59 research outputs found
COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News
Fake news has emerged as a critical global issue, magnified by the COVID-19
pandemic, underscoring the need for effective preventive tools. Leveraging
machine learning, including deep learning techniques, offers promise in
combatting fake news. This paper goes beyond by establishing BERT as the
superior model for fake news detection and demonstrates its utility as a tool
to empower the general populace. We have implemented a browser extension,
enhanced with explainability features, enabling real-time identification of
fake news and delivering easily interpretable explanations. To achieve this, we
have employed two publicly available datasets and created seven distinct data
configurations to evaluate three prominent machine learning architectures. Our
comprehensive experiments affirm BERT's exceptional accuracy in detecting
COVID-19-related fake news. Furthermore, we have integrated an explainability
component into the BERT model and deployed it as a service through Amazon's
cloud API hosting (AWS). We have developed a browser extension that interfaces
with the API, allowing users to select and transmit data from web pages,
receiving an intelligible classification in return. This paper presents a
practical end-to-end solution, highlighting the feasibility of constructing a
holistic system for fake news detection, which can significantly benefit
society.Comment: 7 pages, 4 figure
Identifying Recent Behavioral Data Length in Mobile Phone Log
Mobile phone log data (e.g., phone call log) is not static as it is
progressively added to day-by-day according to individ- ual's diverse behaviors
with mobile phones. Since human behavior changes over time, the most recent
pattern is more interesting and significant than older ones for predicting in-
dividual's behavior. The goal of this poster paper is to iden- tify the recent
behavioral data length dynamically from the entire phone log for recency-based
behavior modeling. To the best of our knowledge, this is the first dynamic
recent log-based study that takes into account individual's recent behavioral
patterns for modeling their phone call behaviors.Comment: 14th EAI International Conference on Mobile and Ubiquitous Systems:
Computing, Networking and Services (MobiQuitous 2017), Melbourne, Australi
LEI2JSON: Schema-based Validation and Conversion of Livestock Event Information
Livestock producers often need help in standardising (i.e., converting and
validating) their livestock event data. This article introduces a novel
solution, LEI2JSON (Livestock Event Information To JSON). The tool is an add-on
for Google Sheets, adhering to the livestock event information (LEI) schema.
The core objective of LEI2JSON is to provide livestock producers with an
efficient mechanism to standardise their data, leading to substantial savings
in time and resources. This is achieved by building the spreadsheet template
with the appropriate column headers, notes, and validation rules, converting
the spreadsheet data into JSON format, and validating the output against the
schema. LEI2JSON facilitates the seamless storage of livestock event
information locally or on Google Drive in JSON. Additionally, we have conducted
an extensive experimental evaluation to assess the effectiveness of the tool.Comment: 20 pages, 6 figure
LEI: Livestock Event Information Schema for Enabling Data Sharing
Data-driven advances have resulted in significant improvements in dairy
production. However, the meat industry has lagged behind in adopting
data-driven approaches, underscoring the crucial need for data standardisation
to facilitate seamless data transmission to maximise productivity, save costs,
and increase market access. To address this gap, we propose a novel data
schema, Livestock Event Information (LEI) schema, designed to accurately and
uniformly record livestock events. LEI complies with the International
Committee for Animal Recording (ICAR) and Integrity System Company (ISC)
schemas to deliver this data standardisation and enable data sharing between
producers and consumers. To validate the superiority of LEI, we conducted a
structural metrics analysis and a comprehensive case study. The analysis
demonstrated that LEI outperforms the ICAR and ISC schemas in terms of design,
while the case study confirmed its superior ability to capture livestock event
information. Our findings lay the foundation for the implementation of the LEI
schema, unlocking the potential for data-driven advances in livestock
management. Moreover, LEI's versatility opens avenues for future expansion into
other agricultural domains, encompassing poultry, fisheries, and crops. The
adoption of LEI promises substantial benefits, including improved data
accuracy, reduced costs, and increased productivity, heralding a new era of
sustainability in the meat industry.Comment: 63 pages, 7 figure
AI-Driven Personalised Offloading Device Prescriptions: A Cutting-Edge Approach to Preventing Diabetes-Related Plantar Forefoot Ulcers and Complications
Diabetes-related foot ulcers and complications are a significant concern for
individuals with diabetes, leading to severe health implications such as
lower-limb amputation and reduced quality of life. This chapter discusses
applying AI-driven personalised offloading device prescriptions as an advanced
solution for preventing such conditions. By harnessing the capabilities of
artificial intelligence, this cutting-edge approach enables the prescription of
offloading devices tailored to each patient's specific requirements. This
includes the patient's preferences on offloading devices such as footwear and
foot orthotics and their adaptations that suit the patient's intention of use
and lifestyle. Through a series of studies, real-world data analysis and
machine learning algorithms, high-risk areas can be identified, facilitating
the recommendation of precise offloading strategies, including custom orthotic
insoles, shoe adaptations, or specialised footwear. By including
patient-specific factors to promote adherence, proactively addressing pressure
points and promoting optimal foot mechanics, these personalised offloading
devices have the potential to minimise the occurrence of foot ulcers and
associated complications. This chapter proposes an AI-powered Clinical Decision
Support System (CDSS) to recommend personalised prescriptions of offloading
devices (footwear and insoles) for patients with diabetes who are at risk of
foot complications. This innovative approach signifies a transformative leap in
diabetic foot care, offering promising opportunities for preventive healthcare
interventions.Comment: 33 pages, 2 figure
A Policy Framework for Subject-Driven Data Sharing
Organizations (e.g., hospitals, university etc.) are custodians of data on their clients and use this information to improve their service. Personal data of an individual therefore ends up hosted under the administration of different data custodians. Individuals (data subjects) may want to share their data with others for various reasons. However, existing data sharing mechanisms provided by the data custodians do not provide individuals enough flexibility to share their data, especially in a cross-domain (data custodian) environment. In this paper, we propose a data sharing policy language and related framework for a data subject to capture their fine-grained data sharing requirements. This proposed language allows the data subject to define data sharing policies that consider context conditions, privacy obligations and re-sharing restrictions. Furthermore, we have implemented a prototype to demonstrate how data subjects can define their data sharing policies and how the policies can be used and enforced at runtime
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