pinecone/core-2020-05-10-deduplication huggingface dataset:
Active Learning and Proofreading for Delineation of Curvilinear
  Structures

Many state-of-the-art delineation methods rely on supervised machine learning
algorithms. As a result, they require manually annotated training data, which
is tedious to obtain. Furthermore, even minor classification errors may
significantly affect the topology of the final result. In this paper we propose
a generic approach to addressing both of these problems by taking into account
the influence of a potential misclassification on the resulting delineation. In
an Active Learning context, we identify parts of linear structures that should
be annotated first in order to train a classifier effectively. In a
proofreading context, we similarly find regions of the resulting reconstruction
that should be verified in priority to obtain a nearly-perfect result. In both
cases, by focusing the attention of the human expert on potential
classification mistakes which are the most critical parts of the delineation,
we reduce the amount of required supervision. We demonstrate the effectiveness
of our approach on microscopy images depicting blood vessels and neurons.Comment: extended version with fast reconstructio
Simultaneous Recognition and Pose Estimation of Instruments in Minimally
  Invasive Surgery

Detection of surgical instruments plays a key role in ensuring patient safety
in minimally invasive surgery. In this paper, we present a novel method for 2D
vision-based recognition and pose estimation of surgical instruments that
generalizes to different surgical applications. At its core, we propose a novel
scene model in order to simultaneously recognize multiple instruments as well
as their parts. We use a Convolutional Neural Network architecture to embody
our model and show that the cross-entropy loss is well suited to optimize its
parameters which can be trained in an end-to-end fashion. An additional
advantage of our approach is that instrument detection at test time is achieved
while avoiding the need for scale-dependent sliding window evaluation. This
allows our approach to be relatively parameter free at test time and shows good
performance for both instrument detection and tracking. We show that our
approach surpasses state-of-the-art results on in-vivo retinal microsurgery
image data, as well as ex-vivo laparoscopic sequences.Comment: 8 pages, 2 figures, MICCAI 201
Motion-Compensated Autonomous Scanning for Tumour Localisation using
  Intraoperative Ultrasound

Intraoperative ultrasound facilitates localisation of tumour boundaries
during minimally invasive procedures. Autonomous ultrasound scanning systems
have been recently proposed to improve scanning accuracy and reduce surgeons'
cognitive load. However, current methods mainly consider static scanning
environments typically with the probe pressing against the tissue surface. In
this work, a motion-compensated autonomous ultrasound scanning system using the
da Vinci Research Kit (dVRK) is proposed. An optimal scanning trajectory is
generated considering both the tissue surface shape and the ultrasound
transducer dimensions. A robust vision-based approach is proposed to learn the
underlying tissue motion characteristics. The learned motion model is then
incorporated into the visual servoing framework. The proposed system has been
validated with both phantom and ex vivo experiments using the ground truth
motion data for comparison.Comment: Accepted by Medical Image Computing and Computer Assisted
  Interventions Conference (MICCAI) 201
A hazard analysis method for systematic identification of safety requirements for user interface software in medical devices

© Springer International Publishing AG (outside the US) 2017. Formal methods technologies have the potential to verify the usability and safety of user interface (UI) software design in medical devices, enabling significant reductions in use errors and consequential safety incidents with such devices. This however depends on comprehensive and verifiable safety requirements to leverage these techniques for detecting and preventing flaws in UI software that can induce use errors. This paper presents a hazard analysis method that extends Leveson’s System Theoretic Process Analysis (STPA) with a comprehensive set of causal factor categories, so as to provide developers with clear guidelines for systematic identification of use-related hazards associated with medical devices, their causes embedded in UI software design, and safety requirements for mitigating such hazards. The method is evaluated with a case study on the Gantry-2 radiation therapy system, which demonstrates that (1) as compared to standard STPA, our method allowed us to identify more UI software design issues likely to cause use-related hazards; and (2) the identified UI software design issues facilitated the definition of precise, verifiable safety requirements for UI software, which could be readily formalized in verification tools such as Prototype Verification System (PVS).- U.S. Food and Drug Administration(NORTE-01-0145-FEDER-000016)Sandy Weininger (FDA), Scott Thiel (Navigant Consulting, Inc.), Michelle Jump (Stryker), Stefania Gnesi (ISTI/CNR) and the CHI+MED team (www.chi-med.ac.uk) provided useful feedback and inputs. Paolo Masci’s work is supported by the North Portugal Regional Operational Programme (NORTE 2020) under the PORTUGAL 2020 Partnership Agreement, and by the European Regional Development Fund (ERDF) within Project “NORTE-01-0145-FEDER-000016”.info:eu-repo/semantics/publishedVersio
Early Safety Assessment of Automotive Systems Using Sabotage Simulation-Based Fault Injection Framework

