KNNHI: Resilient KNN algorithm for heterogeneous incomplete data classification and K identification using rough set theory

The original K-nearest neighbour ( KNN) algorithm was meant to classify homogeneous complete data, that is, data with only numerical features whose values exist completely. Thus, it faces problems when used with heterogeneous incomplete (HI) data, which has also categorical features and is plagued with missing values. Many solutions have been proposed over the years but most have pitfalls. For example, some solve heterogeneity by converting categorical features into numerical ones, inflicting structural damage. Others solve incompleteness by imputation or elimination, causing semantic disturbance. Almost all use the same K for all query objects, leading to misclassification. In the present work, we introduce KNNHI, a KNN-based algorithm for HI data classification that avoids all these pitfalls. Leveraging rough set theory, KNNHI preserves both categorical and numerical features, leaves missing values untouched and uses a different K for each query. The end result is an accurate classifier, as demonstrated by extensive experimentation on nine datasets mostly from the University of California Irvine repository, using a 10-fold cross-validation technique. We show that KNNHI outperforms six recently published KNN-based algorithms, in terms of precision, recall, accuracy and F-Score. In addition to its function as a mighty classifier, KNNHI can also serve as a K calculator, helping KNN-based algorithms that use a single K value for all queries that find the best such value. Sure enough, we show how four such algorithms improve their performance using the K obtained by KNNHI. Finally, KNNHI exhibits impressive resilience to the degree of incompleteness, degree of heterogeneity and the metric used to measure distance.

Essential elements, conceptual foundations and workflow design in crowd-powered projects

Crowdsourcing arose as a problem-solving strategy that uses a large number of workers to achieve tasks and solve specific problems. Although there are many studies that explore crowdsourcing platforms and systems, little attention has been paid to define what a crowd-powered project is. To address this issue, this article introduces a general-purpose conceptual model that represents the essential elements involved in this kind of project and how they relate to each other. We consider that the workflow in crowdsourcing projects is context-oriented and should represent the planning and coordination by the crowdsourcer in the project, instead of only facilitating decomposing a complex task into subtask sets. Since structural models are limited to cannot properly represent the execution flow, we also introduce the use of behavioural conceptual models, specifically Unified Modeling Language (UML) activity diagrams, to represent the user, tasks, assets, control activities and products involved in a specific project.

Knowledge management activities: Conceptual foundations and research issues

Knowledge is a broad concept whose epistemological construct has been debated since the days of the early Greek philosophers. Knowledge was discussed extensively during the Renaissance, became a central area of study during the Scientific Revolution and was applied extensively within organisations throughout the Industrial Revolution. Knowledge became an organisational resource of significant interest, emerging over the past 25 years as a unique field of study called knowledge management (KM). Much of the KM literature addresses matters of practice and application; what is missing is a deep and conceptual analysis of the activities that drive KM processes. This article provides a conceptualisation of KM activities focusing on the underlying foundations of these activities. The result is a rich framework of KM activities that can be used to pursue important research areas involved in studying KM processes, including theory development, areas of overlap and where further research is needed.

The relationship between sentiment score and COVID-19 cases in the United States

The coronavirus disease (COVID-19) continues to have devastating effects across the globe. No nation has been free from the uncertainty brought by this pandemic. The health, social and economic tolls associated with it are causing strong emotions and spreading fear in people of all ages, genders and races. Since the beginning of the COVID-19 pandemic, many have expressed their feelings and opinions related to a wide range of aspects of their lives via Twitter. In this study, we consider a framework for extracting sentiment scores and opinions from COVID-19–related tweets. We connect users’ sentiment with COVID-19 cases across the United States and investigate the effect of specific COVID-19 milestones on public sentiment. The results of this work may help with the development of pandemic-related legislation, serve as a guide for scientific work, as well as inform and educate the public on core issues related to the pandemic.

Social capital or non-human sources? A cross-context study on information source selection of migrant farmer workers

Insufficient examination of social factors obscures the reason why non-human information sources are under-utilised by social groups with lower information literacy. This study explores the mechanism of information source selection (ISS) of Chinese migrant farmer workers (MFWs) in different industries by conducting a cross-context analysis. After iterative analyses of multiple cases, a theoretical model of information source selection within an individual’s information world is constructed. It explains why MFWs make more use of social capitals than non-human information sources in information seeking. Besides, the information needs are examined form both the needed information and the need itself. A classification of social capital as human information source is created and the roles that social capitals and non-human information sources play in ISS are identified. This study provides novel theoretical insights into the ‘old’ issue of ISS, and thus has practical implications for public information service providers and MFW-related policy makers.

