ACIS 2015 Bibliographical-based Facets for Expertise SearchGan Keng Hoon
This document discusses expertise search and proposes a bibliographical-based faceted search framework. It describes the problem with current faceted search approaches that use general predefined facets rather than facets extracted from bibliographical texts. The proposed framework first extracts facet values from texts using natural language processing techniques. It then generates facet-value pairs to refine search results. The framework was evaluated on a dataset of bibliographical records using precision and recall measured against human experts. The framework aims to improve discovery of expertise by allowing refinement of search results based on extracted facets from bibliographical texts.
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Lic. Alba Bitalina Vargas Saritama
Ciclo: Séptimo
Bimestre: Segundo
Emil Pulido on Qualitative Research: Analyzing Qualitative DataEmilEJP
There are no set formulas or steps for analyzing and interpreting qualitative research data. The main goals are to summarize the collected data accurately and find meaning within the data. When analyzing qualitative data, researchers should explore all possible perspectives to identify patterns and new understandings. Suggested steps include becoming familiar with the data, identifying themes, describing findings, categorizing and coding data, and looking for themes. Computer software can help with data storage and manipulation but does not replace the researcher's analysis abilities. Strategies for interpretation include extending the analysis, connecting findings to experiences, seeking advice, contextualizing within literature, and linking to theory. Researchers should ensure the credibility of interpretations by considering factors like observation reliability and potential biases.
This document provides information about getting fully solved assignments from an assignment help service. It lists a mail ID and phone number to contact for assistance with marketing research assignments. It then provides sample answers to 4 marketing research assignment questions, covering topics like primary research processes, types of data, qualitative vs. quantitative research methods, conjoint analysis, and probability sampling methods.
This document provides guidance on analyzing both quantitative and qualitative data. It discusses coding data as it is collected and categorizing variables like gender, age, and origin. Generalizations should be drawn from the full data set, noting common responses and deviations. Findings should be summarized using examples and illustrations from the data through quotes and figures. Codes must be mutually exclusive, exhaustive, and consistently applied. Researchers should reflect on how their findings link to the original questions, fit with prior work, and further the field through new assertions and contributions to knowledge, while also considering limitations.
Data collection is the process of systematically gathering and measuring information to answer questions and evaluate outcomes. There are three main sources of data: secondary data collected by others, internal data from within an organization, and primary data collected through questioning and observation. Questionnaires can be structured, with predetermined questions and responses, or unstructured, allowing respondents to answer freely in their own words. Properly designing questionnaires requires skill, as researchers must determine what information is needed, the questionnaire type and format, question content and response format, sequencing, and whether it will be disguised or structured. Factors like cover letters, question number, logical arrangement, simplicity, sensitivity, instructions, footnotes, objectivity, calculations, pre-testing, cross-
ACIS 2015 Bibliographical-based Facets for Expertise SearchGan Keng Hoon
This document discusses expertise search and proposes a bibliographical-based faceted search framework. It describes the problem with current faceted search approaches that use general predefined facets rather than facets extracted from bibliographical texts. The proposed framework first extracts facet values from texts using natural language processing techniques. It then generates facet-value pairs to refine search results. The framework was evaluated on a dataset of bibliographical records using precision and recall measured against human experts. The framework aims to improve discovery of expertise by allowing refinement of search results based on extracted facets from bibliographical texts.
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Lic. Alba Bitalina Vargas Saritama
Ciclo: Séptimo
Bimestre: Segundo
Emil Pulido on Qualitative Research: Analyzing Qualitative DataEmilEJP
There are no set formulas or steps for analyzing and interpreting qualitative research data. The main goals are to summarize the collected data accurately and find meaning within the data. When analyzing qualitative data, researchers should explore all possible perspectives to identify patterns and new understandings. Suggested steps include becoming familiar with the data, identifying themes, describing findings, categorizing and coding data, and looking for themes. Computer software can help with data storage and manipulation but does not replace the researcher's analysis abilities. Strategies for interpretation include extending the analysis, connecting findings to experiences, seeking advice, contextualizing within literature, and linking to theory. Researchers should ensure the credibility of interpretations by considering factors like observation reliability and potential biases.