As road vehicles increase their autonomy and the driver reduces his role in the control loop, novel challenges on dependability assessment arise. Model-based design combined with a simulation-based fault injection technique and a virtual vehicle poses as a promising solution for an early safety assessment of automotive systems. To start with, the design, where no safety was considered, is stimulated with a set of fault injection simulations (fault forecasting). By doing so, safety strategies can be evaluated during early development phases estimating the relationship of an individual failure to the degree of misbehaviour on vehicle level. After having decided the most suitable safety concept, a second set of fault injection experiments is used to perform an early safety validation of the chosen architecture. This double-step process avoids late redesigns, leading to significant cost and time savings. This paper presents a simulation-based fault injection approach aimed at finding acceptable safety properties for model-based design of automotive systems. We focus on instrumenting the use of this technique to obtain fault effects and the maximum response time of a system before a hazardous event occurs. Through these tangible outcomes, safety concepts and mechanisms can be more accurately dimensioned. In this work, a prototype tool called Sabotage has been developed to set up, configure, execute and analyse the simulation results. The feasibility of this method is demonstrated by applying it to a Lateral Control system.The authors have partially received funding from the ECSEL JU AMASS\ud
project under H2020 grant agreement No 692474, the UnCoVerCPS project under H2020 grant\ud
agreement No 643921 and MINETUR (Spain)
Systematic pattern approach for safety and security co-engineering in the automotive domain

Future automotive systems will exhibit increased levels of automation as well as ever tighter integration with other vehicles, traffic infrastructure, and cloud services. From safety perspective, this can be perceived as boon or bane - it greatly increases complexity and uncertainty, but at the same time opens up new opportunities for realizing innovative safety functions. Moreover, cybersecurity becomes important as additional concern because attacks are now much more likely and severe. Unfortunately, there is lack of experience with security concerns in context of safety engineering in general and in automotive safety departments in particular. To remediate this problem, we propose a systematic pattern-based approach that interlinks safety and security patterns and provides guidance with respect to selection and combination of both types of patterns in context of system engineering. The application of a combined safety and security pattern engineering workflow is shown and demonstrated by an automotive use case scenario
Modeling Operator Behavior in the Safety Analysis of Collaborative Robotic Applications

Human-Robot Collaboration is increasingly prominent in peo-\ud
ple's lives and in the industrial domain, for example in manufacturing\ud
applications. The close proximity and frequent physical contacts between\ud
humans and robots in such applications make guaranteeing suitable levels\ud
of safety for human operators of the utmost importance. Formal veri-\ud
cation techniques can help in this regard through the exhaustive explo-\ud
ration of system models, which can identify unwanted situations early in\ud
the development process. This work extends our SAFER-HRC method-\ud
ology with a rich non-deterministic formal model of operator behaviors,\ud
which captures the hazardous situations resulting from human errors.\ud
The model allows safety engineers to rene their designs until all plausi-\ud
ble erroneous behaviors are considered and mitigated
On Binary Matroid Minors and Applications to Data Storage over Small
  Fields

Locally repairable codes for distributed storage systems have gained a lot of
interest recently, and various constructions can be found in the literature.
However, most of the constructions result in either large field sizes and hence
too high computational complexity for practical implementation, or in low rates
translating into waste of the available storage space. In this paper we address
this issue by developing theory towards code existence and design over a given
field. This is done via exploiting recently established connections between
linear locally repairable codes and matroids, and using matroid-theoretic
characterisations of linearity over small fields. In particular, nonexistence
can be shown by finding certain forbidden uniform minors within the lattice of
cyclic flats. It is shown that the lattice of cyclic flats of binary matroids
have additional structure that significantly restricts the possible locality
properties of $\mathbb{F}_{2}$-linear storage codes. Moreover, a collection of
criteria for detecting uniform minors from the lattice of cyclic flats of a
given matroid is given, which is interesting in its own right.Comment: 14 pages, 2 figure
Asymptotic bounds for the sizes of constant dimension codes and an
  improved lower bound

We study asymptotic lower and upper bounds for the sizes of constant
dimension codes with respect to the subspace or injection distance, which is
used in random linear network coding. In this context we review known upper
bounds and show relations between them. A slightly improved version of the
so-called linkage construction is presented which is e.g. used to construct
constant dimension codes with subspace distance $d=4$, dimension $k=3$ of the
codewords for all field sizes $q$, and sufficiently large dimensions $v$ of the
ambient space, that exceed the MRD bound, for codes containing a lifted MRD
code, by Etzion and Silberstein.Comment: 30 pages, 3 table
Symbolic-Numeric Integration of the Dynamical Cosserat Equations

