pinecone/core-2020-05-10-deduplication huggingface dataset:
Statistical Distances and Their Role in Robustness

Statistical distances, divergences, and similar quantities have a large
history and play a fundamental role in statistics, machine learning and
associated scientific disciplines. However, within the statistical literature,
this extensive role has too often been played out behind the scenes, with other
aspects of the statistical problems being viewed as more central, more
interesting, or more important. The behind the scenes role of statistical
distances shows up in estimation, where we often use estimators based on
minimizing a distance, explicitly or implicitly, but rarely studying how the
properties of a distance determine the properties of the estimators. Distances
are also prominent in goodness-of-fit, but the usual question we ask is "how
powerful is this method against a set of interesting alternatives" not "what
aspect of the distance between the hypothetical model and the alternative are
we measuring?"
  Our focus is on describing the statistical properties of some of the distance
measures we have found to be most important and most visible. We illustrate the
robust nature of Neyman's chi-squared and the non-robust nature of Pearson's
chi-squared statistics and discuss the concept of discretization robustness.Comment: 23 page
The out-of-source error in multi-source cross validation-type procedures

A scientific phenomenon under study may often be manifested by data arising
from processes, i.e. sources, that may describe this phenomenon. In this contex
of multi-source data, we define the "out-of-source" error, that is the error
committed when a new observation of unknown source origin is allocated to one
of the sources using a rule that is trained on the known labeled data. We
present an unbiased estimator of this error, and discuss its variance. We
derive natural and easily verifiable assumptions under which the consistency of
our estimator is guaranteed for a broad class of loss functions and data
distributions. Finally, we evaluate our theoretical results via a simulation
study.Comment: 16 pages, 4 table
Sieving for closest lattice vectors (with preprocessing)

Lattice-based cryptography has recently emerged as a prime candidate for
efficient and secure post-quantum cryptography. The two main hard problems
underlying its security are the shortest vector problem (SVP) and the closest
vector problem (CVP). Various algorithms have been studied for solving these
problems, and for SVP, lattice sieving currently dominates in terms of the
asymptotic time complexity: one can heuristically solve SVP in time
$2^{0.292d}$ in high dimensions $d$ [BDGL'16]. Although several SVP algorithms
can also be used to solve CVP, it is not clear whether this also holds for
heuristic lattice sieving methods. The best time complexity for CVP is
currently $2^{0.377d}$ [BGJ'14].
  In this paper we revisit sieving algorithms for solving SVP, and study how
these algorithms can be modified to solve CVP and its variants as well. Our
first method is aimed at solving one problem instance and minimizes the overall
time complexity for a single CVP instance with a time complexity of
$2^{0.292d}$. Our second method minimizes the amortized time complexity for
several instances on the same lattice, at the cost of a larger preprocessing
cost. We can solve the closest vector problem with preprocessing (CVPP) with
$2^{0.636d}$ space and preprocessing, in $2^{0.136d}$ time, while the query
complexity can even be reduced to $2^{\epsilon d}$ at the cost of preprocessing
time and memory complexities of $(1/\epsilon)^{O(d)}$.
  For easier variants of CVP, such as approximate CVP and bounded distance
decoding (BDD), we further show how the preprocessing method achieves even
better complexities. For instance, we can solve approximate CVPP with large
approximation factors $k$ with polynomial-sized advice in polynomial time if $k
= \Omega(\sqrt{d/\log d})$, heuristically closing the gap between the
decision-CVPP result of [AR'04] and the search-CVPP result of [DRS'14].Comment: 23rd Conference on Selected Areas in Cryptography (SAC), 201
Semi-supervised log pattern detection and exploration using event concurrence and contextual information