This document provides information about getting fully solved assignments from an assignment help service. It lists a mail ID and phone number to contact for assistance with marketing research assignments. It then provides sample answers to 4 marketing research assignment questions, covering topics like primary research processes, types of data, qualitative vs. quantitative research methods, conjoint analysis, and probability sampling methods.
This document provides guidance on analyzing both quantitative and qualitative data. It discusses coding data as it is collected and categorizing variables like gender, age, and origin. Generalizations should be drawn from the full data set, noting common responses and deviations. Findings should be summarized using examples and illustrations from the data through quotes and figures. Codes must be mutually exclusive, exhaustive, and consistently applied. Researchers should reflect on how their findings link to the original questions, fit with prior work, and further the field through new assertions and contributions to knowledge, while also considering limitations.
Data collection is the process of systematically gathering and measuring information to answer questions and evaluate outcomes. There are three main sources of data: secondary data collected by others, internal data from within an organization, and primary data collected through questioning and observation. Questionnaires can be structured, with predetermined questions and responses, or unstructured, allowing respondents to answer freely in their own words. Properly designing questionnaires requires skill, as researchers must determine what information is needed, the questionnaire type and format, question content and response format, sequencing, and whether it will be disguised or structured. Factors like cover letters, question number, logical arrangement, simplicity, sensitivity, instructions, footnotes, objectivity, calculations, pre-testing, cross-
An introduction to conducting a systematic literature review for social scien...rosie.dunne
An introduction to conducting a systematic literature review for social scientists and health researchers presented by Luke van Rhoon Health Behaviour Change Research Group, School of Psychology, NUI Galway November 2020
This document discusses the report writing process and research methods. It covers:
1) Characteristics of formal and informal reports and classifications of business reports.
2) The problem-solving process which includes recognizing the problem, selecting a solution method, collecting and organizing data, and arriving at an answer.
3) Types of primary and secondary research sources and objectives of secondary research such as establishing a starting point and avoiding duplication.
Collecting, analyzing and interpreting dataJimi Kayode
This document discusses the process of collecting, analyzing, and interpreting data in research. It involves:
1) Collecting appropriate data through selected methods and tools based on the research question, respondents, objectives, and available resources.
2) Analyzing raw data by coding and tabulating it into categories for further analysis, attempting to classify it into purposeful categories. Software can help with large amounts of data.
3) Interpreting relationships or differences found in the analysis to determine validity of conclusions and explain findings, with the goal of answering the original research questions.
This document discusses secondary data - data originally collected by someone other than the user. It defines secondary data and lists common sources like censuses and government/organizational records. The purposes of secondary data are extracting relevant information, fact finding, model building, and data mining. Criteria for evaluating secondary data include specifications, error, currency, objectives, nature, and dependability. Secondary data is advantageous as it is economical, time saving, and helps focus primary data collection. However, disadvantages are that secondary data may not fit the research factors and accuracy is unknown. Secondary data can be used to identify problems, better define problems, develop research approaches, formulate research designs, and help interpret primary data.
The final exam is due xxxx20xx. late assignments will not be accBHANU281672
The document outlines requirements for a final exam project. Students must choose one of two options: 1) a research report on a topic related to software engineering or 2) a question/answer bank derived from assigned course materials. The research report option requires a scholarly paper that follows specific formatting guidelines and includes chapters on introduction, literature review, methodology, findings, and conclusions. The question/answer bank option requires students to create at least 10 multiple choice, fill-in-the-blank, multiple answer, or essay questions for each assigned chapter, for a total of 130 questions. Both options are worth 800 points and must be submitted by the specified due date to receive credit.
