García-Robledo Gabriela A, Reyes-Ortiz José A, González-Beltrán Beatriz A, Bravo Maricela
{"title":"Ontology-Based Question Answering System for an Academic Domain","authors":"García-Robledo Gabriela A, Reyes-Ortiz José A, González-Beltrán Beatriz A, Bravo Maricela","doi":"10.5121/csit.2021.111902","DOIUrl":"https://doi.org/10.5121/csit.2021.111902","url":null,"abstract":"The development of question answering (QA) systems involves methods and techniques from the areas of Information Extraction (EI), Natural Language Processing (NLP), and sometimes speech recognition. A user interface that involves all these tasks requires deep development to improve the interaction between a user and a device. This paper describes a Spanish QA system for an academic domain through a multi-platform user interface. The system uses a voice query to be transformed into text. The semi-structured query is converted into SQWRL language to extract a system of ontologies from an academic domain using patterns. The answer of the ontologies is placed in templates classified according to the type of question. Finally, the answer is transformed into a voice. A method for experimentation is presented focusing on the questions asked in voice and their respective answers by experts from the academic domain in a set of 258 questions, obtaining a 92% accuracy.","PeriodicalId":193651,"journal":{"name":"NLP Techniques and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133075506","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}
{"title":"Design of SRAM-based 8T-Cell for Memory Alias Table","authors":"S. Abdel-Hafeez, Sanabel Otoom, Muhannad Quwaider","doi":"10.5121/csit.2021.111907","DOIUrl":"https://doi.org/10.5121/csit.2021.111907","url":null,"abstract":"Memory Alias Table exploits a major role in Register Renaming Unit (RRU) for maintaining the translation between logical registers to physical registers for the given instruction(s). This work presents the design of the memory Alias Table based on the 8TCell with multiport write, read, and content-addressable operation for 2-WAY three operands machine cycle. Results show that four read ports operate simultaneously within a half-cycle, while two-write ports operate simultaneously within the other half-cycle. The operation of content-addressable with two parallel ports is managed during the half-cycle of the read phase; thus, the three operations occur within a single cycle without latency. HSPICE simulations conduct 32-rows x 6-bit with 21T-Cell memory Alias Table that has 4- read ports, 2-write ports, and 2-content-addressable ports using a standard 65 nm/1V CMOS process. Simulations reveal that the proposed design operates within a one-cycle of 1 GHz consuming an average power of 0.87 mW","PeriodicalId":193651,"journal":{"name":"NLP Techniques and Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124807011","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}
{"title":"Interactive Dashboard Design for Manager, Data Analyst and Data Scientist Perspective","authors":"Temitope Olubunmi Awodiji, Temitope Olubunmi Awodiji","doi":"10.5121/csit.2021.111914","DOIUrl":"https://doi.org/10.5121/csit.2021.111914","url":null,"abstract":"With large amounts of unstructured data being produced every day, organizations are trying to extract as much relevant information as possible. This massive quantity of data is collected from a variety of sources, and data analysts and data scientists use it to create a dashboard that provides a complete picture of the organization's performance. Dashboards are business intelligence (BI) reporting tools that collect and show key metrics and key performance indicators (KPIs) on a single screen, enabling users to monitor and analyse business performance at a glance. An objective assessment of the company's overall performance, as well as of each department, is provided. If each department has access to the dashboard, it may serve as a springboard for future discussion and good decision-making. The goal of this article is to explain in detail the implementation of Dashboard and how it works, which will serve as a blueprint for building an effective dashboard with respect to best practices for dashboard design.","PeriodicalId":193651,"journal":{"name":"NLP Techniques and Applications","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114725185","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}
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