pinecone/core-2020-05-10-deduplication huggingface dataset:
One step towards automatic inference of formal specification using Automated VeriFast

VeriFast is a sound modular formal verification tool for C and Java programs. Based on separation logic and using symbolic execution, VeriFast can verify not only memory safety of programs but also full functional correctness. Formal verification is a powerful way of analyzing code, but not yet widely used in practice. Source code has to be annotated with formal specification mostly in the form of function preconditions and postconditions. In this paper, we present Automated VeriFast which is a new extension or an automation layer that lies on top of VeriFast that, given a partially annotated program, offers to attempt to incrementally improve the annotations, e.g. by inferring a fix to the specification of a program fragment that fails to verify. Our thesis is that such small, interactive inference steps will have practical benefits over non-interactive specification inference approaches by allowing the user to guide the inference process and by being simpler and therefore more predictable and diagnosable.status: publishe
Towards the Automated Verification of Weibull Distributions for System Failure Rates

Weibull distributions can be used to accurately model failure
behaviours of a wide range of critical systems such as on-orbit satellite
subsystems. Markov chains have been used extensively to model reliability
and performance of engineering systems or applications. However,
the exponentially distributed sojourn time of Continuous-Time Markov
Chains (CTMCs) can sometimes be unrealistic for satellite systems that
exhibit Weibull failures. In this paper, we develop novel semi-Markov
models that characterise failure behaviours, based on Weibull failure
modes inferred from realistic data sources. We approximate and encode
these new models with CTMCs and use the PRISM probabilistic model
checker. The key bene t of this integration is that CTMC-based model
checking tools allow us to automatically and e ciently verify reliability
properties relevant to industrial critical systems
Non-trivial reputation effects on social decision making in virtual
  environment

Reputation systems are currently used, often with success, to ensure the
functioning of online services as well as of e-commerce sites. Despite the
relationship between reputation and material cooperative behaviours is quite
supported, less obvious appears the relationship with informative behaviours,
which are crucial for the transmission of reputational information and
therefore for the maintenance of cooperation among individuals. The purpose of
this study was to verify how reputation affects cooperation dynamics in virtual
environment, within a social dilemma situation (i.e., where there are
incentives to act selfishly). The results confirm that reputation can activate
prosocial conducts, however it highlights also the limitations and distortions
that reputation can create
Privacy through Anonymisation in Large-scale Socio-technical Systems: Multi-lingual Contact Centres across the EU

Large-scale socio-technical systems (STS) inextricably interconnect individual – e.g., the right to privacy –, social – e.g., the eﬀectiveness of organisational processes –, and technology issues —e.g., the software engineering process. As a result, the design of the complex software infrastructure involves also non-technological aspects such as the legal ones—so that, e.g., law-abidingness can be ensured since the early stages of the software engineering process.\ud
By focussing on contact centres (CC) as relevant examples of knowledge-intensive STS, we elaborate on the articulate aspects of anonymisation: there, individual and organisational needs clash, so that only an accurate balancing between legal and technical aspects could possibly ensure the system eﬃciency while preserving the individual right to privacy. We discuss ﬁrst the overall legal framework, then the general theme of anonymisation in CC. Finally we overview the technical process developed\ud
in the context of the BISON project
Characterizing word problems of groups

The word problem of a finitely generated group is a fundamental notion in group theory; it can be defined as the set of all the words in the generators of the group that represent the identity element of the group. This definition allows us to consider a word problem as a formal language and a rich topic of research concerns the connection between the complexity of this language and the algebraic structure of the corresponding group.\ud
Another interesting problem is that of characterizing which languages are word problems of groups. There is a known necessary and sufficient criterion for a language to be a word problem of a group; however a natural question is what other characterizations there are. In this paper we investigate this question, using sentences expressed in first-order logic where the relations we consider are membership of the language in question and concatenation of words. We choose some natural conditions that apply to word problems and then characterize which sets of these conditions are sufficient to guarantee that the language in question really is the word problem of a group. We finish by investigating the decidability of these conditions for the families of regular and one-counter languages.This paper was completed whilst the second author was on study leave from\ud
the University of Leicester and he would like to acknowledge the help and support\ud
of the university in this respect.Peer-reviewedPost-print10th International Workshop, RP 2016, Aalborg, Denmar
Lectures on Matrix Field Theory I