With the help of this powerpoint presentation, Ken Mease, discusses the advantages of various types of data sources and collection methods, including archival and secondary data, survey data, quantitative and qualitative approaches and data, and finally de jure and de facto information. The presentation was held at the Workshop on Governance Assessment Methods and Applications of Governance Data in Policy-Making (June 2009)
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding texts, identifying patterns, and reducing qualitative data into quantitative codes. It also outlines several stages of qualitative analysis including familiarization with data, transcription, organization, coding, identifying themes, recoding, developing categories, exploring relationships between categories, and developing theories. Finally, it discusses challenges of qualitative analysis including placing raw data into logical categories and communicating interpretations to others.
This document discusses secondary analysis of existing data sets. It describes publicly available data from sources like the CDC that can be downloaded for free. It also covers privately held data from previous studies that researchers may collaborate on or obtain approval to analyze. The advantages are reduced costs and resources compared to primary data collection, but limitations include working only with existing variables and needing to trust the original data collection methods. Ethics review is typically required for private or clinical data but not anonymous public sources.
This document discusses different types of data analysis methods, including content analysis and secondary data analysis. Content analysis involves breaking down written, spoken, or visual communication into common elements or themes. It requires determining the unit of analysis, indicators, and then coding the data according to those indicators. Secondary data analysis involves reanalyzing existing data collected by others. It saves costs and time but risks the data not being suitable for the new research purposes.
This document provides guidance on preparing for a major research project, summer internship, or on-the-job training. It discusses selecting a specialization, developing a title, and outlines the key components of a synopsis including an introduction, problem statement, objectives, research methodology, and limitations. The methodology section covers research design, data collection sources and techniques, sampling, tools for analysis, and hypotheses. Finally, it provides examples of titles and formatting for references.
The document discusses the meaning of research and reports. It defines research as an organized and systematic way of finding answers to questions. There are several types of business research classified by method and purpose, including basic research, applied research, evaluation research, R&D, and action research. The types of research methods include historical research, correlational research, descriptive research, causal-comparative, and experimental. Reports are defined as documents that communicate factual information for some business purpose. The steps of writing a research report include choosing a topic, determining scope, locating information, analyzing data, and presenting the report.
This document discusses content analysis as a qualitative data analysis technique. It begins by defining content analysis as a method to systematically reduce and categorize textual data to identify patterns and relationships. The document then outlines the coding process, describing codes as labels assigned to segments of text that are then grouped into categories. It provides examples of different types of codes and discusses hierarchical coding structures. Steps in the content analysis process are also outlined, from defining research questions to data analysis and interpretation. Issues of reliability in content analysis are raised at the end.
This document provides an overview of an online talk about writing research papers and literature reviews. It discusses what constitutes a research paper, the different types including conceptual and empirical papers. It also outlines the steps for conducting a literature review including narrowing the topic, searching for literature, evaluating sources, organizing findings, developing a thesis statement, and writing the review. Additionally, it demonstrates using a table format to organize literature review findings and identify gaps for future research. The talk emphasizes focusing recent conceptual and empirical studies to develop a strong theoretical foundation and identify research gaps.
Coding of the Interview Theme Analysis.docxwrite31
This document provides instructions and resources for a student assignment on coding interview transcripts and identifying themes as part of a qualitative research methods course. Students are asked to code an interview transcript based on their research question, list the codes developed, describe their coding process, and apply codes directly to excerpts from the transcript. Resources include a textbook chapter on qualitative data analysis and coding, as well as optional readings on interviewing techniques and improving qualitative research skills.
Coding of the Interview Theme Analysis.docxwrite12
This document provides instructions and resources for a student assignment on coding interview transcripts and identifying themes as part of a qualitative research methods course. Students are asked to code a provided interview transcript, list the codes developed, describe their coding process, and apply codes directly to excerpts from the transcript. Resources include a textbook chapter on qualitative data analysis and coding, as well as optional readings on improving interview and coding techniques.