Process mining offers a variety of techniques for analyzing process execution event logs. Although process discovery algorithms construct end-to-end process models, they often have difficulties dealing with the complexity of real-life event logs. Discovered models may contain either complex or over-generalized fragments, the interpretation of which is difficult, and can result in misleading insights. Detecting and visualizing behavioral patterns instead of creating model structures can reduce complexity and give more accurate insights into recorded behaviors. Unsupervised detection techniques, based on statistical properties of the log only, generate a multitude of patterns and lack domain context. Supervised pattern detection requires a domain expert to specify patterns manually and lacks the event log context. In this paper, we reconcile supervised and unsupervised pattern detection. We visualize the log and help users extract patterns of interest from the log or obtain patterns through unsupervised learning automatically. Pattern matches are visualized in the context of the event log (also showing concurrency and additional contextual information). Earlier patterns can be extended or modified based on the insights. This enables an interactive and iterative approach to identify complex and concrete behavioral patterns in event logs. We implemented our approach in the ProM framework and evaluated the tool using both the BPI Challenge 2012 log of a loan application process and an insurance claims log from a major Australian insurance company
Knowledge is at the Edge! How to Search in Distributed Machine Learning
  Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount
of data is produced at the edge of the network. Due to a lack of computing
power, this data is currently send to the cloud where centralized machine
learning models are trained to derive higher level knowledge. With the recent
development of specialized machine learning hardware for mobile devices, a new
era of distributed learning is about to begin that raises a new research
question: How can we search in distributed machine learning models? Machine
learning at the edge of the network has many benefits, such as low-latency
inference and increased privacy. Such distributed machine learning models can
also learn personalized for a human user, a specific context, or application
scenario. As training data stays on the devices, control over possibly
sensitive data is preserved as it is not shared with a third party. This new
form of distributed learning leads to the partitioning of knowledge between
many devices which makes access difficult. In this paper we tackle the problem
of finding specific knowledge by forwarding a search request (query) to a
device that can answer it best. To that end, we use a entropy based quality
metric that takes the context of a query and the learning quality of a device
into account. We show that our forwarding strategy can achieve over 95%
accuracy in a urban mobility scenario where we use data from 30 000 people
commuting in the city of Trento, Italy.Comment: Published in CoopIS 201
Beyond Volume: The Impact of Complex Healthcare Data on the Machine
  Learning Pipeline

From medical charts to national census, healthcare has traditionally operated
under a paper-based paradigm. However, the past decade has marked a long and
arduous transformation bringing healthcare into the digital age. Ranging from
electronic health records, to digitized imaging and laboratory reports, to
public health datasets, today, healthcare now generates an incredible amount of
digital information. Such a wealth of data presents an exciting opportunity for
integrated machine learning solutions to address problems across multiple
facets of healthcare practice and administration. Unfortunately, the ability to
derive accurate and informative insights requires more than the ability to
execute machine learning models. Rather, a deeper understanding of the data on
which the models are run is imperative for their success. While a significant
effort has been undertaken to develop models able to process the volume of data
obtained during the analysis of millions of digitalized patient records, it is
important to remember that volume represents only one aspect of the data. In
fact, drawing on data from an increasingly diverse set of sources, healthcare
data presents an incredibly complex set of attributes that must be accounted
for throughout the machine learning pipeline. This chapter focuses on
highlighting such challenges, and is broken down into three distinct
components, each representing a phase of the pipeline. We begin with attributes
of the data accounted for during preprocessing, then move to considerations
during model building, and end with challenges to the interpretation of model
output. For each component, we present a discussion around data as it relates
to the healthcare domain and offer insight into the challenges each may impose
on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20
  Pages, 1 Figur
Using jaccard distance measure for unsupervised activity recognition with smartphone accelerometers

The rapid popularity of smartphones has led to a growing research interest in human activity recognition (HAR) with the mobile devices. Accelerometer is the most commonly used sensor of smartphone for HAR. Many supervised HAR methods have been developed. However, it is very difficult to collect the annotated or labeled training data for HAR. So, developing of effective unsupervised methods for HAR is very necessary. The accuracy of an unsupervised method, such as clustering, can be greatly affected by the similarity or distance measures, because the learning process of clustering method is completely depending on the similarity between objects. Although Euclidean distance measure is commonly used in unsupervised activity recognition, it is not suitable for measuring distance when the number of features is very large, which is usually the case in HAR. Jaccard distance is a distance measure based on mutual information theory and can better represent the differences between nonnegative feature vectors than Euclidean distance. It can also work well with a large number of features. In this work, the Jaccard distance measure is applied to HAR for the first time. In the experiments, the results of the Jaccard distance measure and the Euclidean distance measure are compared, using three different feature extraction methods which include time-domain, frequency-domain and mixed-domain feature extractions. To comprehensively analyze the experimental results, two different evaluation methods are used: (a) C-Index before clustering, (b) FM-index after using five different clustering methods which are Spectral Cluster, Single-Linkage, Ward-Linkage, Average-Linkage, and K-Medoids. Experiments show that, almost for every combination of the feature extraction methods and the evaluation methods, the Jaccard distance measure is consistently better than the Euclidean distance measure for unsupervised HAR.This work is supported by the National Science Foundation of China (Grants No. 61272213) and the\ud
Fundamental Research Funds for the Central Universities (Grants No. lzujbky-2016-k07).Peer-reviewedPost-prin
The universal ontology: A vision for conceptual modeling and the semantic web

