pinecone/core-2020-05-10-deduplication huggingface dataset:
How does Scrum Conform to the Regulatory Requirements Defined in MDevSPICE?

Medical device software development is subject to high regulations due to the potential risk of harming patients with unsafe medical devices. These regulations require software development to be performed with high discipline and evidence to be provided for auditory purposes. It’s not easy to manage both conformance to regulations and efficiency in medical device development. Therefore, there is a transition towards agility in safety critical systems development, to build high quality systems, shorten time to market, improve customer and employee satisfaction and ensure both safety and reliability. In this study, we evaluated one of the most highly adopted agile software development methods, Scrum from a regulatory perspective. We investigated to what extend the regulatory requirements defined in MDevSPICE® are met with implementation of the Scrum method and what additional processes and practices have to be performed to ensure safety and regulatory compliance in the healthcare domain
The role of international standards to corroborate artefact development and evaluation: Experiences from a design science research project in process assessment

International standards were used to corroborate artefact development and evaluation in a Design Science Research (DSR) project within the context of Process Assessments in IT Service Management (ITSM). While there have been significant research efforts towards extending DSR guidelines and the development and revisions of the standards, reports of the application of International Standards to validate DSR artefacts are scant. DSR, akin to any academic research, is required to demonstrate rigour and relevance with the use of theories and prior knowledge. Moreover, DSR presents an artefact as a solution to a class of problems and reports how the artefact is developed and evaluated. Our DSR project demonstrated that concerns about the quality of artefacts can be addressed and thereby the utility and validity of the artefact can be verified with the use of International Standards. Using three International Standards, process assessment ISO/IEC 15504-33000 series, IT Service Management ISO/IEC 20000, and System and Software Quality Models ISO/IEC 25010, this paper presents an account of a real-life DSR project that demonstrates the significant role of International Standards to guide DSR researchers during artefact design, development and evaluation.Full Tex
Cultural Issues and Impacts of Software Process in Very Small Entities (VSEs)

ISO/IEC29110 is an international standard of software lifecycle for small software companies also known as Very Small Entities (VSEs). While VSEs come from a diversity of cultural backgrounds, the current ISO/IEC29110 for VSEs does not address these cultural variations. VSEs from various cultural backgrounds might therefore find it difficult to adapt such a standard. This paper raises the issue that the current ISO/IEC29110 should recognize the impacts of cultural variation on software processes and cultural suggestions. It also point out one cultural dimension has a significant impact on software processes and their efficacy. Furthermore, the concepts of cultural consideration should not be limited to regions but, also cover the management perspective of individual VSE. In this paper, we identified two opposite cultural types which affect their software processes significantly. We propose that to make software process standards more practical for VSEs from different cultural backgrounds.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Deep-FExt: Deep Feature Extraction for Vessel Segmentation and
  Centerline Prediction

Feature extraction is a very crucial task in image and pixel (voxel)
classification and regression in biomedical image modeling. In this work we
present a machine learning based feature extraction scheme based on inception
models for pixel classification tasks. We extract features under multi-scale
and multi-layer schemes through convolutional operators. Layers of Fully
Convolutional Network are later stacked on this feature extraction layers and
trained end-to-end for the purpose of classification. We test our model on the
DRIVE and STARE public data sets for the purpose of segmentation and centerline
detection and it out performs most existing hand crafted or deterministic
feature schemes found in literature. We achieve an average maximum Dice of 0.85
on the DRIVE data set which out performs the scores from the second human
annotator of this data set. We also achieve an average maximum Dice of 0.85 and
kappa of 0.84 on the STARE data set. Though these datasets are mainly 2-D we
also propose ways of extending this feature extraction scheme to handle 3-D
datasets.Comment: 9 page
Yet Another ADNI Machine Learning Paper? Paving The Way Towards
  Fully-reproducible Research on Classification of Alzheimer's Disease

