bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trauma and Migration
Authors: Theisen-Womersley Gail 
Published: March 2021

AbstractOver the past two decades, there has been an increasing interest in the question of trauma among refugee populations. This body of research has largely focused on the immediate psychological aftermaths of armed conflicts in light of the well-described associations between these psychiatric disorders, displacement, and generalized forms of violence (Morina et al., Morina et al., .Frontiers in Psychiatry 9:433, 2018). In general, the literature attests to the greater mental health difficulties among refugees compared to general populations within host communities.
Beyond PTSD
Authors: Theisen-Womersley Gail 
Published: March 2021

AbstractPTSD as a disorder was first introduced as a diagnosis by the American Psychiatric Association (American Psychiatric Association (APA), Diagnostic and statistical manual of mental disorders 1980) in the DSM III in 1980, with interest in it booming to such an extent thereafter that it was referred to in mass media as “the disorder of the 1990s” (Marsella et al., Ethnocultural aspects of posttraumatic stress disorder: Issues, research, and clinical applications, 1996).
Collective Resilience and Imagination
Authors: Theisen-Womersley Gail 
Published: March 2021

AbstractWhile indeed the stories of forced displacement are often unfathomably difficult and layered with sometimes profound and traumatizing obstacles, they too are often stories of hope and human triumph (Neace et al. in Handbook of refugee experience: Trauma resilience and recovery. California Cognella Academic Publishing, San Diego, pp. 9–20, 2020).
Working with Shame and Trauma
Authors: Theisen-Womersley Gail 
Published: March 2021

AbstractNot only does the literature attest to the high levels of trauma among displaced populations, research in the past decade has increasingly revealed the hidden yet pervasive role that shame may play in posttraumatic symptomatology. As defined by Wilson and colleagues, “in the posttraumatic self, shame develops from traumatic experiences that render the victim fearful, powerless, helpless, and unable to act congruently with moral values” (Wilson et al., 2006, p. 127). In the context of forced migration in particular, both trauma and shame are ubiquitous, pervasive, and contagious.
Becoming Open Knowledge Institutions: Divergence, Dialogue and Diversity
Authors: Wilson Katie , Montgomery Lucy , Neylon Cameron , Handcock Rebecca N. , Hosking Richard , Huang Chun-Kai (Karl) , Ozaygen Alkim , Roelofs Aniek 
Published: March 2021

AbstractThe Curtin Open Knowledge Initiative (COKI) is an innovative research project that collects and analyses publicly available research output data to assist and encourage researchers, academics, administrators and executives to understand the actual and potential reach of openness in research, and to assess their progress on the path towards open knowledge institutions. By taking a broad global approach and using multiple data sources, the project diverges from existing approaches, methods and bibliometric measures in the scholarly research environment. It combines analysis of research output, citations, publication sources and publishers, funders, social media events, open and not open access to provide overviews of research output and performance at institutional, funder, consortial and country levels. The project collects and analyses personnel diversity data such as gender, focusing on widening the reach of data analysis to emphasise the importance and value of diversity in research and knowledge production. Interactive visual tools present research output and performance to encourage understanding and dialogue among researchers and management. The path towards becoming open knowledge institutions involves a process of cultural change, moving beyond dominant publishing and evaluation practices. This paper discusses how through divergence, diversity and dialogue the COKI project can contribute to this change, with examples of applications in understanding and embracing openness.
On Benchmarking for Concurrent Runtime Verification
Authors: Aceto Luca , Attard Duncan Paul , Francalanza Adrian , Ingólfsdóttir Anna 
Published: March 2021

AbstractWe present a synthetic benchmarking framework that targets the systematic evaluation of RV tools for message-based concurrent systems. Our tool can emulate various load profiles via configuration. It provides a multi-faceted view of measurements that is conducive to a comprehensive assessment of the overhead induced by runtime monitoring. The tool is able to generate significant loads to reveal edge case behaviour that may only emerge when the monitoring system is pushed to its limit. We evaluate our framework in two ways. First, we conduct sanity checks to assess the precision of the measurement mechanisms used, the repeatability of the results obtained, and the veracity of the behaviour emulated by our synthetic benchmark. We then showcase the utility of the features offered by our tool in a two-part RV case study.
Keeping Pace with the History of Evolving Runtime Models
Authors: Sakizloglou Lucas , Barkowsky Matthias , Giese Holger 
Published: March 2021

