Text-based Tools Research Articles

Policy synergy on stock price crash risk: An intergovernmental perspective

Both central and local government policies present implications for companies and investors. While several studies evidence that government policies shape corporate behaviors, few studies have explored them from an intergovernmental view. This research constructs a new measurement of intergovernmental policy synergy with text-based tools and reveals an inverted U-shaped relation between central-local policy synergy and stock price crash risk, which still holds after checking robustness. Furthermore, we validate corporate greenwashing and divergence of investors’ opinion as underlying mechanisms. Additionally, the relation is more prominent among firms without online interactive platform and firms with high web search intensity.

Evaluating The Impact of Feature Extraction Techniques on Arabic Reviews Classification

With the advent of AI text-based tools and applications, the need to introduce and investigate word-processing tools has also been raised. NLP tools and techniques have developed rapidly for some languages, such as English. However, other languages, such as Arabic, still need to introduce more methods and techniques to provide more explanations. In this study, we present a sample to classify customer reviews which are written in Arabic. The data set (HARD) is used to be certified as a dataset for work. This study adopted four classifications in machine learning and deep learning (CNN, RNN, NB, LR). In addition, the texts were cleaned using data cleaning techniques, and the stemming technique was used, and three types of them were implemented (Khoja Stemmer, Snowball Stemmer, Thashaphyne Stemmer). Moreover, two methods of feature extraction were used (TF-IDF, N-gram). The results of the model provided several explanations. The best performance resulted from the use of (CNN+ Snowball Stemmer +N-gram) with accuracy (%93.5). The results of the model stated that some workbooks are sensitive to the use of different tools, and some accuracy performance can also be affected if there are different methods for extracting the features used. Either feature extraction has an impact on accuracy performance. The model also proved that colloquial Arabic could cause some limitations because different dialects can give different meanings across different regions or countries. The results of the study open the door to exploring other tools and methods to enrich natural Arabic language processing and contribute to the development of new applications that support Arabic content.

REINVENTING QUALITATIVE DATA COLLECTION BOUNDARIES IN PANDEMIC DISPENSATIONS LIKE THE COVID-19

The newly discovered coronavirus (COVID-19) has changed the traditional methods of conducting qualitative research which, by nature, heavily relies on face-to-face interaction through interviews, field work, and focus group discussions for data collection. However, the various COVID-19 safety protocols, including physical distancing, mask wearing, quarantines, restrictions of national and international travels as well as the need to limits one’s movements to reduce exposure to the virus, have all disrupted traditional methods of qualitative data collection, thereby leaving researchers stranded. This article explores a number of other methods which researchers can utilize to achieve the same results released by face-to-face interactions. These methods include the use of digital voice, video, and text-based tools, online surveys, and content analysis. Drawing from a qualitative study conducted during a lockdown in Zimbabwe between May and June 2020, the article demonstrates how these tools can be used to generate data or to sample data that is already available to satisfy research questions and meet research objectives.

Booster or stabilizer? Economic policy uncertainty: New firm-specific measurement and impacts on stock price crash risk

We employ text-based tools to construct a new measurement of firm-specific economic policy uncertainty (FEPU, hereafter): the share of sentences related to EPU in management discussion and analysis (MD&A, hereafter). Our measure enables us to exploit the wide range of variation in actual and perceived EPU across firms and over time. Linking our FEPU to stock price crash risk shows that FEPU negatively affects stock price crash risk, which still holds after addressing endogeneity problem, and varies across firms’ ownership and asset irreversibility. We extend extant research on impacts of EPU by capturing time-varying firms’ exposures to EPU.