In recent years, the number of papers on Alzheimer's disease classification
has increased dramatically, generating interesting methodological ideas on the
use machine learning and feature extraction methods. However, practical impact
is much more limited and, eventually, one could not tell which of these
approaches are the most efficient. While over 90\% of these works make use of
ADNI an objective comparison between approaches is impossible due to variations
in the subjects included, image pre-processing, performance metrics and
cross-validation procedures. In this paper, we propose a framework for
reproducible classification experiments using multimodal MRI and PET data from
ADNI. The core components are: 1) code to automatically convert the full ADNI
database into BIDS format; 2) a modular architecture based on Nipype in order
to easily plug-in different classification and feature extraction tools; 3)
feature extraction pipelines for MRI and PET data; 4) baseline classification
approaches for unimodal and multimodal features. This provides a flexible
framework for benchmarking different feature extraction and classification
tools in a reproducible manner. We demonstrate its use on all (1519) baseline
T1 MR images and all (1102) baseline FDG PET images from ADNI 1, GO and 2 with
SPM-based feature extraction pipelines and three different classification
techniques (linear SVM, anatomically regularized SVM and multiple kernel
learning SVM). The highest accuracies achieved were: 91% for AD vs CN, 83% for
MCIc vs CN, 75% for MCIc vs MCInc, 94% for AD-A$\beta$+ vs CN-A$\beta$- and 72%
for MCIc-A$\beta$+ vs MCInc-A$\beta$+. The code is publicly available at
https://gitlab.icm-institute.org/aramislab/AD-ML (depends on the Clinica
software platform, publicly available at http://www.clinica.run)
Efficient Compression and Indexing of Trajectories

We present a new compressed representation of free trajectories of moving
objects. It combines a partial-sums-based structure that retrieves in constant
time the position of the object at any instant, with a hierarchical
minimum-bounding-boxes representation that allows determining if the object is
seen in a certain rectangular area during a time period. Combined with spatial
snapshots at regular intervals, the representation is shown to outperform
classical ones by orders of magnitude in space, and also to outperform previous
compressed representations in time performance, when using the same amount of
space.Comment: This research has received funding from the European Union's Horizon
  2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
  Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094
A situated BDI agent architecture for the GAMA modelling and simulation platform

With the increase of computing power and the development of user-friendly multi-agent simulation frameworks, social simulations have become increasingly realistic. However, most agent architectures in these simulations use simple reactive models. Indeed, cognitive agent architectures face two main obstacles: their complexity for the field-expert modeler, and their computational cost. In this paper, we propose a new cognitive agent architecture based on the BDI (Belief-Desire-Intention) paradigm integrated into the GAMA modeling platform and its GAML modeling language. This architecture was designed to be simple-to-use for modelers, flexible enough to manage complex behaviors, and with low computational cost. An experiment carried out with different profiles of end-users shows that the architecture is actually usable even by modelers who have little knowledge in programming and in Artificial Intelligence
Exploring trade and health policies influence on Dengue spread with an Agent-Based Model

With the globalization, several free trade areas have been and are being created all around the world. They usually have positive consequences for increasing economic exchanges, but negative ecological or health side effects. These negative effects are difficult to predict or even to understand due to the complexity of the system and of the number of involved processes. In this article, we focus on the Southeast Asia free trade area (the ASEAN) and specifically in the East-West economic corridor. A significant correlation has been observed in this area between the corridor opening and dengue fever cases, without being able to establish a causality relationship. We choose to tackle this issue by building an agent-based geographically explicit model. We propose an approach coupling dengue fever dynamics, climate data, economic mobility and health policies, following a design methodology decomposing these processes in sub-models and linking them to make one integrated model. In addition, we propose a way to deal with lack of data in the modeling process. Our simulation results show that there is influence of the increase in mobility and application of different control policies on the increase of dengue cases
Killing 2-forms in dimension 4

A Killing $p$-form on a Riemannian manifold is a $p$-form whose covariant
derivative is totally anti-symmetric. In this paper we give the complete
(local) description of 4-dimensional Riemannian manifolds (M,g) carrying
non-parallel Killing 2-forms $\varphi$. If $M$ is connected and oriented, we
show that there exists a dense open subset of $M$ on which one of the three
exclusive situations holds: either $\phi$ is everywhere degenerate and $g$ is
conformal to a product metric, or $g$ is conformal to an ambik\"ahler metric
obtained via the Calabi construction from a polarized Riemannian surface, or
$g$ is conformal to an ambitoric structure of hyperbolic type, and depends
locally on two functions of one variable. We also give compact examples, by
constructing infinite-dimensional families of Riemannian metrics carrying
Killing 2-forms of each of the above types on $S^4$ and on Hirzebruch surfaces.Comment: 36 page
A foundation for runtime monitoring

Runtime Verification is a lightweight technique that complements other verification methods in an effort to ensure software correctness. The technique poses novel questions to software engineers: it is not easy to identify which specifications are amenable to runtime monitor-ing, nor is it clear which monitors effect the required runtime analysis correctly. This exposition targets a foundational understanding of these questions. Particularly, it considers an expressive specification logic (a syntactic variant of the modal μ-calculus) that is agnostic of the verification method used, together with an elemental framework providing an operational semantics for the runtime analysis performed by monitors. The correspondence between the property satisfactions in the logic on the one hand, and the verdicts reached by the monitors performing the analysis on the other, is a central theme of the study. Such a correspondence underpins the concept of monitorability, used to identify the subsets of the logic that can be adequately monitored for by RV. Another theme of the study is that of understanding what should be expected of a monitor in order for the verification process to be correct. We show how the monitor framework considered can constitute a basis whereby various notions of monitor correctness may be defined and investigated.peer-reviewe
HySIA: Tool for Simulating and Monitoring Hybrid Automata Based on
  Interval Analysis

