41,453 research outputs found
ERP implementation methodologies and frameworks: a literature review
Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history
Do GANs leave artificial fingerprints?
In the last few years, generative adversarial networks (GAN) have shown
tremendous potential for a number of applications in computer vision and
related fields. With the current pace of progress, it is a sure bet they will
soon be able to generate high-quality images and videos, virtually
indistinguishable from real ones. Unfortunately, realistic GAN-generated images
pose serious threats to security, to begin with a possible flood of fake
multimedia, and multimedia forensic countermeasures are in urgent need. In this
work, we show that each GAN leaves its specific fingerprint in the images it
generates, just like real-world cameras mark acquired images with traces of
their photo-response non-uniformity pattern. Source identification experiments
with several popular GANs show such fingerprints to represent a precious asset
for forensic analyses
The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences
Current smartphone operating systems regulate application permissions by
prompting users on an ask-on-first-use basis. Prior research has shown that
this method is ineffective because it fails to account for context: the
circumstances under which an application first requests access to data may be
vastly different than the circumstances under which it subsequently requests
access. We performed a longitudinal 131-person field study to analyze the
contextuality behind user privacy decisions to regulate access to sensitive
resources. We built a classifier to make privacy decisions on the user's behalf
by detecting when context has changed and, when necessary, inferring privacy
preferences based on the user's past decisions and behavior. Our goal is to
automatically grant appropriate resource requests without further user
intervention, deny inappropriate requests, and only prompt the user when the
system is uncertain of the user's preferences. We show that our approach can
accurately predict users' privacy decisions 96.8% of the time, which is a
four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure
Guide to using Evidence in Higher Education
This Guide to Using Evidence has been designed to, to support and encourage students and students’ association and union staff to actively engage with data and evidence. It offers an accessible introduction to a range of key ideas and concepts and a range of activities which allow readers to develop their own thinking and confidence in key areas.
The ambition of its authors, QAA Scotland and the students who reviewed early drafts, is that students and students’ association and union staff will reach for this resource as they prepare for committees, devise new campaigns, deliver services, and do all of the other things they do to enhance students’ experiences and outcomes. Underpinning all of this is a belief that students themselves, the institutions they are working with, and the sector as a whole, are better served when students are, and are seen to be, agents in the ‘data landscape’, not just subjects of it. Engaging with this Guide will help students and students’ association and union staff to develop that sense of agency in themselves and foster it in others.
This Guide is a product of a student-led project coordinated by QAA Scotland as part of the Evidence for Enhancement Theme (2017-20)
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning
Federated learning is a distributed machine learning system that uses
participants' data to train an improved global model. In federated learning,
participants cooperatively train a global model, and they will receive the
global model and payments. Rational participants try to maximize their
individual utility, and they will not input their high-quality data truthfully
unless they are provided with satisfactory payments based on their data
quality. Furthermore, federated learning benefits from the cooperative
contributions of participants. Accordingly, how to establish an incentive
mechanism that both incentivizes inputting data truthfully and promotes stable
cooperation has become an important issue to consider. In this paper, we
introduce a data sharing game model for federated learning and employ
game-theoretic approaches to design a core-selecting incentive mechanism by
utilizing a popular concept in cooperative games, the core. In federated
learning, the core can be empty, resulting in the core-selecting mechanism
becoming infeasible. To address this, our core-selecting mechanism employs a
relaxation method and simultaneously minimizes the benefits of inputting false
data for all participants. However, this mechanism is computationally expensive
because it requires aggregating exponential models for all possible coalitions,
which is infeasible in federated learning. To address this, we propose an
efficient core-selecting mechanism based on sampling approximation that only
aggregates models on sampled coalitions to approximate the exact result.
Extensive experiments verify that the efficient core-selecting mechanism can
incentivize inputting high-quality data and stable cooperation, while it
reduces computational overhead compared to the core-selecting mechanism
Supporting Regularized Logistic Regression Privately and Efficiently
As one of the most popular statistical and machine learning models, logistic
regression with regularization has found wide adoption in biomedicine, social
sciences, information technology, and so on. These domains often involve data
of human subjects that are contingent upon strict privacy regulations.
Increasing concerns over data privacy make it more and more difficult to
coordinate and conduct large-scale collaborative studies, which typically rely
on cross-institution data sharing and joint analysis. Our work here focuses on
safeguarding regularized logistic regression, a widely-used machine learning
model in various disciplines while at the same time has not been investigated
from a data security and privacy perspective. We consider a common use scenario
of multi-institution collaborative studies, such as in the form of research
consortia or networks as widely seen in genetics, epidemiology, social
sciences, etc. To make our privacy-enhancing solution practical, we demonstrate
a non-conventional and computationally efficient method leveraging distributing
computing and strong cryptography to provide comprehensive protection over
individual-level and summary data. Extensive empirical evaluation on several
studies validated the privacy guarantees, efficiency and scalability of our
proposal. We also discuss the practical implications of our solution for
large-scale studies and applications from various disciplines, including
genetic and biomedical studies, smart grid, network analysis, etc
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