AbstractStructural runtime models provide a snapshot of the constituents of a system and their state. Capturing the history of runtime models, i.e., previous snapshots, has been shown to be useful for a number of aims. Handling, however, history at runtime poses important challenges to tool support. We present the InTempo tool which is based on the Eclipse Modeling Framework and encodes runtime models as graphs. Key features of InTempo, such as, the integration of temporal requirements into graph queries, the in-memory storage of the model, and a systematic method to contain the model’s memory consumption, intend to address issues which seemingly place limitations on the available tool support. InTempo offers two operation modes which support both runtime and postmortem application scenarios.
SpecTest: Specification-Based Compiler Testing
Authors: Schumi Richard , Sun Jun 
Published: March 2021

AbstractCompilers are error-prone due to their high complexity. They are relevant for not only general purpose programming languages, but also for many domain specific languages. Bugs in compilers can potentially render all programs at risk. It is thus crucial that compilers are systematically tested, if not verified. Recently, a number of efforts have been made to formalise and standardise programming language semantics, which can be applied to verify the correctness of the respective compilers. In this work, we present a novel specification-based testing method named SpecTest to better utilise these semantics for testing. By applying an executable semantics as test oracle, SpecTest can discover deep semantic errors in compilers. Compared to existing approaches, SpecTest is built upon a novel test coverage criterion called semantic coverage which brings together mutation testing and fuzzing to specifically target less tested language features. We apply SpecTest to systematically test two compilers, i.e., the Java compiler and the Solidity compiler. SpecTest improves the semantic coverage of both compilers considerably and reveals multiple previously unknown bugs.
Understanding Local Robustness of Deep Neural Networks under Natural Variations
Authors: Zhong Ziyuan , Tian Yuchi , Ray Baishakhi 
Published: March 2021

AbstractDeep Neural Networks (DNNs) are being deployed in a wide range of settings today, from safety-critical applications like autonomous driving to commercial applications involving image classifications. However, recent research has shown that DNNs can be brittle to even slight variations of the input data. Therefore, rigorous testing of DNNs has gained widespread attention.While DNN robustness under norm-bound perturbation got significant attention over the past few years, our knowledge is still limited when natural variants of the input images come. These natural variants, e.g., a rotated or a rainy version of the original input, are especially concerning as they can occur naturally in the field without any active adversary and may lead to undesirable consequences. Thus, it is important to identify the inputs whose small variations may lead to erroneous DNN behaviors. The very few studies that looked at DNN’s robustness under natural variants, however, focus on estimating the overall robustness of DNNs across all the test data rather than localizing such error-producing points. This work aims to bridge this gap.To this end, we study the local per-input robustness properties of the DNNs and leverage those properties to build a white-box (DeepRobust-W) and a black-box (DeepRobust-B) tool to automatically identify the non-robust points. Our evaluation of these methods on three DNN models spanning three widely used image classification datasets shows that they are effective in flagging points of poor robustness. In particular, DeepRobust-W and DeepRobust-B are able to achieve an F1 score of up to 91.4% and 99.1%, respectively. We further show that DeepRobust-W can be applied to a regression problem in a domain beyond image classification. Our evaluation on three self-driving car models demonstrates that DeepRobust-W is effective in identifying points of poor robustness with F1 score up to 78.9%.
Status Report on Software Testing: Test-Comp 2021
Authors: Beyer Dirk 
Published: March 2021

AbstractThis report describes Test-Comp 2021, the 3rd edition of the Competition on Software Testing. The competition is a series of annual comparative evaluations of fully automatic software test generators for C programs. The competition has a strong focus on reproducibility of its results and its main goal is to provide an overview of the current state of the art in the area of automatic test-generation. The competition was based on 3 173 test-generation tasks for C programs. Each test-generation task consisted of a program and a test specification (error coverage, branch coverage). Test-Comp 2021 had 11 participating test generators from 6 countries.
FuSeBMC: A White-Box Fuzzer for Finding Security Vulnerabilities in C Programs (Competition Contribution)
Authors: Alshmrany Kaled M. , Menezes Rafael S. , Gadelha Mikhail R. , Cordeiro Lucas C. 
Published: March 2021