This paper puts forward a vision of a universal ontology (UO) aiming at solving, or at least greatly alleviating, the semantic integration problem in the field of conceptual modeling and the understandability problem in the field of the semantic web. So far it has been assumed that the UO is not feasible in practice, but we think that it is time to revisit that assumption in the light of the current state-of-the-art. This paper aims to be a step in this direction. We try to make an initial proposal of a feasible UO. We present the scope of the UO, the kinds of its concepts, and the elements that could comprise the specification of each concept. We propose a modular structure for the UO consisting of four levels. We argue that the UO needs a complete set of concept composition operators, and we sketch three of them. We also tackle a few issues related to the feasibility of the UO, which we think that they could be surmountable. Finally, we discuss the desirability of the UO, and we explain why we conjecture that there are already organizations that have the knowledge and resources needed to develop it, and that might have an interest in its development in the near future.Peer ReviewedPostprint (author's final draft
Conceptual modeling for genomics: Building an integrated repository of open data

Many repositories of open data for genomics, collected by world-wide consortia, are important enablers of biological research; moreover, all experimental datasets leading to publications in genomics must be deposited to public repositories and made available to the research community. These datasets are typically used by biologists for validating or enriching their experiments; their content is documented by metadata. However, emphasis on data sharing is not matched by accuracy in data documentation; metadata are not standardized across the sources and often unstructured and incomplete. In this paper, we propose a conceptual model of genomic metadata, whose purpose is to query the underlying data sources for locating relevant experimental datasets. First, we analyze the most typical metadata attributes of genomic sources and define their semantic properties. Then, we use a top-down method for building a global-as-view integrated schema, by abstracting the most important conceptual properties of genomic sources. Finally, we describe the validation of the conceptual model by mapping it to three well-known data sources: TCGA, ENCODE, and Gene Expression Omnibus
OCLuniv: Expressive UML/OCL conceptual schemas for finite reasoning

Full UML/OCL is so expressive that most reasoning tasks are known to be undecidable in schemas defined with these languages. To tackle this situation, literature has proposed mainly three decidable fragments of UML/OCL: UML with no OCL, UML with limited OCL and no maximum cardinality constraints (OCL-Lite), and UML with limited OCL with no minimum cardinality constraints (OCL  UNIVUNIV ). Since most conceptual schemas make use of OCL together with min and max cardinalities, this poses a strong limitation to current proposals. In this paper, we go beyond these limits by showing that OCL  UNIVUNIV  with acyclic min cardinality constraints and path acyclicity constraints also preserves decidability. In this way, we establish a language that can deal with most of UML/OCL identified constraint patterns. We also empirically test the expressiveness of this language through different UML/OCL case studies.Peer ReviewedPostprint (author's final draft
A catalogue of reusable context model elements based on the i* framework

The definition of the context of a system is one of the most relevant activities in the early phases of information systems engineering. It allows system engineers to narrow the system scope, by defining well established system boundaries. In practice, outlining a system context model is complex and cumbersome. In order to support context modeling, in this paper we propose a catalogue of context model elements expressed in i*, which can be reused as building blocks in the construction of context models for new systems. We describe the process used for the identification of a set of actors and dependencies recurrently appearing in several academic and industrial cases, and the process to store them into a catalogue of reusable i* context dependencies.Peer ReviewedPostprint (author's final draft
Should I stay or should I go? : On forces that drive and prevent MBSE adoption in the embedded systems industry

[Context] Model-based Systems Engineering (MBSE) comprises a set of models and techniques that is often suggested as solution to cope with the challenges of engineering complex systems. Although many practitioners agree with the arguments on the potential benefits of the techniques, companies struggle with the adoption of MBSE. [Goal] In this paper, we investigate the forces that prevent or impede the adoption of MBSE in companies that develop embedded software systems. We contrast the hindering forces with issues and challenges that drive these companies towards introducing MBSE. [Method] Our results are based on 20 interviews with experts from 10 companies. Through exploratory research, we analyze the results by means of thematic coding. [Results] Forces that prevent MBSE adoption mainly relate to immature tooling, uncertainty about the return-on-investment, and fears on migrating existing data and processes. On the other hand, MBSE adoption also has strong drivers and participants have high expectations mainly with respect to managing complexity, adhering to new regulations, and reducing costs. [Conclusions] We conclude that bad experiences and frustration about MBSE adoption originate from false or too high expectations. Nevertheless, companies should not underestimate the necessary efforts for convincing employees and addressing their anxiety
A Study of the Scrum Master's Role

