214 research outputs found
A Philadelphia Story: Building Civic Capacity for School Reform in a Privatizing System
Following the 2001 state takeover of the School District of Philadelphia, a new governance structure was established and an ambitious set of reforms went into effect, generating renewed public confidence in the district. Despite this, maintaining reform momentum continues to be difficult in Philadelphia. This can be traced to on-going challenges to civic capacity around education. Defined by Stone et al (2001), civic capacity involves collaboration and mobilization of the city's civic and community sectors to pursue the collective good of educational improvement. Using interviews conducted with over 65 local civic actors and district administrators, and case studies of local organizations involved with education, the authors examine civic capacity in the context of Philadelphia. The authors find that while many individuals and organizations are actively involved with the schools, there are several factors that present unique challenges to the development of civic capacity in Philadelphia. Despite these challenges, the authors conclude that there are many reasons to be optimistic and offer several recommendations for generating civic capacity -- the kind that creates and sustains genuine educational change
Herb-Drug Interactions: A Holistic Decision Support System in Healthcare
Complementary and alternative medicine are commonly used concomitantly with
conventional medications leading to adverse drug reactions and even fatality in
some cases. Furthermore, the vast possibility of herb-drug interactions
prevents health professionals from remembering or manually searching them in a
database. Decision support systems are a powerful tool that can be used to
assist clinicians in making diagnostic and therapeutic decisions in patient
care. Therefore, an original and hybrid decision support system was designed to
identify herb-drug interactions, applying artificial intelligence techniques to
identify new possible interactions. Different machine learning models will be
used to strengthen the typical rules engine used in these cases. Thus, using
the proposed system, the pharmacy community, people's first line of contact
within the Healthcare System, will be able to make better and more accurate
therapeutic decisions and mitigate possible adverse events
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection
Machine Learning (ML) can be incredibly valuable to automate anomaly
detection and cyber-attack classification, improving the way that Network
Intrusion Detection (NID) is performed. However, despite the benefits of ML
models, they are highly susceptible to adversarial cyber-attack examples
specifically crafted to exploit them. A wide range of adversarial attacks have
been created and researchers have worked on various defense strategies to
safeguard ML models, but most were not intended for the specific constraints of
a communication network and its communication protocols, so they may lead to
unrealistic examples in the NID domain. This Systematization of Knowledge (SoK)
consolidates and summarizes the state-of-the-art adversarial learning
approaches that can generate realistic examples and could be used in real ML
development and deployment scenarios with real network traffic flows. This SoK
also describes the open challenges regarding the use of adversarial ML in the
NID domain, defines the fundamental properties that are required for an
adversarial example to be realistic, and provides guidelines for researchers to
ensure that their future experiments are adequate for a real communication
network.Comment: 31 pages, 3 tables, 6 figures, Computers and Security journa
Incomplete operational transition complexity of regular languages
The state complexity of basic operations on regular languages considering complete deterministic finite automata (DFA) has been extensively studied in the literature. But, if incomplete DFAs are considered, transition complexity is also a significant measure. In this paper we study the incomplete (deterministic) state and transition complexity of some operations for regular and finite languages. For regular languages we give a new tight upper bound for the transition complexity of the union, which refutes the conjecture presented by Y. Gao et al. For finite languages, we correct the published state complexity of concatenation for complete DFAs and provide a tight upper bound for the case when the right operand is larger than the left one. We also present some experimental results to test the behavior of those operations on the average case, and we conjecture that for many operations and in practical applications the worst-case complexity is seldom reached
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection
Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in real ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their future experiments are adequate for a real communication network.The present work was partially supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within project ”Cybers SeC IP” (NORTE-01-0145-FEDER000044). This work has also received funding from UIDB/00760/2020.info:eu-repo/semantics/acceptedVersio
TestLab: An Intelligent Automated Software Testing Framework
The prevalence of software systems has become an integral part of modern-day
living. Software usage has increased significantly, leading to its growth in
both size and complexity. Consequently, software development is becoming a more
time-consuming process. In an attempt to accelerate the development cycle, the
testing phase is often neglected, leading to the deployment of flawed systems
that can have significant implications on the users daily activities. This work
presents TestLab, an intelligent automated software testing framework that
attempts to gather a set of testing methods and automate them using Artificial
Intelligence to allow continuous testing of software systems at multiple levels
from different scopes, ranging from developers to end-users. The tool consists
of three modules, each serving a distinct purpose. The first two modules aim to
identify vulnerabilities from different perspectives, while the third module
enhances traditional automated software testing by automatically generating
test cases through source code analysis.Comment: 10 pages, 5 figures, 1 table, accepted for DCAI202
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection
Drowsy driving is a major cause of road accidents, but drivers are dismissive
of the impact that fatigue can have on their reaction times. To detect
drowsiness before any impairment occurs, a promising strategy is using Machine
Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work
presents multiple experiments with different HRV time windows and ML models, a
feature impact analysis using Shapley Additive Explanations (SHAP), and an
adversarial robustness analysis to assess their reliability when processing
faulty input data and perturbed HRV signals. The most reliable model was
Extreme Gradient Boosting (XGB) and the optimal time window had between 120 and
150 seconds. Furthermore, SHAP enabled the selection of the 18 most impactful
features and the training of new smaller models that achieved a performance as
good as the initial ones. Despite the susceptibility of all models to
adversarial attacks, adversarial training enabled them to preserve
significantly higher results, especially XGB. Therefore, ML models can
significantly benefit from realistic adversarial training to provide a more
robust driver drowsiness detection.Comment: 10 pages, 2 tables, 3 figures, AIME 2023 conferenc
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