The subject of matrix field theory involves matrix models, noncommutative
geometry, fuzzy physics and noncommutative field theory and their interplay. In
these lectures, a lot of emphasis is placed on the matrix formulation of
noncommutative and fuzzy spaces, and on the non-perturbative treatment of the
corresponding field theories. In particular, the phase structure of
noncommutative $\phi^4$ theory is treated in great detail, and an introduction
to noncommutative gauge theory is given.Comment: 293 pages, 10 figures. Several small parts of these lectures have
  already appeared in various preprints found on the arXiv. I have felt no need
  to reword my own expressions if I thought they conveyed the physics properl
What I really want from this course is庠Tailoring Learning to Meet Students⠎eeds, using Pedagogies of Connection and Engagement

A problematic shift in culture has seen the focus of Western universities move from the collective pursuit of wisdom to a pre-packaging of professional knowledge and an emphasis on profits, accountability and student throughput (Connell, The good university. Paper presented at the meeting of the NTEU, Griffith University, Brisbane, Australia, 2015). This commodification of education can manifest in classrooms as reductive pedagogies of uniformity, conformity and knowledge transmission which impact on student satisfaction, attendance and teacher/student rapport. This chapter documents my attempts at resisting these pedagogies in an undergraduate Secondary English curriculum course. I set about building rapport with students by fostering democratic spaces where all were permitted a voice in designing the learning agenda. Students were encouraged to lead discussions, suggest tutorial content/foci and emotionally “check in” with their tutor. Student feedback and increased attendance rates indicate that there is value to be had in listening authentically before the talking begins.Arts, Education & Law Group, School of Education and Professional StudiesFull Tex
Lessons from the postal sector to telecommunications and vice versa

The arrival and diffusion of the commercial Internet has been one of the main causes of change in the postal sector in the last two decades. Its impact has been rapid and it has gone straight to the core of postal operators’ (POs’) activities: the mail business. Electronic communications, especially email, available at zero marginal cost and relatively high quality, have quickly changed consumers’ and businesses’ communicating habits, leading to substitution away from traditional physical mails. Although substantial postal volumes remain, for reasons that will later be discussed, it has certainly hit the sector’s volume and profitability dramatically
Maximal Unbordered Factors of Random Strings

A border of a string is a non-empty prefix of the string that is also a
suffix of the string, and a string is unbordered if it has no border other than
itself. Loptev, Kucherov, and Starikovskaya [CPM 2015] conjectured the
following: If we pick a string of length $n$ from a fixed non-unary alphabet
uniformly at random, then the expected maximum length of its unbordered factors
is $n - O(1)$. We confirm this conjecture by proving that the expected value
is, in fact, ${n - \Theta(\sigma^{-1})}$, where $\sigma$ is the size of the
alphabet. This immediately implies that we can find such a maximal unbordered
factor in linear time on average. However, we go further and show that the
optimum average-case running time is in $\Omega (\sqrt{n}) \cap O (\sqrt{n
\log_\sigma n})$ due to analogous bounds by Czumaj and G\k{a}sieniec [CPM 2000]
for the problem of computing the shortest period of a uniformly random string.Comment: A preliminary version with weaker results was presented at the 23rd
  Symposium on String Processing and Information Retrieval (SPIRE '16
A New Tableau-based Satisfiability Checker for Linear Temporal Logic

Tableaux-based  methods  were  among  the  first  techniques proposed for Linear Temporal Logic satisfiability checking. The earliest tableau for LTL by [21] worked by constructing a graph whose path represented possible models for the formula, and then searching for an actual model among those paths. Subsequent developments led to the tree-like tableau by [17], which works by building a structure similar to an actual search tree, which however still has back-edges and needs multiple passes to assess the existence of a model. This paper summarizes\ud
theworkdoneonanewtool for LTL satisfiability checking based on a novel tableau method. The new tableau construction, which is very simple and easy to explain, builds an actually tree-shaped structure and it only requires a single pass to decide whether to accept a given branch or not. The implementation has been compared in terms of speed and memory consumption with tools implementing both existing tableau methods and different satisfiability techniques, showing good results despite the simplicity of the underlying algorithm
Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to
  OpenPower and Beyond

