Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relative Value of Facebook Advertising Data for Poverty Mapping

Having reliable and up-to-date poverty data is a prerequisite for monitoring the United Nations Sustainable Development Goals (SDGs) and for planning effective poverty reduction interventions. Unfortunately, traditional data sources are often outdated or lacking appropriate disaggregation. As a remedy, satellite imagery has recently become prominent in obtaining geographically-fine-grained and up-to-date poverty estimates. Satellite data can pick up signals of economic activity by detecting light at night, it can pick up development status by detecting infrastructure such as roads, and it can pick up signals for individual household wealth by detecting different building footprints and roof types. It can, however, not look inside the households and pick up signals from individuals. On the other hand, alternative data sources such as audience estimates from Facebook's advertising platform provide insights into the devices and internet connection types used by individuals in different locations. Previous work has shown the value of such anonymous, publicly-accessible advertising data from Facebook for studying migration, gender gaps, crime rates, and health, among others. In this work, we evaluate the added value of using Facebook data over satellite data for mapping socioeconomic development in two low and middle income countries – the Philippines and India. We show that Facebook features perform roughly similar to satellite data in the Philippines with value added for urban locations. In India, however, where Facebook penetration is lower, satellite data perform better.
Time is of the Essence: Spatiotemporalities of Food Delivery Platform Work in China

Spatiotemporality has never been so prominent in the gig economy, where the mantra is to harness individual persons’ “spare time” across large geographical areas to create economic value with scale. The time-sensitive nature of food delivery service and the spatial distribution of a large number of delivery workers require intensive algorithmic coordination and control. In this paper, we report findings from a qualitative study conducted in Beijing with food-delivery platform workers and managers. By taking a critical, sociomaterial perspective, the study shows how the workers are tethered to and simultaneously co-construct multiple spatiotemporalities in their daily work to meet the demand of speed defined by platform algorithms. We further argue that the algorithms are not creating so-called “atomized” workers in the gig economy, but interconnected and co-dependent collective agencies in space and time.
Social Network Site User Archetypes: An Exploratory Study of Implications for Workplace Outcomes

The booming popularity of social network sites (SNS) is making its mark on individuals as well as organizations. Past literature indicates that there is increased interest in SNS user behavior. However, there is a dearth of peer-reviewed literature attempting to represent SNS user archetypes. This study extends previous research by creating SNS user archetypes based on hedonic and utilitarian usage behaviors and examining the implications for user outcomes. Data was collected from 210 employees of South African firms. Using k-means cluster analysis approach and ANOVA, SNS users we segmented into four (4) quadrants. Each quadrant indicates SNS usage behavior and its implication on job satisfaction and job performance. From a theoretical viewpoint, this study contributes to the discussion around the SNS usage behavior, user archetypes, and the implication for user outcomes. The archetypes can be used by other researchers and practitioners to further refine understanding of SNS usage behavior.
Sharing Information Security Failure: The Role of Social Context and Social Environment

Due to a massive rise in information security incidents caused by employees, organizations need to employ security measures through which their workforce can learn from their colleagues' mistakes. Sharing personal experience with security incidents may work as such a means. Employees, however, often hesitate to share their failure stories with confrères and supervisors. By drawing on the social information processing (SIP) perspective, this paper investigates the impact of social context and social environment on the sharing of information security failure. The results of an investigation with 241 respondents indicate which type of leader will be successful in making employees feel safe enough to disclose their incident experience and why age effects should be considered. Our findings are of high relevance for both theory and practice, as understanding employees' sharing behavior of social engineering failures can help enhance information security awareness campaigns and to alter security practices, preventing the same mistakes from happening again.
Gene flow contributes to genetic diversity of tetraploid populations of the North American plant genus Houstonia (Rubiaceae)