A research proposal outlines a research project and provides information on key elements such as the research question, methodology, and ethical considerations. It connects the proposed research to existing literature and discusses the importance and viability of the research topic. Important components of a research proposal include the introduction of the research topic and why it is being studied, a literature review, research design outlining the methodology and methods of data collection/analysis, and consideration of ethical issues. The proposal should also include sections on aims/objectives, conclusions/recommendations, and references. A good title reflects the well-defined aims of the research in a concrete manner.
A research article reports the results of original research on a particular topic. It follows the IMRD format, with separate sections for the Introduction, Methods, Results, and Discussion. The Introduction presents the problem being studied and previous research in the area. The Methods section outlines the methodology used. The Results section presents the data from the analysis. Finally, the Discussion section interprets the results in the context of existing knowledge and considers theoretical implications. Proper citations of sources are also important.
This document provides an overview of a lecture on educational research and statistics. It discusses different types of research methods, including historical research, descriptive research, and experimental research. It also covers conducting literature reviews, including introducing literature reviews, their importance, sources, and the citation and referencing process. The objectives of the lecture are outlined, and each research method is defined and explained in detail with examples. Guidelines are provided for each step in conducting literature reviews and ensuring proper citation and referencing.
This document provides information about action research methods in education. It discusses:
- Qualitative and quantitative research methods including case studies, surveys, and experiments.
- Action research is conducted by teachers and other education stakeholders to investigate problems and improve student learning. It involves gathering data, analysis, and developing an action plan.
- Examples of action research questions focus on teaching practices that can be improved. The goal is to empower teachers to study their own practices.
An introduction to conducting a systematic literature review for social scien...rosie.dunne
An introduction to conducting a systematic literature review for social scientists and health researchers presented by Luke van Rhoon Health Behaviour Change Research Group, School of Psychology, NUI Galway November 2020
This document discusses the report writing process and research methods. It covers:
1) Characteristics of formal and informal reports and classifications of business reports.
2) The problem-solving process which includes recognizing the problem, selecting a solution method, collecting and organizing data, and arriving at an answer.
3) Types of primary and secondary research sources and objectives of secondary research such as establishing a starting point and avoiding duplication.
Collecting, analyzing and interpreting dataJimi Kayode
This document discusses the process of collecting, analyzing, and interpreting data in research. It involves:
1) Collecting appropriate data through selected methods and tools based on the research question, respondents, objectives, and available resources.
2) Analyzing raw data by coding and tabulating it into categories for further analysis, attempting to classify it into purposeful categories. Software can help with large amounts of data.
3) Interpreting relationships or differences found in the analysis to determine validity of conclusions and explain findings, with the goal of answering the original research questions.
This document discusses secondary data - data originally collected by someone other than the user. It defines secondary data and lists common sources like censuses and government/organizational records. The purposes of secondary data are extracting relevant information, fact finding, model building, and data mining. Criteria for evaluating secondary data include specifications, error, currency, objectives, nature, and dependability. Secondary data is advantageous as it is economical, time saving, and helps focus primary data collection. However, disadvantages are that secondary data may not fit the research factors and accuracy is unknown. Secondary data can be used to identify problems, better define problems, develop research approaches, formulate research designs, and help interpret primary data.
The final exam is due xxxx20xx. late assignments will not be accBHANU281672
The document outlines requirements for a final exam project. Students must choose one of two options: 1) a research report on a topic related to software engineering or 2) a question/answer bank derived from assigned course materials. The research report option requires a scholarly paper that follows specific formatting guidelines and includes chapters on introduction, literature review, methodology, findings, and conclusions. The question/answer bank option requires students to create at least 10 multiple choice, fill-in-the-blank, multiple answer, or essay questions for each assigned chapter, for a total of 130 questions. Both options are worth 800 points and must be submitted by the specified due date to receive credit.
