce the number of False Positives (FP) that occur because of high similarity between the visual crop and the proposed bounding boxes from the baseline's Region Proposal Network (RPN). Our method uses a transformer to determine similarity in higher dimensions which is used as our prio","url":"http://arxiv.org/abs/2305.17611v1","date":"2023-05-28","authors":["Syed Asjad","Aniket Gupta","Hanumant Singh"],"venue":"arXiv","year":2023,"pdf_url":"http://arxiv.org/pdf/2305.17611v1"},{"position":19,"site_name":"doi.org","title":"Modeling Query Events in Spoken Natural Language for Human-Database Interaction","url":"https://doi.org/10.5772/5873","authors":["Omar U.","SeungJin Lim"],"venue":"Human Computer Interaction: New Developments","year":2008,"cited_by_count":0}],"total_results":19,"page":0,"_links":{"self":{"href":"https://web.23signs.org/v1/search?domain_type=research_paper&query=http+events+query","method":"GET"},"fetch":{"href":"https://web.23signs.org/v1/fetch","method":"POST","title":"Fetch up to 10 URLs as markdown, JSON blocks or clean HTML"},"page":{"href":"https://web.23signs.org/{+url}","templated":true,"method":"GET","title":"One page as a markdown document (Accept: application/json for the result object)"},"watch":{"href":"https://web.23signs.org/v1/search","method":"QUERY","title":"Events Query: repeat this search and receive new results in-band"},"search":{"href":"https://web.23signs.org/v1/search{?query,domain_type,include_domains,exclude_domains,language,location,recency_minutes,after_date,before_date,pub_year_min,pub_year_max,page}","templated":true,"method":"GET","title":"Search papers (arXiv, Crossref), news (GDELT) or Wikipedia"},"status":{"href":"https://web.23signs.org/v1/status","method":"GET","title":"Limits, search backends and pricing right now"},"openapi":{"href":"https://web.23signs.org/openapi.yaml","type":"application/yaml","title":"OpenAPI description of this API (also /openapi.json)"},"catalog":{"href":"https://web.23signs.org/.well-known/api-catalog","type":"application/linkset+json","title":"RFC 9727 API catalog"},"describedby":{"href":"https://web.23signs.org/SKILL.md","type":"text/markdown","title":"Agent skill file: when to use, parameters, response and error shapes, the Events Query flow"},"next":{"href":"https://web.23signs.org/v1/search?domain_type=research_paper&query=http+events+query&page=1","method":"GET","title":"Next page"}}}