Scrum is an increasingly common approach to software development adopted by
organizations around the world. However, as organizations transition from
traditional plan-driven development to agile development with Scrum, the
question arises as to which Scrum role (Product Owner, Scrum Master, or Scrum
Team Member) corresponds to a Project Manager, or conversely which Scrum role
should the Project Managers adopt? In an attempt to answer this question, we
adopted a mixed-method research approach comprising a systematic literature
review and embedded case study of a commercial software development team. Our
research has identified activities that comprise the Scrum Master role, and
which additional roles are actually performed by Scrum Masters in practice. We
found nine activities that are performed by Scrum Masters. In addition, we
found that Scrum Masters also perform other roles, most importantly as Project
Managers. This latter situation results in tension and conflict of interest
that could have a negative impact on the performance of the team as a whole.
These results point to the need to re-assess the role of Project Managers in
organizations that adopt Scrum as a development approach. We hypothesize that
it might be better for Project Managers to become Product Owners, as aspects of
this latter role are more consistent with the traditional responsibilities of a
Project Manager.Comment: 16 pages, 1 figure, PROFES 201
An Exploratory Study of Applying a Scrum Development Process for
  Safety-Critical Systems

Agile techniques recently have received attention in developing
safety-critical systems. However, a lack of empirical knowledge of performing
safety assurance techniques in practice, especially safety analysis into agile
development processes prevents further steps. In this article, we aim at
investigating the feasibility and the effects of our S-Scrum development
process, and stepwise improving and proposing an Optimized S-Scrum development
process for safety-critical systems in a real environment. We conducted an
exploratory case study in a one-year student project "Smart Home" at the
University of Stuttgart, Germany. We participated in the project and collected
quantitative and qualitative data from questionnaire, interviews, participant
observation, physical artifacts, and documentation review. Furthermore, we
evaluated the Optimized S-Scrum in industry by conducting interviews. The
first-stage results showed that by integrating STPA (System-Theoretic Process
Analysis) can ensure the safety during each sprint and enhance the safety of
delivered products, while the agility of S-Scrum is slightly worse than the
original Scrum. Six challenges have been explored: Management changes the
team's priorities during an iteration; Disturbed safety-related communication;
Non-functional requirements are determined too late; Insufficient upfront
planning; Insufficient well-defined completion criteria; Excessive time to
perform upfront planning. We investigated further the causalities and
optimizations. The second-stage results revealed that the safety and agility
have been improved after the optimizations. We have gained a positive
assessment and suggestions from industry. The optimized S-Scrum is feasible for
developing safety-critical systems concerning the capability to ensure safety
and the acceptable agility in a student project. Further attempt is still
needed in industrial projects.Comment: 16 pages, 4 figure
Non-functional requirements documentation in agile software development: challenges and solution proposal

Non-functional requirements (NFRs) are determinant for the success of software projects. However, they are characterized as hard to define, and in agile software development (ASD), are often given less priority and usually not documented. In this paper, we present the findings of the documentation practices and challenges of NFRs in companies utilizing ASD and propose guidelines for enhancing NFRs documentation in ASD. We interviewed practitioners from four companies and identified that epics, features, user stories, acceptance criteria, Definition of Done (DoD), product and sprint backlogs are used for documenting NFRs. Wikis, word documents, mockups and spreadsheets are also used for doc-umenting NFRs. In smaller companies, NFRs are communicated through white board and flip chart discussions and developers’ tacit knowledge is prioritized over documentation. However, loss of traceability of NFRs, the difficulty in com-prehending NFRs by new developers joining the team and limitations of docu-mentation practices for NFRs are challenges in ASD. In this regard, we propose guidelines for documenting NFRs in ASD. The proposed guidelines consider the diversity of the NFRs to document and suggest different representation artefacts depending on the NFRs scope and level of detail. The representation artefacts suggested are among those currently used in ASD in order not to introduce new specific ones that might hamper actual adoption by practitioners.Peer ReviewedPostprint (author's final draft
Optimization of asteroid capture missions using Earth resonant encounters

This paper describes a robust methodology to design Earth-resonant asteroid capture trajectories leading to Libration Point Orbits (LPOs). These trajectories consider two impulsive manoeuvres; one occurring before the first Earth encounter and a final one that inserts the asteroid into a stable hyperbolic manifold trajectory leading to an LPO of the Sun-Earth system. The first manoeuvre is key to exploit the chaotic perturbative effects of the Earth and obtain important reductions on the cost of inserting the asteroid into a manifold trajectory. The perturbative effects caused by the Earth are here modelled by means of a Keplerian Map approximation, and these are a posteriori compared with the dynamics of the Circular Restricted Three-Body Problem. Savings in the order of 50% of total Δv are computed for four different asteroids