With the help of this powerpoint presentation, Ken Mease, discusses the advantages of various types of data sources and collection methods, including archival and secondary data, survey data, quantitative and qualitative approaches and data, and finally de jure and de facto information. The presentation was held at the Workshop on Governance Assessment Methods and Applications of Governance Data in Policy-Making (June 2009)
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding texts, identifying patterns, and reducing qualitative data into quantitative codes. It also outlines several stages of qualitative analysis including familiarization with data, transcription, organization, coding, identifying themes, recoding, developing categories, exploring relationships between categories, and developing theories. Finally, it discusses challenges of qualitative analysis including placing raw data into logical categories and communicating interpretations to others.
This document discusses secondary analysis of existing data sets. It describes publicly available data from sources like the CDC that can be downloaded for free. It also covers privately held data from previous studies that researchers may collaborate on or obtain approval to analyze. The advantages are reduced costs and resources compared to primary data collection, but limitations include working only with existing variables and needing to trust the original data collection methods. Ethics review is typically required for private or clinical data but not anonymous public sources.
This document discusses different types of data analysis methods, including content analysis and secondary data analysis. Content analysis involves breaking down written, spoken, or visual communication into common elements or themes. It requires determining the unit of analysis, indicators, and then coding the data according to those indicators. Secondary data analysis involves reanalyzing existing data collected by others. It saves costs and time but risks the data not being suitable for the new research purposes.
This document provides guidance on preparing for a major research project, summer internship, or on-the-job training. It discusses selecting a specialization, developing a title, and outlines the key components of a synopsis including an introduction, problem statement, objectives, research methodology, and limitations. The methodology section covers research design, data collection sources and techniques, sampling, tools for analysis, and hypotheses. Finally, it provides examples of titles and formatting for references.
The document discusses the meaning of research and reports. It defines research as an organized and systematic way of finding answers to questions. There are several types of business research classified by method and purpose, including basic research, applied research, evaluation research, R&D, and action research. The types of research methods include historical research, correlational research, descriptive research, causal-comparative, and experimental. Reports are defined as documents that communicate factual information for some business purpose. The steps of writing a research report include choosing a topic, determining scope, locating information, analyzing data, and presenting the report.
This document discusses content analysis as a qualitative data analysis technique. It begins by defining content analysis as a method to systematically reduce and categorize textual data to identify patterns and relationships. The document then outlines the coding process, describing codes as labels assigned to segments of text that are then grouped into categories. It provides examples of different types of codes and discusses hierarchical coding structures. Steps in the content analysis process are also outlined, from defining research questions to data analysis and interpretation. Issues of reliability in content analysis are raised at the end.
This document provides an overview of an online talk about writing research papers and literature reviews. It discusses what constitutes a research paper, the different types including conceptual and empirical papers. It also outlines the steps for conducting a literature review including narrowing the topic, searching for literature, evaluating sources, organizing findings, developing a thesis statement, and writing the review. Additionally, it demonstrates using a table format to organize literature review findings and identify gaps for future research. The talk emphasizes focusing recent conceptual and empirical studies to develop a strong theoretical foundation and identify research gaps.
Coding of the Interview Theme Analysis.docxwrite31
This document provides instructions and resources for a student assignment on coding interview transcripts and identifying themes as part of a qualitative research methods course. Students are asked to code an interview transcript based on their research question, list the codes developed, describe their coding process, and apply codes directly to excerpts from the transcript. Resources include a textbook chapter on qualitative data analysis and coding, as well as optional readings on interviewing techniques and improving qualitative research skills.
Coding of the Interview Theme Analysis.docxwrite12
This document provides instructions and resources for a student assignment on coding interview transcripts and identifying themes as part of a qualitative research methods course. Students are asked to code a provided interview transcript, list the codes developed, describe their coding process, and apply codes directly to excerpts from the transcript. Resources include a textbook chapter on qualitative data analysis and coding, as well as optional readings on improving interview and coding techniques.
