1,416 research outputs found
Learning Linear Temporal Properties
We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach
Safe deployment of time-series classifiers for real-world applications relies
on the ability to detect the data which is not generated from the same
distribution as training data. This task is referred to as out-of-distribution
(OOD) detection. We consider the novel problem of OOD detection for the
time-series domain. We discuss the unique challenges posed by time-series data
and explain why prior methods from the image domain will perform poorly.
Motivated by these challenges, this paper proposes a novel {\em Seasonal Ratio
Scoring (SRS)} approach. SRS consists of three key algorithmic steps. First,
each input is decomposed into class-wise semantic component and remainder.
Second, this decomposition is employed to estimate the class-wise conditional
likelihoods of the input and remainder using deep generative models. The
seasonal ratio score is computed from these estimates. Third, a threshold
interval is identified from the in-distribution data to detect OOD examples.
Experiments on diverse real-world benchmarks demonstrate that the SRS method is
well-suited for time-series OOD detection when compared to baseline methods.
Open-source code for SRS method is provided at
https://github.com/tahabelkhouja/SRSComment: Accepted for publication at ACM Transactions on Intelligent Systems
and Technology (TIST
Leveraging Program Analysis to Reduce User-Perceived Latency in Mobile Applications
Reducing network latency in mobile applications is an effective way of
improving the mobile user experience and has tangible economic benefits. This
paper presents PALOMA, a novel client-centric technique for reducing the
network latency by prefetching HTTP requests in Android apps. Our work
leverages string analysis and callback control-flow analysis to automatically
instrument apps using PALOMA's rigorous formulation of scenarios that address
"what" and "when" to prefetch. PALOMA has been shown to incur significant
runtime savings (several hundred milliseconds per prefetchable HTTP request),
both when applied on a reusable evaluation benchmark we have developed and on
real applicationsComment: ICSE 201
Interpretable and Steerable Sequence Learning via Prototypes
One of the major challenges in machine learning nowadays is to provide
predictions with not only high accuracy but also user-friendly explanations.
Although in recent years we have witnessed increasingly popular use of deep
neural networks for sequence modeling, it is still challenging to explain the
rationales behind the model outputs, which is essential for building trust and
supporting the domain experts to validate, critique and refine the model. We
propose ProSeNet, an interpretable and steerable deep sequence model with
natural explanations derived from case-based reasoning. The prediction is
obtained by comparing the inputs to a few prototypes, which are exemplar cases
in the problem domain. For better interpretability, we define several criteria
for constructing the prototypes, including simplicity, diversity, and sparsity
and propose the learning objective and the optimization procedure. ProSeNet
also provides a user-friendly approach to model steering: domain experts
without any knowledge on the underlying model or parameters can easily
incorporate their intuition and experience by manually refining the prototypes.
We conduct experiments on a wide range of real-world applications, including
predictive diagnostics for automobiles, ECG, and protein sequence
classification and sentiment analysis on texts. The result shows that ProSeNet
can achieve accuracy on par with state-of-the-art deep learning models. We also
evaluate the interpretability of the results with concrete case studies.
Finally, through user study on Amazon Mechanical Turk (MTurk), we demonstrate
that the model selects high-quality prototypes which align well with human
knowledge and can be interactively refined for better interpretability without
loss of performance.Comment: Accepted as a full paper at KDD 2019 on May 8, 201
From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare
<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p>
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