With the appearance of the heterogeneous platform OpenPower,many-core
accelerator devices have been coupled with Power host processors for the first
time. Towards utilizing their full potential, it is worth investigating
performance portable algorithms that allow to choose the best-fitting hardware
for each domain-specific compute task. Suiting even the high level of
parallelism on modern GPGPUs, our presented approach relies heavily on abstract
meta-programming techniques, which are essential to focus on fine-grained
tuning rather than code porting. With this in mind, the CUDA-based open-source
plasma simulation code PIConGPU is currently being abstracted to support the
heterogeneous OpenPower platform using our fast porting interface cupla, which
wraps the abstract parallel C++11 kernel acceleration library Alpaka. We
demonstrate how PIConGPU can benefit from the tunable kernel execution
strategies of the Alpaka library, achieving portability and performance with
single-source kernels on conventional CPUs, Power8 CPUs and NVIDIA GPUs.Comment: 9 pages, 3 figures, accepted on IWOPH 201
Electromagnetic Transmission of Intellectual Property Data to Protect FPGA Designs

International audienceOver the past 10 years, the designers of intellectual properties(IP) have faced increasing threats including cloning, counterfeiting, andreverse-engineering. This is now a critical issue for the microelectronicsindustry. The design of a secure, efficient, lightweight protection scheme fordesign data is a serious challenge for the hardware security community. In thiscontext, this chapter presents two ultra-lightweight transmitters using sidechannel leakage based on electromagnetic emanation to send embedded IPidentity discreetly and quickl
Fibre Distribution and the Process-Property Dilemma

The options for the fibre reinforcement of polymer matrix composites cover a range from short-fibre chopped strand mat, through woven fabric to unidirectional pre-impregnated (prepreg) reinforcements. The modelling of such materials may be simplified by assumptions such as perfect regular packing of fibres and the total absence of fibre waviness. However, these and other features such as the crimp or waviness in woven fabrics make real materials more complex than the simplified models. Clustering of fibres creates fibre-rich and resin-rich volumes (FRV and RRV respectively) in the composites. Prior to impregnation, large RRV will be pore-space that can expedite the flow of resin in liquid composite moulding processes (especially resin transfer moulding (RTM) and resin infusion under flexible tooling (RIFT). In the composite, the clustering of fibres tends to reduce the mechanical properties. The use of image processing and analysis can permit micro-/meso-structural characterisation which may correlate to the respective properties. This chapter considers the quantification of microstructure images in the context of the process-property dilemma for woven carbon-fibre reinforced composites with the aim of increasing understanding of the balance between processability and mechanical performance
A tool for subjective and interactive visual data exploration

We present SIDE, a tool for Subjective and Interactive Visual Data Exploration, which lets users explore high dimensional data via subjectively informative 2D data visualizations. Many existing visual analytics tools are either restricted to specific problems and domains or they aim to find visualizations that align with user’s belief about the data. In contrast, our generic tool computes data visualizations that are surprising given a user’s current understanding of the data. The user’s belief state is represented as a set of projection tiles. Hence, this user-awareness offers users an efficient way to interactively explore yet-unknown features of complex high dimensional datasets
Learning Language Models from Images with ReGLL

International audienceIn this demonstration, we present ReGLL, a system that is able to learn language models taking into account the perceptual context in which the sentences of the model are produced. Thus, ReGLL learns from pairs (Context, Sentence) where: Context is given in the form of an image whose objects have been identified, and Sentence gives a (partial) description of the image. ReGLL uses Inductive Logic Programming Techniques and learns some mappings between n-grams and first order representations of their meanings. The demonstration shows some applications of the language models learned, such as generating relevant sentences describing new images given by the user and translating some sentences from one language to another without the need of any parallel corpus
Coordinate transformations for characterization and cluster analysis of spatial configurations in football

Current technologies allow movements of the players and the ball in football matches to be tracked and recorded with high accuracy and temporal frequency. We demonstrate an approach to analyzing football data with the aim to find typical patterns of spatial arrangement of the field players. It involves transformation of original coordinates to relative positions of the players and the ball with respect to the center and attack vector of each team. From these relative positions, we derive features for characterizing spatial configurations in different time steps during a football game. We apply clustering to these features, which groups the spatial configurations by similarity. By summarizing groups of similar configurations, we obtain representation of spatial arrangement patterns practiced by each team. The patterns are represented visually by density maps built in the teams’ relative coordinate systems. Using additional displays, we can investigate under what conditions each pattern was applied
Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics

