9,703 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
A Privacy Calculus Perspective
Sandhu, R. K., Vasconcelos-Gomes, J., Thomas, M. A., & Oliveira, T. (2023). Unfolding the Popularity of Video Conferencing Apps: A Privacy Calculus Perspective. International Journal Of Information Management, 68(February), 1-17. [102569]. https://doi.org/10.1016/j.ijinfomgt.2022.102569. Funding: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC).Videoconferencing (VC) applications (apps) have surged in popularity as an alternative to face-to-face communications especially during the COVID-19 pandemic. Although VC apps offer myriad benefits, it has caught much media attention owing to concerns of privacy infringements. This study examines the key determinants of working professional’s intentions to use VC apps in the backdrop of this conflicting duality. A conceptual research model is proposed that is based on theoretical foundations of privacy calculus and extended with conceptualizations of mobile users’ information privacy concerns (MUIPC), trust, technicality, ubiquity, as well as theoretical underpinnings of social presence theory. Structural equation modelling (SEM) is used to empirically test the model using data collected from 487 working professionals. For researchers, the study offers insights on the extent to which social richness and technological capabilities afforded by the virtual environment serve as predictors of the continuance intentions of using VC apps. Researchers may also find the model applicable to other studies of surveillance-based technologies. For practitioners, key recommendations pivotal to the design and development mobile video-conferencing apps are presented to ensure higher acceptance and continued usage of VC apps in professional settings.preprintauthorsversionepub_ahead_of_prin
Conversations on Empathy
In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
LabelVizier: Interactive Validation and Relabeling for Technical Text Annotations
With the rapid accumulation of text data produced by data-driven techniques,
the task of extracting "data annotations"--concise, high-quality data summaries
from unstructured raw text--has become increasingly important. The recent
advances in weak supervision and crowd-sourcing techniques provide promising
solutions to efficiently create annotations (labels) for large-scale technical
text data. However, such annotations may fail in practice because of the change
in annotation requirements, application scenarios, and modeling goals, where
label validation and relabeling by domain experts are required. To approach
this issue, we present LabelVizier, a human-in-the-loop workflow that
incorporates domain knowledge and user-specific requirements to reveal
actionable insights into annotation flaws, then produce better-quality labels
for large-scale multi-label datasets. We implement our workflow as an
interactive notebook to facilitate flexible error profiling, in-depth
annotation validation for three error types, and efficient annotation
relabeling on different data scales. We evaluated the efficiency and
generalizability of our workflow with two use cases and four expert reviews.
The results indicate that LabelVizier is applicable in various application
scenarios and assist domain experts with different knowledge backgrounds to
efficiently improve technical text annotation quality.Comment: 10 pages, 5 figure
Moving from a Sales Led to a Product Led Business: Evaluation and value delivery in SaaS products self-service
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and AnalyticsSaaS companies are transforming their traditional sales processes by taking advantage of their products as the main vehicle to acquire, activate, and retain customers. We focused on the SaaS software evaluation process and value delivery to examine how SaaS products that can be evaluated in self-service, by the users, deliver value along the customer journey. For this, we conducted qualitative research through in-depth interviews with senior executives from companies in different growth stages and geographies and observations to explore the strategies and organizational initiatives to seize the opportunities associated with product-led business models. Our findings evidence two main categories - evaluation and value. Evaluations start top-down, driven by a clear strategic direction from the management team or to address a pressing need that is hindering the business from moving forward, or bottom-up, started by the users with a clear use case, and connected to an urgent, often daily, need. Value, in the product-led model, is now delivered sooner on the customer journey creating a shift to the left in value delivered, now closer to the start of an evaluation, and value captured is going right, now after value is delivered and the product is started to be adopted. A discussion on how sales-led and product-led evaluation and value delivery, across the customer journey, differ is presented. Finally, we offer recommendations to business leaders wanting to move to product-led growth
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Low- and high-resource opinion summarization
Customer reviews play a vital role in the online purchasing decisions we make. The reviews
express user opinions that are useful for setting realistic expectations and uncovering important
details about products. However, some products receive hundreds or even thousands of
reviews, making them time-consuming to read. Moreover, many reviews contain uninformative
content, such as irrelevant personal experiences. Automatic summarization offers an
alternative – short text summaries capturing the essential information expressed in reviews.