Abstract Polyploids are thought to harbor more genetic diversity than diploids, despite the genetic bottleneck they experience upon formation. Identifying the means by which polyploids become more genetically variable can advance our understanding of the evolutionary trajectories of polyploid lineages. Gene flow is one such mechanism that can contribute to polyploid genetic diversity. Here, we used microsatellite markers to measure genetic diversity and to estimate gene flow among closely related diploid and tetraploid species of the North American plant genus Houstonia section Amphiotis. We found that tetraploid populations were more genetically variable when compared to diploid populations and that gene flow (admixture) occurred more often among tetraploid populations than among diploids. In addition, we provide evidence to support previous reports that autopolyploidy is the likely mode of polyploid formation in this group. We suggest that gene flow among closely related species, particularly among tetraploid populations, has likely contributed to the genetic diversity levels of tetraploid lineages of Houstonia species.
Crowdsourcing Metadata for Audiovisual Cultural Heritage: Finnish Full-Length Films, 1946-1985

This paper is based on a crowdsourcing project which was realised at the School of History, Culture and Arts Studies of the University of Turku be-tween the years 2013–2018. The idea was to develop a format through which long-term crowdsourcing could be integrated into the humanities curriculum. The project was realised in close cooperation with the National Audiovisual Institute (KAVI) in Finland. The aim was to help KAVI in developing its open database for Finnish cinema, Elonet, by engaging both graduate and postgraduate students in producing keywords, genre characterisations, plot summaries and other relevant fields of information for Finnish cinema. In total, the project produced metadata for 572 full-length films, both fiction films and long docu-mentaries that had their theatre release between the years 1946 and 1985. The amount is substantial considering that, to date, around 1,600 full-length films have been released in Finland. At the same time, it produced a successful model for drawing on crowdsourcing in the classroom.
Decision Process for Blockchain Architectures based on Requirements

In recent years, blockchain has grown in popularity due to its singular attributes, enabling the development of new innovative decentralized applications. But when companies consider leveraging blockchain for their applications, the plethora of possible choices and the difficulty of integrating blockchain into architectures can hinder its adoption. Our research project aims to ease the adoption of blockchain into companies, notably with the construction of an automated decision process to solve this issue in which requirements are first-class citizens, a knowledge base containing architectural patterns and blockchains refined over time, and an architecture generator able to process outputs into architectural stubs. This paper will also present our current progression on this decision process, by introducing the preliminary version that is able to choose the most suitable blockchain between multiple choices and our process-driven benchmarking tool.
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In almost every industry, firms launch digital transformation (DT) initiatives. However, not all of these initiatives have been successful. Despite its growing popularity in both research and industry, the term DT is used in a fragmented manner and ambiguity exists concerning the organizational characteristics of DT initiatives. While both academic and industry-oriented outlets have published a wide array of articles on DT initiatives, they are not comprehensive and have been published to satisfy different purposes. We argue that building a taxonomy of DT initiatives based on empirical cases will yield multiple benefits, such as providing a uniform categorization scheme for DT case studies that can be used to structure future field research. For this purpose, we follow an adapted version of the common taxonomy construction methodology. As a first step toward this goal, we have conducted case studies of DT initiatives of companies from different industries. We then applied the taxonomy building methodology to derive a working taxonomy of DT initiatives based on comprehensive, in-depth analysis. With our research, we aim to investigate a variety of DT initiatives. The resulting taxonomy can be used by practitioners to assess and compare their own DT initiatives and by researchers as a conceptual basis for future research projects on DT.
Evolution of Mitochondrial DNA Variation within and among Yukon River Chum Salmon Populations