We present HySIA: a reliable runtime verification tool for nonlinear hybrid
automata (HA) and signal temporal logic (STL) properties. HySIA simulates an HA
with interval analysis techniques so that a trajectory is enclosed sharply
within a set of intervals. Then, HySIA computes whether the simulated
trajectory satisfies a given STL property; the computation is performed again
with interval analysis to achieve reliability. Simulation and verification
using HySIA are demonstrated through several example HA and STL formulas.Comment: Appeared in RV'17; the final publication is available at Springe
Domain-adversarial neural networks to address the appearance variability
  of histopathology images

Preparing and scanning histopathology slides consists of several steps, each
with a multitude of parameters. The parameters can vary between pathology labs
and within the same lab over time, resulting in significant variability of the
tissue appearance that hampers the generalization of automatic image analysis
methods. Typically, this is addressed with ad-hoc approaches such as staining
normalization that aim to reduce the appearance variability. In this paper, we
propose a systematic solution based on domain-adversarial neural networks. We
hypothesize that removing the domain information from the model representation
leads to better generalization. We tested our hypothesis for the problem of
mitosis detection in breast cancer histopathology images and made a comparative
analysis with two other approaches. We show that combining color augmentation
with domain-adversarial training is a better alternative than standard
approaches to improve the generalization of deep learning methods.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi
Simultaneous Multiple Surface Segmentation Using Deep Learning

The task of automatically segmenting 3-D surfaces representing boundaries of
objects is important for quantitative analysis of volumetric images, and plays
a vital role in biomedical image analysis. Recently, graph-based methods with a
global optimization property have been developed and optimized for various
medical imaging applications. Despite their widespread use, these require human
experts to design transformations, image features, surface smoothness priors,
and re-design for a different tissue, organ or imaging modality. Here, we
propose a Deep Learning based approach for segmentation of the surfaces in
volumetric medical images, by learning the essential features and
transformations from training data, without any human expert intervention. We
employ a regional approach to learn the local surface profiles. The proposed
approach was evaluated on simultaneous intraretinal layer segmentation of
optical coherence tomography (OCT) images of normal retinas and retinas
affected by age related macular degeneration (AMD). The proposed approach was
validated on 40 retina OCT volumes including 20 normal and 20 AMD subjects. The
experiments showed statistically significant improvement in accuracy for our
approach compared to state-of-the-art graph based optimal surface segmentation
with convex priors (G-OSC). A single Convolution Neural Network (CNN) was used
to learn the surfaces for both normal and diseased images. The mean unsigned
surface positioning errors obtained by G-OSC method 2.31 voxels (95% CI
2.02-2.60 voxels) was improved to $1.27$ voxels (95% CI 1.14-1.40 voxels) using
our new approach. On average, our approach takes 94.34 s, requiring 95.35 MB
memory, which is much faster than the 2837.46 s and 6.87 GB memory required by
the G-OSC method on the same computer system.Comment: 8 page
End-to-End Unsupervised Deformable Image Registration with a
  Convolutional Neural Network

In this work we propose a deep learning network for deformable image
registration (DIRNet). The DIRNet consists of a convolutional neural network
(ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet
analyzes a pair of fixed and moving images and outputs parameters for the
spatial transformer, which generates the displacement vector field that enables
the resampler to warp the moving image to the fixed image. The DIRNet is
trained end-to-end by unsupervised optimization of a similarity metric between
input image pairs. A trained DIRNet can be applied to perform registration on
unseen image pairs in one pass, thus non-iteratively. Evaluation was performed
with registration of images of handwritten digits (MNIST) and cardiac cine MR
scans (Sunnybrook Cardiac Data). The results demonstrate that registration with
DIRNet is as accurate as a conventional deformable image registration method
with substantially shorter execution times
Generalised Dice overlap as a deep learning loss function for highly
  unbalanced segmentations