A Text Classification Methodology to Assist a Large Technical Support System

Text-based tools for reporting technical issues and receiving support are widespread in commercial applications, such as customer services and internal corporate communication. Past issues recorded in such systems may provide valuable knowledge for better handling future interactions. Nevertheless, the predominance of short messages and the presence of specific domain subjects constitute additional challenges. In this work, we aim to build an assistant for a system operating in a large company that provides asynchronous services for technical support. It is known that some repetitive technical issues can be handled with simple standard messages, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">templates</i> . Thus, we propose a modular pipeline based on natural language processing and machine learning algorithms to enable raw text processing, feature extraction, and supervised learning to recommend suitable templates from a given textual description of the incoming issue. In a real-world scenario, the proposed pipeline achieved an average accuracy of 72.7%, a promising result for a setup with 9 classes and few labeled training instances. Moreover, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">post hoc</i> analysis shows how our methodology is able to correctly identify the words more closely related to the corresponding templates.

From cognitive overload to digital detox: Psychological implications of telework during the COVID-19 pandemic

For most people, telework during the COVID-19 pandemic necessitates the increased use of digital tools. Although working from home can enhance flexibility, it comes with various psychological challenges, all of which can be substantially exacerbated for people during the COVID-19 pandemic. The increased need to use digital tools can create cognitive overload that may negatively impact work productivity and well-being. The idea of digital detox has received increasing attention in the last few years as a means for recovering from stress caused by the use of digital media. This paper presents an analysis of the relationships between the use of digital work tools, the feeling of cognitive overload, digital detox measures, perceived work performance, and well-being. Results from an online survey (N = 403) conducted during the period of strict lockdown measures in Germany in April and May 2020 indicate that the relationship between the use of text-based tools and well-being, but not perceived job performance, is mediated by cognitive overload. These relationships were not found for the use of videoconferencing tools. However, for users of these tools, the number of digital detox measures moderates the relationship between cognitive overload and the perception of work demands.

Effects of Computer-Mediated Communication (CMC) Peer Review in an EFL Writing Course

This study aims to investigate the effects of the two-step blended computer mediated communication (CMC) peer review process (Word commenting followed by Zoom discussion) in an English writing course for 29 Chinese EFL learners, and their perceptions of this mode. Compared with previous studies, the findings of this study are encouraging: 1) the proportions of revision-oriented comments students gave reached at a high level of above 85% of the total comments; 2) students gave more local comments, but the ratio of revision-oriented comments in global areas to revision-oriented comments in local areas was more balanced (approximately 40% vs 60%); 3) the adoption rates of revision-oriented comments in text revision were also at a high level (63%-73%). What&amp;rsquo;s more, most students held positive attitudes toward this mode, perceiving it useful in their text revision and development of writing ability. 65% of them expressed their willingness to attend this mode of review activities in the future. This study reveals that the two-step CMC peer review process with Word commenting followed by discussion via online video conferencing system can be used as a useful tool in EFL writing class. This study contributes to the current research on CMC peer review since most previous studies investigated effects of peer review using text-based CMC tools and little research has been done on speech-based tools.

Using virtual reality to facilitate communication in the AEC domain: a systematic review

PurposeThe architecture, engineering and construction (AEC) industry exists in a dynamic environment and requires several stakeholders to communicate regularly. However, evidence indicates current communication practices fail to meet the requirements of increasingly complex projects. With the advent of Industry 4.0, a trend is noted to create a digital communication environment between stakeholders. Identified as a central technology in Industry 4.0, virtual reality (VR) has the potential to supplement current communication and facilitate the digitization of the AEC industry. This paper aims to explore how VR has been applied and future research directions for communication purpose.Design/methodology/approachThis research follows a systematic literature assessment methodology to summarize the results of 41 research articles in the last 15 years and outlines the applications of VR in facilitating communication in the AEC domain.FindingsRelevant VR applications are mainly found in building inspection, facility management, safety training, construction education and design and review. Communication tools and affordance are provided or built in several forms: text-based tools, voice chat tool, visual sharing affordance and avatars. Objective and subjective communication assessments are observed from those publications.Originality/valueThis review contributes to identifying the recent employment areas and future research directions of VR to facilitate communication in the AEC domain. The outcome can be a practical resource to guide both industry professionals and researchers to recognize the potentials of VR and will ultimately facilitate the creation of digital construction environments.