A research proposal outlines a research project and provides information on key elements such as the research question, methodology, and ethical considerations. It connects the proposed research to existing literature and discusses the importance and viability of the research topic. Important components of a research proposal include the introduction of the research topic and why it is being studied, a literature review, research design outlining the methodology and methods of data collection/analysis, and consideration of ethical issues. The proposal should also include sections on aims/objectives, conclusions/recommendations, and references. A good title reflects the well-defined aims of the research in a concrete manner.
A research article reports the results of original research on a particular topic. It follows the IMRD format, with separate sections for the Introduction, Methods, Results, and Discussion. The Introduction presents the problem being studied and previous research in the area. The Methods section outlines the methodology used. The Results section presents the data from the analysis. Finally, the Discussion section interprets the results in the context of existing knowledge and considers theoretical implications. Proper citations of sources are also important.
This document provides an overview of a lecture on educational research and statistics. It discusses different types of research methods, including historical research, descriptive research, and experimental research. It also covers conducting literature reviews, including introducing literature reviews, their importance, sources, and the citation and referencing process. The objectives of the lecture are outlined, and each research method is defined and explained in detail with examples. Guidelines are provided for each step in conducting literature reviews and ensuring proper citation and referencing.
This document provides information about action research methods in education. It discusses:
- Qualitative and quantitative research methods including case studies, surveys, and experiments.
- Action research is conducted by teachers and other education stakeholders to investigate problems and improve student learning. It involves gathering data, analysis, and developing an action plan.
- Examples of action research questions focus on teaching practices that can be improved. The goal is to empower teachers to study their own practices.
Systematic Literature Reviews : Concise Overviewyoukayaslam
This document provides an overview of a workshop on systematic approaches to literature reviewing led by Dr. Mark Matthews. The workshop explores elements of the systematic review process and how they can be adapted for thesis literature reviews and keeping up with literature through a PhD. It discusses formulating review questions, systematically searching literature databases and other sources, selecting studies, critically appraising research, analyzing and synthesizing findings, and structuring the writing of literature reviews. Challenges of literature reviews and additional resources are also presented.
Introduction to research and its different aspectsbarsharoy19
This slide introduces the basic aspects of a research paper. It gives a brief description on impact factor, citation index and different categories of research paper
This document provides an overview of library research skills for Murdoch Business School masters students. It covers the library website and resources available, research steps to follow for a successful research process, how to get help from librarians, subject guides as a one-stop shop for resources in different subjects, and search skills using Boolean operators, truncation, and wildcards to refine database searches. The research steps include analyzing the topic, finding background information, further researching using relevant resources, evaluating resources, managing references, and citing references.
Finding and reviewing hr literature and information sources for PhD hr disser...Tutors India
For a number of research questions, reviewing the literature is the best method to provide answers. Reviewers find useful when the researcher provides the theory that has been evaluated or proof in a certain area or to check the validity or precision of a certain theory.
In this topic, we have discussed reviewing HR literature and information sources for PhD Dissertation. Researchers are encouraged to research and good dissertation writing skills are necessary. The present article helps the USA, the UK, Europe and the Australian students pursuing their master’s degree to identify the best HR literature review, which is usually considered to be challenging.
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The document extents detail guidelines on project report preparation at UG level. It details on the different components of research methodology to be undertaken and its little detailing which will help students to design their own research project with quality. Every research endeavor has objectives as well as defined outcomes. The ppt detailed the same.
Optimized Technique for Academic Search engine Optimizationkomalkumari103
Optimized technique for academic search engine optimization (ASEO) is proposed to improve ranking and exposure of academic articles. The proposed system uses a novel Lingo clustering algorithm to group similar entities, reducing the original dataset. Semantics and context of search queries are also considered to better determine relevance. Evaluation shows the technique improves precision, recall, and F1-score for academic search results. Future work could incorporate additional analysis like part-of-speech tagging and reference other paper sections to develop article impact factors.