We devise a symbolic-numeric approach to the integration of the dynamical
part of the Cosserat equations, a system of nonlinear partial differential
equations describing the mechanical behavior of slender structures, like fibers
and rods. This is based on our previous results on the construction of a closed
form general solution to the kinematic part of the Cosserat system. Our
approach combines methods of numerical exponential integration and symbolic
integration of the intermediate system of nonlinear ordinary differential
equations describing the dynamics of one of the arbitrary vector-functions in
the general solution of the kinematic part in terms of the module of the twist
vector-function. We present an experimental comparison with the
well-established generalized alpha-method illustrating the computational
efficiency of our approach for problems in structural mechanics.Comment: 12 pages, 2 figures. arXiv admin note: text overlap with
  arXiv:1610.0336
Symbolic Versus Numerical Computation and Visualization of Parameter
  Regions for Multistationarity of Biological Networks

We investigate models of the mitogenactivated protein kinases (MAPK) network,
with the aim of determining where in parameter space there exist multiple
positive steady states. We build on recent progress which combines various
symbolic computation methods for mixed systems of equalities and inequalities.
We demonstrate that those techniques benefit tremendously from a newly
implemented graph theoretical symbolic preprocessing method. We compare
computation times and quality of results of numerical continuation methods with
our symbolic approach before and after the application of our preprocessing.Comment: Accepted into Proc. CASC 201
Statistical abstraction for multi-scale spatio-temporal systems

Spatio-temporal systems exhibiting multi-scale behaviour are common in
applications ranging from cyber-physical systems to systems biology, yet they
present formidable challenges for computational modelling and analysis. Here we
consider a prototypic scenario where spatially distributed agents decide their
movement based on external inputs and a fast-equilibrating internal
computation. We propose a generally applicable strategy based on statistically
abstracting the internal system using Gaussian Processes, a powerful class of
non-parametric regression techniques from Bayesian Machine Learning. We show on
a running example of bacterial chemotaxis that this approach leads to accurate
and much faster simulations in a variety of scenarios.Comment: 14th International Conference on Quantitative Evaluation of SysTems
  (QEST 2017
Sequential schemes for frequentist estimation of properties in statistical model checking

Statistical Model Checking (SMC) is an approximate verification method that overcomes the state space explosion problem for probabilistic systems by Monte Carlo simulations. Simulations might be however costly if many samples are required. It is thus necessary to implement efficient algorithms to reduce the sample size while preserving precision and accuracy. In the literature, some sequential schemes have been provided for the estimation of property occurrence based on predefined confidence and absolute or relative error. Nevertheless, these algorithms remain conservative and may result in huge sample sizes if the required precision standards are demanding. In this article, we compare some useful bounds and some sequential methods based on frequentist estimations. We propose outperforming and rigorous alternative schemes, based on Massart bounds and robust confidence intervals. Our theoretical and empirical analysis show that our proposal reduces the sample size while providing guarantees on error bounds.Full Tex
EUStress: A human behaviour analysis system for monitoring and assessing stress during exams

In today’s society, there is a compelling need for innovative approaches for the solution of many pressing problems, such as understanding the fluctuations in the performance of an individual when involved in complex and high-stake tasks. In these cases, individuals are under an increasing demand for performance, driving them to be under constant pressure, and consequently to present variations in their levels of stress. Human stress can be viewed as an agent, circumstance, situation, or variable that disturbs the normal functioning of an individual, that when not managed can bring mental problems, such as chronic stress or depression. In this paper, we propose a different approach for this problem. The EUStress application is a non-intrusive and non-invasive performance monitoring environment based on behavioural biometrics and real time analysis, used to quantify the level of stress of individuals during online exams.FCT - Fuel Cell Technologies Program(NORTE-01-0247-FEDER-017832)info:eu-repo/semantics/publishedVersio
A manifesto for conscientious design of hybrid online social systems

Online Social Systems such as community forums, social media, e-commerce and gaming are having an increasingly significant impact on our lives. They affect the way we accomplish all sorts of collective activities, the way we relate to others, and the way we construct are own self-image. These systems often have both human and artificial agency creating what we call online hybrid social systems. However, when systems are designed and constructed, the psychological and sociological impact of such systems on individuals and communities is not always worked out in advance. \ud
We see this as a significant challenge for which coordination, organisations, institutions and norms are core resources and we would like to make a call to arms researchers in these topics to subscribe a conscientious approach to that challenge. \ud
\ud
In this paper we identify a class of design issues that need attention when designing hybrid online social systems and propose to address those problems using conscientious design which is underpinned by ethical and social values. We present an austere framework to articulate those notions and illustrate these ideas with an example. We  outline five lines of research  that we see worth pursuing. © Springer International Publishing AG 2017.Peer Reviewe
Identifying game elements suitable for MOOCs