Article promotion on Twitter and Facebook: A case study of Cell journal

Social media has become an increasingly important channel of scholarly communication, especially for promoting the latest research outputs, so its role in facilitating access to academic texts is worth exploring. Based on 324 posts containing scholarly articles shared by journal Cell on Twitter and Facebook, this study compared the user engagement performance of articles posted on both platforms and examined the effect of such social media promotion and user engagement on article visiting. The user engagement performance of the articles was measured by retweets, shares, reactions, and likes, while click data tracked through bitly.com were used to indicate article visits. Statistical analysis, correlation analysis, and regression analysis were applied to explore and understand these data. For Cell, Facebook posts have a more significant influence than similar tweets in terms of volume. The user engagement on Facebook is 2.5~4 times as much as on Twitter. Moreover, the click metric of short links shows that Cell’s posts on Facebook directed twice as many visitors to the papers as posts on Twitter. However, the efficiency of the two platforms is approximate when the difference in the volume of followers is eliminated. The correlation and regression analysis suggested that user engagement positively affects the visiting of Cell’s papers. Both reactions and shares would affect the clicks of the short links to paper text. The results shed light on the implications of sharing scholarly articles on social media platforms for the promotion of article visits.

Connecting information literacy and social capital to better utilise knowledge resources in the workplace

Human resources and intellectual capital are best utilised through an ongoing interaction between individual and social processes. Still there is a research gap of empirical multilevel studies, focusing both on individual and organisational aspects of knowledge processes. To fill this gap, this article reports on a quantitative study, where the relationship between information literacy and social capital, representing the individual and social contexts affecting organisational knowledge processes, is explored. Structural equation modelling-based analysis of 378 employees working in different companies in Finland demonstrated that information literacy supports all three dimensions of social capital at workplace. Strong information handling skills enable better access to knowledge beyond the resources of an individual, that is, social capital. The results of the study contribute to a better understanding of how to manage human resources and the information and knowledge processes that employees are expected to be involved in.

Characterising users’ task completion process in learning-related tasks:A search pace model

This study investigated users’ searching, reading and writing interactions and their activity transitions during task completion process when users were collecting information for learning-related search tasks. Task completion process was defined as the process users started to search till the time when they have collected enough information to accomplish the search tasks. The data analysis was conducted from a new process perspective through synthesising macro- and micro-process levels. Four evenly distributed stages were divided according to the total task completion time in each search session. Our results demonstrated that users generally experienced three sub-processes during task completion process: exploration, accumulation and composition/reporting. Exploration sub-process is basically the first quarter of the total task completion time, during which users often issue more queries and visit more search engine result pages (SERPs) to collect information, and the dominant activity transition is switching between searching and reading; accumulation sub-process is mainly the second and third quarters of the total task completion time, during which they visit more unique content pages, have more revisits per content page, and they switch between reading and writing activities frequently; the last stage is composition/reporting sub-process, which is dominated by writing, and users often switch between writing and reading, and between writing and searching. Based on these findings, we propose a search pace model to describe how users proceed from the beginning to the end of task completion process in these three sub-processes. The methodology applied has been proved to be effective to examine users’ interaction behaviours from the process perspective on both the micro- and macro-levels. The findings of this article help us understand how users proceed their dynamic searching, reading and writing behaviours for learning-related tasks, and also have implications for the design of search systems that support learning-related tasks.

Social capital and individual motivations for information sharing: A theory of reasoned action perspective

Based on the theory of reasoned action, this study examined the impact of social capital and individual motivations on information sharing in the context of higher education. The research conducted an online survey of 277 academic technicians in five academic institutions in public university in Iraq. The model was developed using the structural equation modelling technique with AMOS v.27 and conditional hypotheses were tested. The findings suggest that social connection, trust, reciprocity, shared language, vision and a positive attitude towards assisting others influence technicians’ willingness to share information. It is also shown that attitude and subjective norms significantly affect information-sharing intentions. The results provide insights into understanding the social capital processes and individual motivations that contribute to information sharing among academic technicians in developing countries, particularly Iraq. Therefore, lab managers can implement practical plans to support these factors.

Reuse of the transparency-related information posted on Spanish library and archive websites

This study addresses the types of formats and ease of reuse of transparency-related information available on the websites of 53 national public libraries and 53 provincial historic archives. Further to Spain’s Transparency Act, reuse of public sector information is one of the elements comprising the right of access to information. Access and use must consequently be ensured to enable citizens and businesses to reuse all available data. The working methodology deployed here consisted in searching for, identifying and analysing the transparency-related documents carried on library and archive websites and the legal warnings governing their reuse. The findings revealed a wide variety of formats and rules governing reuse and indications of scant interest in these institutions in fostering the transparency and reuse of public information. Even when available, reusable information was normally found to be posted either separately from the data furnished by libraries and archives directly or positioned on pages or sections with complex access paths.