1) The document discusses a writing assignment to evaluate a research article on a technology topic.
2) Students are asked to write a 4-6 page paper identifying the components of the research article such as the title, abstract, methods section, and discuss how the format contributes to the purpose of the writing.
3) The paper should also discuss what makes research writing different from other types of writing and identify the major components of a research paper format.
1) The document discusses a writing assignment to evaluate a research article on a technology topic.
2) Students are asked to write a 4-6 page paper identifying the components of the research article such as the title, abstract, methods section, and discuss how the format furthers the purpose of the writing.
3) The paper should also discuss what makes research writing different from other types of writing and identify the major components of a research paper format.
This is a sneak peek into the 2014 Spring EAIE Academy course 'SEO and online content: strategies for international student recruitment'.
Are you an international higher education professional? Check out all the training events of the European Association for International Education (EAIE) here: www.eaie.org/training
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OSS 2020 Using SOLR as Open-Source Search Platform.pdfGan Keng Hoon
This document summarizes a presentation about using SOLR as an open-source search platform for organizational research expert retrieval. The presentation introduces SOLR and text search concepts, demonstrates how to set up and use a basic SOLR installation with sample data, and shows a prototype that indexes publication data from academics to enable expert search within an organization. The prototype crawls Scopus data, indexes it with SOLR, implements a search interface, and displays results to find research experts from the School of Computer Sciences.
This document discusses procrastination among PhD students. It begins by asking the reader to identify a task they are procrastinating on and provides examples like updating one's supervisor or writing a journal paper. It then explores common reasons for procrastinating like lacking awareness of deadlines or being afraid to start. The document encourages breaking large tasks into smaller steps, setting clear deadlines, and finding accountability partners to avoid procrastination. It emphasizes staying mindful of distraction and using procrastination time productively, like learning or self-care. Overall, the document provides advice on analyzing the causes of procrastination and developing strategies to improve time management and progress for PhD students.
Guest Lecture for Principles of Data Analytics.pdfGan Keng Hoon
This document provides an overview of text analytics and mining techniques. It discusses topics like text normalization, tokenization, lemmatization, stemming, part-of-speech tagging, document representation using bags of words and TF-IDF, inverted indexing, text retrieval, sentiment analysis using lexicons, and keyword extraction through text mining activities. Various text analytics techniques in the pipeline from data collection to modeling are covered.
Knowledge Representation Reasoning and Acquisition.pdfGan Keng Hoon
This document provides an overview of knowledge representation, reasoning, and acquisition. It discusses how knowledge helps enable intelligent behavior and decision making. It describes artificial intelligence as using computational means to achieve intelligent behavior. Key topics covered include knowledge representation languages, ontologies for structuring knowledge, semantic standards like OWL and RDF, and knowledge acquisition from both structured and unstructured sources.
Project: Interfacing Chatbot with Data Retrieval and Analytics Queries for De...Gan Keng Hoon
This project covers several areas of research and development such as data storage and retrieval, domain analytics specification, natural language conversational modelling and query transformation. Prospective collaborators include dashboard solution providers, HPC centers and relevant interested parties.
Interfacing Chatbot with Data Retrieval and Analytics Queries for Decision Ma...Gan Keng Hoon
https://doi.org/10.1007/978-981-13-8323-6_32
Conventional data analytics process uses dashboard with tables, charts, summaries, search tool in projecting its analysis outcome to its user with the goal of enabling discovery of useful information or suggesting conclusions to support decision-making. Such decision-making mechanisms can be improved further by using natural language interface in the dashboard components, e.g. using natural language keywords to search the sales performance of a product. Motivated by the needs to enable a user friendlier interaction with analytics outcome, this paper proposes a chatbot, called analytics bot who can assist in the role of decision making by delivering information of dashboard components with human like conversational pattern.