AbstractWe describe and evaluate a novel white-box fuzzer for C programs named , which combines fuzzing and symbolic execution, and applies Bounded Model Checking (BMC) to find security vulnerabilities in C programs. explores and analyzes C programs (1) to find execution paths that lead to property violations and (2) to incrementally inject labels to guide the fuzzer and the BMC engine to produce test-cases for code coverage. successfully participates in Test-Comp’21 and achieves first place in the category and second place in the category.
A Decision Tree Lifted Domain for Analyzing Program Families with Numerical Features
Authors: Dimovski Aleksandar S. , Apel Sven , Legay Axel 
Published: March 2021

AbstractLifted (family-based) static analysis by abstract interpretation is capable of analyzing all variants of a program family simultaneously, in a single run without generating any of the variants explicitly. The elements of the underlying lifted analysis domain are tuples, which maintain one property per variant. Still, explicit property enumeration in tuples, one by one for all variants, immediately yields combinatorial explosion. This is particularly apparent in the case of program families that, apart from Boolean features, contain also numerical features with large domains, thus giving rise to astronomical configuration spaces.The key for an efficient lifted analysis is a proper handling of variability-specific constructs of the language (e.g., feature-based runtime tests and $$\texttt {\#if}$$

#
if

directives). In this work, we introduce a new symbolic representation of the lifted abstract domain that can efficiently analyze program families with numerical features. This makes sharing between property elements corresponding to different variants explicitly possible. The elements of the new lifted domain are constraint-based decision trees, where decision nodes are labeled with linear constraints defined over numerical features and the leaf nodes belong to an existing single-program analysis domain. To illustrate the potential of this representation, we have implemented an experimental lifted static analyzer, called SPLNum$$^2$$

2

Analyzer, for inferring invariants of C programs. An empirical evaluation on BusyBox and on benchmarks from SV-COMP yields promising preliminary results indicating that our decision trees-based approach is effective and outperforms the baseline tuple-based approach.
A Game for Linear-time–Branching-time Spectroscopy
Authors: Bisping Benjamin , Nestmann Uwe 
Published: March 2021

AbstractWe introduce a generalization of the bisimulation game that can be employed to find all relevant distinguishing Hennessy–Milner logic formulas for two compared finite-state processes. By measuring the use of expressive powers, we adapt the formula generation to just yield formulas belonging to the coarsest distinguishing behavioral preorders/equivalences from the linear-time–branching-time spectrum. The induced algorithm can determine the best fit of (in)equivalences for a pair of processes.
Finding Provably Optimal Markov Chains
Authors: Spel Jip , Junges Sebastian , Katoen Joost-Pieter 
Published: March 2021

AbstractParametric Markov chains (pMCs) are Markov chains with symbolic (aka: parametric) transition probabilities. They are a convenient operational model to treat robustness against uncertainties. A typical objective is to find the parameter values that maximize the reachability of some target states. In this paper, we consider automatically proving robustness, that is, an$$\varepsilon $$ε-close upper bound on the maximal reachability probability. The result of our procedure actually provides an almost-optimal parameter valuation along with this upper bound.We propose to tackle these ETR-hard problems by a tight combination of two significantly different techniques: monotonicity checking and parameter lifting. The former builds a partial order on states to check whether a pMC is (local or global) monotonic in a certain parameter, whereas parameter lifting is an abstraction technique based on the iterative evaluation of pMCs without parameter dependencies. We explain our novel algorithmic approach and experimentally show that we significantly improve the time to determine almost-optimal synthesis.
Multi-objective Optimization of Long-run Average and Total Rewards
Authors: Quatmann Tim , Katoen Joost-Pieter 
Published: March 2021