Abstract We surveyed seven regions of the mitochondrial DNA (mtDNA) genome for restriction site variation to investigate the evolution of genetic variability in chum salmon Oncorhynchus keta from the Yukon River. Chum salmon from other Alaska regions were also analyzed to provide context for mtDNA variation. No divergence was observed in mtDNA haplotype frequency distributions among Yukon River populations, but divergence was observed across Alaska. The haplotype genealogy identified two lineages, and nested clade analysis revealed significant relationships between the geographical distribution of haplotypes and their genealogy for a two-step clade (which predominates in the Yukon River), a three-step clade, and for the total cladogram. The demographic signal detected for the two-step clade was consistent with isolation by distance, whereas contiguous range expansion was identified for the three-step clade and the total cladogram. The absence of signals of historical fragmentation suggests that incomplete.
A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.
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Social media serve as an alternate information source for patients, who use them to share information and provide social support. The aim of this research was to enable the analysis of patients’ tweets, by building a classiﬁer of Twitter users that distinguishes patients from other entities. In the ﬁrst stage of the research, a machine learning method, combining both social network analysis and natural language processing, was used to automatically classify users as patients or not. Three types of features were considered: (1) the user’s behavior on Twitter, (2) the content of the user’s tweets, and (3) the social structure of the user’s network. While different classiﬁcation algorithms were considered, the best results (F1-score 0.808 and Precision 0.809) were achieved by a multiple-instance approach which constitute the novelty of this research. In the second stage of the research, the obtained classiﬁcation methods were used to collect tweets of patients, in which they describe the different lifestyle changes they endure in order to deal with their disease. Using IBM Watson Service for entity sentiment analysis, frequently mentioned lifestyles were identiﬁed and their effectiveness on patients’ wellbeing was examined.
Leveraging Social Media for Work Value: Social and Work Affordances

The reasons that people using social media is going beyond personal socializing. Many individuals leverage the social and work affordances of social media to enhance work performance and complete tasks. This study proposed a framework of social media enabled work value based on social psychological theories including social affordance and social presence. We explore how different affordances (social, work, and negative affordances) of social media affect the social value (social presence) and work value perceived by the users. The effects of social affordance, work affordance, and negative affordance were examined. The proposed model was empirically evaluated on a sample of 194 social media users. The results provide support to the themes (1) both social and work values are increased by social and work affordance; (2) negative affordance, however, would not affect the work value; (3) increasing social presence could not enhance the work value. Several suggestions are provided.
Facial Payment Use in China: An Integrated View of Privacy Concerns and Perceived Benefits

This paper presents a study design intended to investigate the privacy concerns and benefits related to the adoption of facial payment technology from a privacy calculus perspective. In the proposed research model, relative advantages, including convenience, availability, and security, are considered as perceived benefits in facial payment adoption and assumed to exert a positive influence on the adoption of facial payment. The privacy concern, involving threat appraisal (perceived severity and vulnerability) and coping appraisals (response efficacy and self-efficacy), are articulated as perceived risks. Threat appraisals negatively affect people’s intention to use facial payment technology, whereas coping appraisals positively influence their usage. Based on privacy calculus framework, the benefit-risk analysis shapes people’s adoption behavior of facial payment technology. In addition, personal innovativeness is set as moderators in the proposed model. This research might contribute to literature on privacy concerns and facial payment technology use, and offer practical implications for facial payment providers.
Estimation of Noise in the In Situ Hyperspectral Data Acquired by Chang'E-4 and Its Effects on Spectral Analysis of Regolith

The Chang’E-4 (CE-4) spacecraft landed successfully on the far side of the Moon on 3 January 2019, and the rover Yutu-2 has explored the lunar surface since then. The visible and near-infrared imaging spectrometer (VNIS) onboard the rover has acquired numerous spectra, providing unprecedented insight into the composition of the lunar surface. However, the noise in these spectral data and its effects on spectral interpretation are not yet assessed. Here we analyzed repeated measurements over the same area at the lunar surface to estimate the signal–noise ratio (SNR) of the VNIS spectra. Using the results, we assessed the effects of noise on the estimation of band centers, band depths, FeO content, optical maturity (OMAT), mineral abundances, and submicroscopic metallic iron (SMFe). The data observed at solar altitudes <20° exhibit low SNR (25 dB), whereas the data acquired at 20°–35° exhibit higher SNR (35–37 dB). We found differences in band centers due to noise to be ~6.2 and up to 28.6 nm for 1 and 2 μm absorption, respectively. We also found that mineral abundances derived using the Hapke model are affected by noise, with maximum standard deviations of 6.3%, 2.4%, and 7.0% for plagioclase, pyroxene, and olivine, respectively. Our results suggest that noise has significant impacts on the CE-4 spectra, which should be considered in the spectral analysis and geologic interpretation of lunar exploration data.
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While social desirability is a long-standing problem in information systems research and which is difficult to measure in questionnaires, we propose a more objective measure of social desirability based on electroencephalographic data. Using a novel machine learning approach analyzing specific fine-graded electroencephalographic sub-bands, we achieve an accuracy of over 75% on completely unseen evaluation data, which is a methodological landmark in measuring social desirability. Our results have theoretical, methodological and practical implications.
Does organizational mindfulness facilitate enterprise system adaptation and organizational agility?