Massive Online Open Courses (MOOCs) have increasingly become
objects of research interest and studies in recent years. While MOOCs could be
a means to address massive audiences, they suffer from high drop-out rates and
low user engagement. Gamification is known as the application of game design
elements in non-gaming scenarios to solve problems or to influence a user’s behaviour
change. By applying gamification to MOOCs, we aim to enhance users’
engagement and goal achievement within a MOOC environment. To define our
gamification strategy, we asked 42 experts in the fields of game design, learning
science and technology-enhanced learning to rate 21 selected game design
patterns according to their suitability within a MOOC environment application.
The data collected allowed us to identify a set of nine game design patterns as
promising candidates to be tested in MOOC environment
A New Theoretical Framework for Curiosity for Learning in Social
  Contexts

Curiosity is a vital metacognitive skill in educational contexts. Yet, little
is known about how social factors influence curiosity in group work. We argue
that curiosity is evoked not only through individual, but also interpersonal
activities, and present what we believe to be the first theoretical framework
that articulates an integrated socio-cognitive account of curiosity based on
literature spanning psychology, learning sciences and group dynamics, along
with empirical observation of small-group science activity in an informal
learning environment. We make a bipartite distinction between individual and
interpersonal functions that contribute to curiosity, and multimodal behaviors
that fulfill these functions. We validate the proposed framework by leveraging
a longitudinal latent variable modeling approach. Findings confirm positive
predictive relationship of the latent variables of individual and interpersonal
functions on curiosity, with the interpersonal functions exercising a
comparatively stronger influence. Prominent behavioral realizations of these
functions are also discovered in a data-driven way. This framework is a step
towards designing learning technologies that can recognize and evoke curiosity
during learning in social contexts.Comment: 14 pages, 1 figure, 12th European Conference on Technology Enhanced
  Learning (ECTEL 2017
Curious Minds Wonder Alike: Studying Multimodal Behavioral Dynamics to
  Design Social Scaffolding of Curiosity

Curiosity is the strong desire to learn or know more about something or
someone. Since learning is often a social endeavor, social dynamics in
collaborative learning may inevitably influence curiosity. There is a scarcity
of research, however, focusing on how curiosity can be evoked in group learning
contexts. Inspired by a recently proposed theoretical framework that
articulates an integrated socio-cognitive infrastructure of curiosity, in this
work, we use data-driven approaches to identify fine-grained social scaffolding
of curiosity in child-child interaction, and propose how they can be used to
elicit and maintain curiosity in technology-enhanced learning environments. For
example, we discovered sequential patterns of multimodal behaviors across group
members and we describe those that maximize an individual's utility, or
likelihood, of demonstrating curiosity during open-ended problem-solving in
group work. We also discovered, and describe here, behaviors that directly or
in a mediated manner cause curiosity related conversational behaviors in the
interaction, with twice as many interpersonal causal influences compared to
intrapersonal ones. We explain how these findings form a solid foundation for
developing curiosity-increasing learning technologies or even assisting a human
coach to induce curiosity among learners.Comment: 14 pages, 12th European Conference on Technology Enhanced Learning
  (ECTEL 2017
Using sequential pattern mining to explore learners' behaviors and evaluate their correlation with performance in inquiry-based learning

This study analyzes students’ behaviors in a remote laboratory environment in order to identify new factors of prediction of academic success. It investigates relations between learners’ activities during practical sessions, and their performance at the final assessment test. Based on learning analytics applied to data collected from an experimentation conducted with our remote lab dedicated to computer education, we discover recurrent sequential patterns of actions that lead us to the definition of learning strategies as indicators of higher level of abstraction. Results show that some of the strategies are correlated to learners’ performance. For instance, the construction of a complex action step by step, or the reflection before submitting an action, are two strategies applied more often by learners of a higher level of performance than by other students. While our proposals are domain-independent and can thus apply to other learning contexts, the results of this study led us to instrument for both students and instructors new visualization and guiding tools in our remote lab environment
Hedging under uncertainty: regret minimization meets exponentially fast convergence

International audienceThis paper examines the problem of multi-agent learning in N-person non-cooperative games. For concreteness, we focus on the so-called “hedge” variant of the exponential weights (EW) algorithm, one of the most widely studied algorithmic schemes for regret minimization in online learning. In this multi-agent context, we show that a) dominated strategies become extinct (a.s.); and b) in generic games, pure Nash equilibria are attracting with high probability, even in the presence of uncertainty and noise of arbitrarily high variance. Moreover, if the algorithm’s step-size does not decay too fast, we show that these properties occur at a quasi-exponential rate – that is, much faster than the algorithm’s O(1/\sqrt{T}) worst-case regret guarantee would suggest