By applying spatio-temporal aggregation to traffic data consisting of vehicle trajectories, we generate a spatially abstracted transportation network, which is a directed graph where nodes stand for territory compartments (areas in geographic space) and links (edges) are abstractions of the possible paths between neighboring areas. From time series of traffic characteristics obtained for the links, we reconstruct mathematical models of the interdependencies between the traffic intensity (a.k.a. traffic flow or flux) and mean velocity. Graphical representations of these interdependencies have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. We present the process of data-driven generation of traffic forecasting and simulation models, in which each step is supported by visual analytics techniques
DANCer: Dynamic Attributed Network with Community Structure Generator

International audienceWe propose a new generator for dynamic attributed networks with community structure which follow the known properties of real-world networks such as preferential attachment, small world and ho-mophily. After the generation, the different graphs forming the dynamic network as well as its evolution can be displayed in the interface. Several measures are also computed to evaluate the properties verified by each graph. Finally, the generated dynamic network, the parameters and the measures can be saved as a collection of files
Shape models and physical properties of asteroids

Despite the large amount of high quality data generated in recent space
encounters with asteroids, the majority of our knowledge about these objects
comes from ground based observations. Asteroids travelling in orbits that are
potentially hazardous for the Earth form an especially interesting group to be
studied. In order to predict their orbital evolution, it is necessary to
investigate their physical properties. This paper briefly describes the data
requirements and different techniques used to solve the lightcurve inversion
problem. Although photometry is the most abundant type of observational data,
models of asteroids can be obtained using various data types and techniques. We
describe the potential of radar imaging and stellar occultation timings to be
combined with disk-integrated photometry in order to reveal information about
physical properties of asteroids.Comment: From Assessment and Mitigation of Asteroid Impact Hazards boo
Objectness Scoring and Detection Proposals in Forward-Looking Sonar
  Images with Convolutional Neural Networks

Forward-looking sonar can capture high resolution images of underwater
scenes, but their interpretation is complex. Generic object detection in such
images has not been solved, specially in cases of small and unknown objects. In
comparison, detection proposal algorithms have produced top performing object
detectors in real-world color images. In this work we develop a Convolutional
Neural Network that can reliably score objectness of image windows in
forward-looking sonar images and by thresholding objectness, we generate
detection proposals. In our dataset of marine garbage objects, we obtain 94%
recall, generating around 60 proposals per image. The biggest strength of our
method is that it can generalize to previously unseen objects. We show this by
detecting chain links, walls and a wrench without previous training in such
objects. We strongly believe our method can be used for class-independent
object detection, with many real-world applications such as chain following and
mine detection.Comment: Author versio
Building ensembles of adaptive nested dichotomies with random-pair selection

A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Although ensembles of nested dichotomies with random structure have been shown to perform well in practice, using a more sophisticated class subset selection method can be used to improve classification accuracy. We investigate an approach to this problem called random-pair selection, and evaluate its effectiveness compared to other published methods of subset selection. We show that our method outperforms other methods in many cases when forming ensembles of nested dichotomies, and is at least on par in all other cases. The software related to this paper is available at https://svn.cms.waikato.ac.nz/svn/weka/trunk/packages/ internal/ensemblesOfNestedDichotomies/
Interactive visual data exploration with subjective feedback

Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, integrated methods and tools that combine advanced visualization and data mining techniques are rare, and those that exist are often specialized to a single problem or domain. In this paper, we introduce a novel generic method for interactive visual exploration of high-dimensional data. In contrast to most visualization tools, it is not based on the traditional dogma of manually zooming and rotating data. Instead, the tool initially presents the user with an ‘interesting’ projection of the data and then employs data randomization with constraints to allow users to flexibly and intuitively express their interests or beliefs using visual interactions that correspond to exactly defined constraints. These constraints expressed by the user are then taken into account by a projection-finding algorithm to compute a new ‘interesting’ projection, a process that can be iterated until the user runs out of time or finds that constraints explain everything she needs to find from the data. We present the tool by means of two case studies, one controlled study on synthetic data and another on real census data. The data and software related to this paper are available at http://​www.​interesting-patterns.​net/​forsied/​interactive-visual-data-exploration-with-subjective-feedback/​