AbstractThis paper presents an efficient procedure for multi-objective model checking of long-run average reward (aka: mean pay-off) and total reward objectives as well as their combination. We consider this for Markov automata, a compositional model that captures both traditional Markov decision processes (MDPs) as well as a continuous-time variant thereof. The crux of our procedure is a generalization of Forejt et al.’s approach for total rewards on MDPs to arbitrary combinations of long-run and total reward objectives on Markov automata. Experiments with a prototypical implementation on top of the Storm model checker show encouraging results for both model types and indicate a substantial improved performance over existing multi-objective long-run MDP model checking based on linear programming.
Iterative Bounded Synthesis for Efficient Cycle Detection in Parametric Timed Automata
Authors: André Étienne , Arias Jaime , Petrucci Laure , Pol Jaco van de 
Published: March 2021

AbstractWe study semi-algorithms to synthesise the constraints under which a Parametric Timed Automaton satisfies some liveness requirement. The algorithms traverse a possibly infinite parametric zone graph, searching for accepting cycles. We provide new search and pruning algorithms, leading to successful termination for many examples. We demonstrate the success and efficiency of these algorithms on a benchmark. We also illustrate parameter synthesis for the classical Bounded Retransmission Protocol. Finally, we introduce a new notion of completeness in the limit, to investigate if an algorithm enumerates all solutions.
Synthesizing Context-free Grammars from Recurrent Neural Networks
Authors: Yellin Daniel M. , Weiss Gail 
Published: March 2021

AbstractWe present an algorithm for extracting a subclass of the context free grammars (CFGs) from a trained recurrent neural network (RNN). We develop a new framework,pattern rule sets(PRSs), which describe sequences of deterministic finite automata (DFAs) that approximate a non-regular language. We present an algorithm for recovering the PRS behind a sequence of such automata, and apply it to the sequences of automata extracted from trained RNNs using the$$L^{*}$$L∗algorithm. We then show how the PRS may converted into a CFG, enabling a familiar and useful presentation of the learned language.Extracting the learned language of an RNN is important to facilitate understanding of the RNN and to verify its correctness. Furthermore, the extracted CFG can augment the RNN in classifying correct sentences, as the RNN’s predictive accuracy decreases when the recursion depth and distance between matching delimiters of its input sequences increases.
Automated and Formal Synthesis of Neural Barrier Certificates for Dynamical Models
Authors: Peruffo Andrea , Ahmed Daniele , Abate Alessandro 
Published: March 2021

AbstractWe introduce an automated, formal, counterexample-based approach to synthesise Barrier Certificates (BC) for the safety verification of continuous and hybrid dynamical models. The approach is underpinned by an inductive framework: this is structured as a sequential loop between a learner, which manipulates a candidate BC structured as a neural network, and a sound verifier, which either certifies the candidate’s validity or generates counter-examples to further guide the learner. We compare the approach against state-of-the-art techniques, over polynomial and non-polynomial dynamical models: the outcomes show that we can synthesise sound BCs up to two orders of magnitude faster, with in particular a stark speedup on the verification engine (up to three orders less), whilst needing a far smaller data set (up to three orders less) for the learning part. Beyond improvements over the state of the art, we further challenge the new approach on a hybrid dynamical model and on larger-dimensional models, and showcase the numerical robustness of our algorithms and codebase.
Counterexample-Guided Prophecy for Model Checking Modulo the Theory of Arrays
Authors: Mann Makai , Irfan Ahmed , Griggio Alberto , Padon Oded , Barrett Clark 
Published: March 2021

AbstractWe develop a framework for model checking infinite-state systems by automatically augmenting them with auxiliary variables, enabling quantifier-free induction proofs for systems that would otherwise require quantified invariants. We combine this mechanism with a counterexample-guided abstraction refinement scheme for the theory of arrays. Our framework can thus, in many cases, reduce inductive reasoning with quantifiers and arrays to quantifier-free and array-free reasoning. We evaluate the approach on a wide set of benchmarks from the literature. The results show that our implementation often outperforms state-of-the-art tools, demonstrating its practical potential.
SAT Solving with GPU Accelerated Inprocessing
Authors: Osama Muhammad , Wijs Anton , Biere Armin 
Published: March 2021