Nowadays firms rely on enterprise systems (ES) to enable organizational agility. However, uncertain environments require firms continuously adapt their ES to meet changing business needs. What factor will drive ES adaptation remains an understudied issue. This study holds that firms need to foster organizational mindfulness to facilitate ES adaptation and organizational agility. Substantial literature has examined the effects of various IT-related capabilities on organizational agility. Organizational attributes, such as organizational mindfulness, are rarely studied. We build and test a model based on the perspectives of organizational mindfulness and boundary object. Based on 138 responses of Taiwanese manufacturing firms, our findings demonstrate the importance of organizational mindfulness in achieving higher ES adaptation and thereby organizational agility. Implications of the results for practices and academics are provided
Tracing Complexity in Food Blogging Entries

Within this paper, we focus on the concept of complexity and how it is represented in food blogging entries on Twitter. We turn specific attention to complexity capture when looking at healthy foods, focusing on food blogging entries that entail the notions of health/healthiness/healthy. We do so because we consider that complexity manifests hedonism - that is the irrational determinant of food choice above rational considerations of nutrition and healthiness. Using text as a platform for our analysis, we derive bigrams and topic models that illustrate the frequencies of words and bi-grams, thus, pointing our attention to current discourse in food blogging entries on Twitter. The results show that, contrary to complexity, that the dominating characteristics in healthy food domain are easiness and speed of preparation, however, rational and health related considerations may not always take precedence when the choice is determined. Food blogging entries show surprisingly little account of healthy food as being tasty and enjoyable. With this we aim to contribute to the knowledge of how to shape more healthy consumer behaviors. Having discovered the scarcity of hedonic connotations, this work invites for further research in text-based information about food.
Faithfulness in Practice: Towards an Expansion of Representation Theory

Representation Theory is concerned with how information systems can provide faithful representations of real-world phenomena. In this paper, we discuss the concept of a faithful representation in relation to contexts where the real-world phenomenon is complex. While current Representation Theory takes an internal view to representations, we argue for an expansion of the theory to include what we term the interpretive structure, to account for faithfulness in complex situations. Based on an observational study, we present an exemplary vignette from an emergency care unit in a Norwegian hospital. We abductively develop an extended view of Representation Theory by synthesizing it with a wider range of theories of information in tandem with our data analysis. We discuss how interaction between an information system’s deep structure and interpretive structure is necessary for an information system’s representations to be faithful to a complex real-world phenomenon. This faithfulness-in-practice perspective is thus an extension to Representation Theory and offers implications for how we conceptualize representations in Information Systems research.
On the Splitting Dynamics of Meetup Social Groups

Groups in online social networks witness continuous evolution by loss of existing members and gain of new members. In this paper, we present a study of group split in Meetup, where a major fraction of members leave the existing group together and join a newly formed group. We identify pivotal group members, called splitters, playing key roles in group split by influencing the existing members to leave the group. We provide an in-depth analysis of the empirical data to reveal key motivating factors leading to a group split and its subsequent impact. Finally, we develop a prediction model for early detection of splitters, as well as the group members likely to be influenced by the splitter to leave the group.
Comparing Word Frequencies and Lexical Diversity with the ZipfExplorer Tool

The ZipfExplorer is a tool for the interactive comparison and visualization of shared word type frequencies for two texts or corpora. The tool can be used to give insight into similarities and differences in textual and discourse content in terms of individual keywords or groups of keywords, and also calculates several measures of lexical diversity for the shared types of the selected texts. A selection of texts and corpora can be analyzed, and users can upload their own files for interactive comparison