Deep-learning has proved in recent years to be a powerful tool for image
analysis and is now widely used to segment both 2D and 3D medical images.
Deep-learning segmentation frameworks rely not only on the choice of network
architecture but also on the choice of loss function. When the segmentation
process targets rare observations, a severe class imbalance is likely to occur
between candidate labels, thus resulting in sub-optimal performance. In order
to mitigate this issue, strategies such as the weighted cross-entropy function,
the sensitivity function or the Dice loss function, have been proposed. In this
work, we investigate the behavior of these loss functions and their sensitivity
to learning rate tuning in the presence of different rates of label imbalance
across 2D and 3D segmentation tasks. We also propose to use the class
re-balancing properties of the Generalized Dice overlap, a known metric for
segmentation assessment, as a robust and accurate deep-learning loss function
for unbalanced tasks
ssEMnet: Serial-section Electron Microscopy Image Registration using a
  Spatial Transformer Network with Learned Features

The alignment of serial-section electron microscopy (ssEM) images is critical
for efforts in neuroscience that seek to reconstruct neuronal circuits.
However, each ssEM plane contains densely packed structures that vary from one
section to the next, which makes matching features across images a challenge.
Advances in deep learning has resulted in unprecedented performance in similar
computer vision problems, but to our knowledge, they have not been successfully
applied to ssEM image co-registration. In this paper, we introduce a novel deep
network model that combines a spatial transformer for image deformation and a
convolutional autoencoder for unsupervised feature learning for robust ssEM
image alignment. This results in improved accuracy and robustness while
requiring substantially less user intervention than conventional methods. We
evaluate our method by comparing registration quality across several datasets.Comment: DLMIA 2017 accepte
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance
  Imaging

3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but
low-resolution image acquisition and highly detailed but slow image
acquisition. Fast imaging is required for targets that move to avoid motion
artefacts. This is in particular difficult for fetal MRI. Spatially independent
upsampling techniques, which are the state-of-the-art to address this problem,
are error prone and disregard contextual information. In this paper we propose
a context-sensitive upsampling method based on a residual convolutional neural
network model that learns organ specific appearance and adopts semantically to
input data allowing for the generation of high resolution images with sharp
edges and fine scale detail. By making contextual decisions about appearance
and shape, present in different parts of an image, we gain a maximum of
structural detail at a similar contrast as provided by high-resolution data. We
experiment on $145$ fetal scans and show that our approach yields an increased
PSNR of $1.25$ $dB$ when applied to under-sampled fetal data \emph{cf.}
baseline upsampling. Furthermore, our method yields an increased PSNR of $1.73$
$dB$ when utilizing under-sampled fetal data to perform brain volume
reconstruction on motion corrupted captured data.Comment: 11 pages, 6 figures, published in Proc MICCAI RAMBO'17
  https://link.springer.com/chapter/10.1007/978-3-319-67564-0_1
Semantic Change and Extension Enforcement in Abstract Argumentation

Change in argumentation frameworks has been widely studied in the recent years. Most of the existing works on this topic are concerned with change of the structure of the argumentation graph (addition or removal of arguments and attacks), or change of the outcome of the framework (acceptance statuses of arguments). Change on the acceptability semantics that is used in the framework has not received much attention so far. Such a change can be motivated by different reasons, especially it is a way to change the outcome of the framework. In this paper, it is shown how semantic change can be used as a way to reach a goal about acceptance statuses in a situation of extension enforcement
Video Pandemics: Worldwide Viral Spreading of Psy's Gangnam Style Video

Viral videos can reach global penetration traveling through international
channels of communication similarly to real diseases starting from a
well-localized source. In past centuries, disease fronts propagated in a
concentric spatial fashion from the the source of the outbreak via the short
range human contact network. The emergence of long-distance air-travel changed
these ancient patterns. However, recently, Brockmann and Helbing have shown
that concentric propagation waves can be reinstated if propagation time and
distance is measured in the flight-time and travel volume weighted underlying
air-travel network. Here, we adopt this method for the analysis of viral meme
propagation in Twitter messages, and define a similar weighted network distance
in the communication network connecting countries and states of the World. We
recover a wave-like behavior on average and assess the randomizing effect of
non-locality of spreading. We show that similar result can be recovered from
Google Trends data as well.Comment: 10 page
On the Scalability of Data Reduction Techniques in Current and Upcoming
  HPC Systems from an Application Perspective

We implement and benchmark parallel I/O methods for the fully-manycore driven
particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as
a major challenge for applications on today's and future HPC systems, we
present a scaling law characterizing performance bottlenecks in
state-of-the-art approaches for data reduction. Consequently, we propose,
implement and verify multi-threaded data-transformations for the I/O library
ADIOS as a feasible way to trade underutilized host-side compute potential on
heterogeneous systems for reduced I/O latency.Comment: 15 pages, 5 figures, accepted for DRBSD-1 in conjunction with ISC'1