Text and Sentiment Analytics for Business IntelligenceGan Keng Hoon
This document outlines how text and sentiment analytics can be used for business intelligence by analyzing unstructured customer feedback from sources like social media and reviews. It discusses how text analytics is used to extract and analyze text, while sentiment analytics determines if sentiments are positive, negative, or neutral. An example of analyzing hotel reviews is provided, showing how aspects like cleanliness could be identified and sentiments around those aspects could be scored. Challenges like differentiating credible vs fake reviews and handling multiple languages are also noted.
Category & Training Texts Selection for Scientific Article Categorization in ...Gan Keng Hoon
This document discusses improving the categorization of scientific articles for an expert search system. It proposes training a category model using labeled training texts from a related domain, rather than requiring labeled scientific articles. Features are extracted from the training texts using TF-IDF and n-grams. The category model is tested on scientific articles by calculating cosine similarity between article and category feature vectors. An evaluation compares automated versus manual training text selection across common categories and common/specific categories, finding that manual selection achieves higher accuracy averages across five expert evaluators. The approach shows potential but challenges include selecting representative training texts and ensuring category coverage of the domain.
Concepts and Challenges of Text Retrieval for Search EngineGan Keng Hoon
This document discusses concepts and challenges in text retrieval for search engines. It provides an overview of text retrieval and search engine concepts. Some key challenges discussed are semantics and specificity in queries. The document also uses an example of an expert search engine to illustrate a case study. It describes various components involved in text retrieval including document representation, indexing, inverted indexing, retrieval functions and evaluation metrics.
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This document provides an overview of knowledge acquisition, representation, and publishing. It defines knowledge and discusses how knowledge can be captured, structured, and shared. Knowledge acquisition involves extracting knowledge from sources, structuring the knowledge, and organizing it for representation. Knowledge representation standards like the Resource Description Framework (RDF) and ontologies provide structured descriptions of knowledge through semantic annotations and metadata. Personal knowledge publishing allows individuals to share their knowledge through various online tools and formats.
The document describes Classsify, a tool that can classify experts by their expertise using training text in selected categories. It allows administrators to select categories, train a model, and view cosine matching reports to determine an expert's categories based on their bibliography. Classsify can then be used to search for experts based on their visualized expertise obtained through Classsify, filtering results by expertise.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
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Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
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Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
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6. Bibliographical –based
Faceted Search
◉ The current facets are general
filter.
◉ A facet and its value is
normally given.
E.g. “Year” facet and its value
“2009”
“Author” facet and its value
“Syaheerah L. Lutfi”, “Juan
Manuel Montero” etc.
7. Bibliographical –based
Faceted Search
Facets like algorithm, framework, model, application etc. are
may be useful to refine the search results.
Challenge to extract the value dynamically
FacetValue
8. Bibliographical –based
Faceted Search
Why is this Challenging ?
as facets and their values are normally not specified as
part of the structured/descriptive information of a
bibliographical text.
<year>2015</year>
Scattered throughout the text, e.g Title, Abstract,
Headings, Paragraphs !
9. This work
◉ Automate the extraction
of dynamic facets.
(Backend Engine)
◉ Use the dynamic facets
in bibliographical
search results filtering.
(Frontend App)
11. Facet Lexicon Preparation
The facet and facet
stemming list is
prepared domain
expert.
Stemming list
• Detect variant of
words of the
same facet
12. Values Extraction
1. Split text to
sentences.
2. Detect facet.
3. Detect value of
the facet.
Algorithm for (3)
a. Sentence
segmentation
with Stop Words
b. Capital
Detection
c. Other issues.
FacetFacet value
13. Facet – Value Mapping
Knowledge base on
facet and values are
created.
19. Thank you
You can find our works at
◉ ir.cs.usm.my
You can email us at
◉ chekwei7280@gmail.com (Teh Chek Wei)
◉ khgan@usm.my (Gan Keng Hoon)
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