Automatically produced summaries can reflect overall or particular opinions and be tailored to
user preferences. Besides being presented on major e-commerce platforms, home assistants
can also vocalize them. This approach can improve user satisfaction by assisting in making
faster and better decisions.
Modern summarization approaches are based on neural networks, often requiring thousands of
annotated samples for training. However, human-written summaries for products are expensive
to produce because annotators need to read many reviews. This has led to annotated data
scarcity where only a few datasets are available. Data scarcity is the central theme of our
works, and we propose a number of approaches to alleviate the problem. The thesis consists
of two parts where we discuss low- and high-resource data settings.
In the first part, we propose self-supervised learning methods applied to customer reviews
and few-shot methods for learning from small annotated datasets. Customer reviews without
summaries are available in large quantities, contain a breadth of in-domain specifics, and
provide a powerful training signal. We show that reviews can be used for learning summarizers
via a self-supervised objective. Further, we address two main challenges associated with
learning from small annotated datasets. First, large models rapidly overfit on small datasets
leading to poor generalization. Second, it is not possible to learn a wide range of in-domain
specifics (e.g., product aspects and usage) from a handful of gold samples. This leads to
subtle semantic mistakes in generated summaries, such as ‘great dead on arrival battery.’ We
address the first challenge by explicitly modeling summary properties (e.g., content coverage
and sentiment alignment). Furthermore, we leverage small modules – adapters – that are
more robust to overfitting. As we show, despite their size, these modules can be used to
store in-domain knowledge to reduce semantic mistakes. Lastly, we propose a simple method
for learning personalized summarizers based on aspects, such as ‘price,’ ‘battery life,’ and
‘resolution.’ This task is harder to learn, and we present a few-shot method for training a
query-based summarizer on small annotated datasets.
In the second part, we focus on the high-resource setting and present a large dataset with
summaries collected from various online resources. The dataset has more than 33,000 humanwritten
summaries, where each is linked up to thousands of reviews. This, however, makes it
challenging to apply an ‘expensive’ deep encoder due to memory and computational costs. To
address this problem, we propose selecting small subsets of informative reviews. Only these
subsets are encoded by the deep encoder and subsequently summarized. We show that the
selector and summarizer can be trained end-to-end via amortized inference and policy gradient
methods
Digital Innovations for a Circular Plastic Economy in Africa
Plastic pollution is one of the biggest challenges of the twenty-first century that requires innovative and varied solutions. Focusing on sub-Saharan Africa, this book brings together interdisciplinary, multi-sectoral and multi-stakeholder perspectives exploring challenges and opportunities for utilising digital innovations to manage and accelerate the transition to a circular plastic economy (CPE).
This book is organised into three sections bringing together discussion of environmental conditions, operational dimensions and country case studies of digital transformation towards the circular plastic economy. It explores the environment for digitisation in the circular economy, bringing together perspectives from practitioners in academia, innovation, policy, civil society and government agencies. The book also highlights specific country case studies in relation to the development and implementation of different innovative ideas to drive the circular plastic economy across the three sub-Saharan African regions. Finally, the book interrogates the policy dimensions and practitioner perspectives towards a digitally enabled circular plastic economy.
Written for a wide range of readers across academia, policy and practice, including researchers, students, small and medium enterprises (SMEs), digital entrepreneurs, non-governmental organisations (NGOs) and multilateral agencies, policymakers and public officials, this book offers unique insights into complex, multilayered issues relating to the production and management of plastic waste and highlights how digital innovations can drive the transition to the circular plastic economy in Africa.
The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license
Monitoring of the life quality of population in Europe and Ukraine in the war conditions
This scientific work is dedicated to highlighting important economic issues related to the quality of life of the population in Europe. The results of this work were obtained through the prism of the research of scientific approaches to the analysis of the quality of life, which made it possible to analyze the indices’ values of the quality of life of the population in the cities and countries of Eastern Europe, as well as Ukraine today, when it is suffering from full-scale Russian military aggression and is fighting for its survival. The authors highlighted three planes of the general architecture of the concept based on social quality of life of the population in Ukraine and visualized the scientific concept of social quality of life with a European orientation
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