Mapping research in student engagement and educational technology in higher education: a systematic evidence map

Digital technology has become a central aspect of higher education, inherently affecting all aspects of the student experience. It has also been linked to an increase in behavioural, affective and cognitive student engagement, the facilitation of which is a central concern of educators. In order to delineate the complex nexus of technology and student engagement, this article systematically maps research from 243 studies published between 2007 and 2016. Research within the corpus was predominantly undertaken within the United States and the United Kingdom, with only limited research undertaken in the Global South, and largely focused on the fields of Arts & Humanities, Education, and Natural Sciences, Mathematics & Statistics. Studies most often used quantitative methods, followed by mixed methods, with little qualitative research methods employed. Few studies provided a definition of student engagement, and less than half were guided by a theoretical framework. The courses investigated used blended learning and text-based tools (e.g. discussion forums) most often, with undergraduate students as the primary target group. Stemming from the use of educational technology, behavioural engagement was by far the most often identified dimension, followed by affective and cognitive engagement. This mapping article provides the grounds for further exploration into discipline-specific use of technology to foster student engagement.

On the Effects of Automatic Transcription and Segmentation Errors in Hungarian Spoken Language Processing

Emerging Artificial Intelligence (AI) technology has brought machines to reach an equal or even superior level compared to human capabilities in several fields; nevertheless, among many other fields, making a computer able to understand human language still remains a challenge. When dealing with speech understanding, Automatic Speech Recognition (ASR) is used to generate transcripts, which are processed with text-based tools targeting Spoken Language Understanding (SLU). Depending on the ASR quality (which further depends on speech quality, the complexity of the topic, environment etc.), transcripts contain errors, which propagate further into the processing pipeline. Subjective tests show on the other hand, that humans understand quite well ASR-closed captions, despite the word and punctuation errors. Through word embedding based semantic parsing, the present paper is interested in quantifying the semantic bias introduced by ASR error propagation. As a special use case, speech summarization is also evaluated with regard to ASR error propagation. We show, that despite the higher word error rates seen with the highly inflectional Hungarian, the semantic space suffers least impact than the difference in Word Error Rate would suggest.

VISUALIZATION OF NETWORK TRAFFIC

Network security in today’s world is critical. System administrators must quickly perceive the security state of their networks, but they often have only text-based tools to work with. These tools often provide no overview that would help users grasp the big-picture. Network traffic visualization tools have successfully enabled security analysts to understand the nature of traffic present in a network. However these tools rely mainly on human expertise to discover anomalies in traffic and attack patterns. The visualization capability provided by Visualization tool allows an operator to assess the state of a large and complex network given an overall view of the entire network and filter/drill-down features with a friendly user interface that allows users to request more detailed information of interest such as specific protocol traffic flows. Visualization tool allows operators to detect and investigate anomalous internal and external network traffic. Visualization tool shows network events graphically. We model the network as a graph with hosts being nodes and traffic being flows on edges. We present a detailed description of Visualization tool functionality and demonstrate its application to traffic dynamics in order to monitor, discover, and investigate security-relevant events.

Tools for making impossible changes – experiences with a tool for transforming large Smalltalk programs

In a commercial system, performing large, invasive changes, such as changing an entire infrastructure layer, is often impossible because development on a system cannot be halted for any significant amount of time. Performing such changes require tools that can perform mass transformations on the source code. One approach is to use tools that can do global search and replace on the entire program structure. However, simple text-based tools are too simplistic for program text. To make accurate changes in a large program the tool must be based on parsing the program text. The paper describes the requirements for such a tool, the shortcomings of this approach, and describes one such tool for the Smalltalk language. This tool, the Refactoring Browser Rewrite Engine, is based on pattern matching of parse trees. The paper then presents three case studies in the successful application of this tool in commercial settings and discusses the shortcomings of such an approach.“impossible adj. - 1 b felt to be incapable of being done, attained, or fulfiled : insuperably difficult. - Merriam-Webster Dictionary”