AbstractSince 2013, the leading SAT solvers in the SAT competition all use inprocessing, which unlike preprocessing, interleaves search with simplifications. However, applying inprocessing frequently can still be a bottle neck, i.e., for hard or large formulas. In this work, we introduce the first attempt to parallelize inprocessing on GPU architectures. As memory is a scarce resource in GPUs, we present new space-efficient data structures and devise a data-parallel garbage collector. It runs in parallel on the GPU to reduce memory consumption and improves memory access locality. Our new parallel variable elimination algorithm is twice as fast as previous work. In experiments our new solver ParaFROST solves many benchmarks faster on the GPU than its sequential counterparts.
Spielplangestaltung im Kampf um Anerkennung – Diversifizierung als Legitimationsstrategie
Authors: Michaels Bianca 
Published: March 2021

ZusammenfassungDeutschlandweit lässt sich seit einigen Jahren eine kontinuierlich steigende Zahl an neuen Veranstaltungsformen und -formaten außerhalb etablierter Genre- und Spartenbezeichnungen an öffentlich getragenen Theatern beobachten. Besonders ausgeprägt ist diese Tendenz zur Diversifizierung in der Spielplangestaltung der Münchner Kammerspiele seit der Spielzeit 2015/2016. Ziel des Beitrags ist die Untersuchung des Zusammenhangs von Spielplangestaltung und Legitimationsdiskursen am Beispiel der Kontroverse um die Münchner Kammerspiele unter der Intendanz von Matthias Lilienthal 2018.
Einführung in die Thematik: Zwischen Aushandlung und Direktive – Kulturpolitische Steuerungsmodelle und Theater
Authors: Siegmund Gerald 
Published: March 2021

Zusammenfassung„Nie hat Politik so permanent und mächtig in den aktuellen Prozess des Kunst- und Kulturschaffens hineingewirkt wie im Augenblick“, schreibt Thomas Oberender, Intendant der Berliner Festspiele, in einem Beitrag zu einem Sammelband über „kulturpolitische Konzeptionen zur Reform der Darstellenden Künste“. Folgen wir Oberenders These, so ergibt sich daraus ein latentes Spannungsverhältnis zwischen Politik und den Theatern, deren Freiheit Politik doch gewährleisten soll. Garantiert Artikel 5 des Grundgesetzes die Kunstfreiheit, dürfte sich Kulturpolitik eigentlich gar nicht in den „Prozess des Kunst- und Kulturschaffens“ einmischen, respektive in diesen hineinwirken.
Kulturpolitische Steuerung. Entwicklung und Praxis am Beispiel öffentlich getragener Theater
Authors: Crückeberg Johannes , Steinhauer Moritz 
Published: March 2021

ZusammenfassungDer Beitrag beschreibt die Entwicklung kulturpolitischer Steuerung von der klassisch-bürokratischen Verwaltung über das Neue Steuerungsmodell hin zur New Public Governance und zeigt anhand von Fallbeispielen ausgewählter Theater die Umsetzung dieser Modelle. Die explorative Analyse offenbart dabei hybride Steuerungsformen, welche Elemente der drei vorgestellten Konzepte integrieren sowie ein Auseinanderfallen von formalen Vorgaben und gelebter Praxis. Resümierend werden drei aus der Analyse abgeleitete Steuerungspraktiken vorgestellt: hierarchische, abwesende und kooperative Steuerung sowie Chancen und Risiken von kulturpolitischen Eingriffen durch Steuerungsmodelle diskutiert.
Zwischen Bedingung und Freiheit – Über die Zusammenarbeit zwischen Theaterbetrieben und ihren Aufsichtsgremien
Authors: Glesner Julia 
Published: March 2021

ZusammenfassungDer Erfolg von Theaterbetrieben hängt auch davon ab, wie erfolgreich die Zusammenarbeit zwischen der Leitung und den aufsichtsführenden Gremien des Betriebs abläuft. Abhängig von den wichtigsten Rechtsformen von Theaterbetrieben in der BRD – GmbH, Eigenbetrieb und Regiebetrieb – kommen den Parteien unterschiedliche Entscheidungs- und Einflussmöglichkeiten zu. Durch unzureichendes Gremienmanagement oder wenn das aufsichtsführende Gremium seine Verantwortung nicht in vollem Umfang übernimmt, geraten die Betriebe in Krisen mit Folgen, die auch die Zukunft des Modells „Stadttheater“ gefährden.