Making distance learning E.R.O.T.I.C.: applying interpretation principles to distance learning

Distance learners are self-directed learners traditionally taught via study books, collections of readings, and exercises to test understanding of learning packages. Despite advances in e-Learning environments and computer-based teaching interfaces, distance learners still lack opportunities to participate in exercises and debates available to classroom learners, particularly through non-text based learning techniques. Effective distance teaching requires flexible learning opportunities. Using arguments developed in interpretation literature, we argue that effective distance learning must also be Entertaining, Relevant, Organised, Thematic, Involving and Creative—E.R.O.T.I.C. (after Ham, 1992). We discuss an experiment undertaken with distance learners at The University of Queensland Gatton Campus, where we initiated an E.R.O.T.I.C. external teaching package aimed at engaging distance learners but using multimedia, including but not limited to text-based learning tools. Student responses to non-text media were positive.

The documentary structure of source code

Many tools designed to help programmers view and manipulate source code exploit the formal structure of the programming language. Language-based tools use information derived via linguistic analysis to offer services that are impractical for purely text-based tools. In order to be effective, however, language-based tools must be designed to account properly for the documentary structure of source code: a structure that is largely orthogonal to the linguistic but no less important. Documentary structure includes, in addition to the language text, all extra-lingual information added by programmers for the sole purpose of aiding the human reader: comments, white space, and choice of names. Largely ignored in the research literature, documentary structure occupies a central role in the practice of programming. An examination of the documentary structure of programs leads to a better understanding of requirements for tool architectures.

Evaluating Tandem Language Learning in the MOO: Discourse Repair Strategies in a Bilingual Internet Project

Over the past years, the Internet has developed communication tools as well as information resources. Text-based tools have been established as valuable modular environments for education. One of them, the MOO virtual environment, has maintained its appeal, not least through initiatives such as the CALLMOO project, which developed an educational database with a Java-based interface and undertook systematic research on educational uses of MOOs (Aarseth & Jopp, 1998). In this paper, we will give a short overview of the concepts of learners autonomy and tandem learning. We will then look at repair strategies as represented in two types of data, questionnaires and transcripts, from a bilateral tandem MOO project between Information and Communication Technology (ICT) students from Trinity College Dublin and the Fachhochschule Rhein-Sieg near St. Augustin, Germany. By triangulating the data, we will show how repair strategies, in particular translation and paraphrase are distributed, and how learners’ intentions of their use (as evidenced by the questionnaire) and their actual realisations (in transcripts) work towards regulating native speaker/non-native speaker discourse, thereby supporting students in becoming more autonomous.

Technology status evaluation report: Computerized endoscopic medical record systemsNovember 1999

Technology status evaluation report: Computerized endoscopic medical record systemsNovember 1999

What's the Story

What's the Story

STORYBOARD-BASED PROGRAMMING TOOLS

Despite the progress in graphical capabilities of hardware and software systems to date, programmers do their jobs with little to no graphic support. This absence of graphic support is particularly costly in the process of debugging. The process of debugging is arduous and expensive, and text-based tools are limited in that they can only provide a one-dimensional view of program execution. The crucial factor in the debugging process is the programmer's ability to visualize how the events in the program unfold over time.

Identifying syntactic differences between two programs

AbstractProgrammers frequently face the need to identify the differences between two programs, usually two different versions of a program. Text‐based tools such as the UNIXr̀ utility diff often produce unsatisfactory comparisons because they cannot accurately pinpoint the differences and because they sometimes produce irrelevant differences. Since programs have a rigid syntactic structure as described by the grammar of the programming language in which they are written, we develop a comparison algorithm that exploits knowledge of the grammar. The algorithm, which is based on a dynamic programming scheme, can point out the differences between two programs more accurately than previous text comparison tools. Finally, the two programs are pretty‐printed ‘synchronously’ with the differences highlighted so that the differences are easily identified.