3,091 research outputs found
Tracing cultural change in the reproduction of intolerance : 'secularism', 'Islamism' and others in Turkey’s experience of democratization
Defence date: 16 January 2020Examining Board: Ayhan Kaya, Istanbul Bilgi University; Hanspeter Kriesi, European University Institute – SPS Department; Élise Massicard, CERI, Sciences Po; Olivier Roy, European University Institute – SPS Department (Supervisor)How do cultural resources such as values and beliefs, and their functions in ideology-making, change? In the democratization literature, the value-based approach to culture seeks cultural change based on values. However, the combination of this approach with value-surveys fails to consider several ways in which change may unfold between cultural periods. Instead, this study will delve into a history of conversational texts, which are endogenously grounded within culture, capable of demonstrating culture in action and reflecting what is collective about culture as it operates through dialectical encounters. I focus on change in three landscapes of culture in Turkey, which have witnessed some of the most persistent stories of the unequal relationship between the self and the other
SoK:Prudent Evaluation Practices for Fuzzing
Fuzzing has proven to be a highly effective approach to uncover software bugs over the past decade. After AFL popularized the groundbreaking concept of lightweight coverage feedback, the field of fuzzing has seen a vast amount of scientific work proposing new techniques, improving methodological aspects of existing strategies, or porting existing methods to new domains. All such work must demonstrate its merit by showing its applicability to a problem, measuring its performance, and often showing its superiority over existing works in a thorough, empirical evaluation. Yet, fuzzing is highly sensitive to its target, environment, and circumstances, e.g., randomness in the testing process. After all, relying on randomness is one of the core principles of fuzzing, governing many aspects of a fuzzer's behavior. Combined with the often highly difficult to control environment, the reproducibility of experiments is a crucial concern and requires a prudent evaluation setup. To address these threats to validity, several works, most notably Evaluating Fuzz Testing by Klees et al., have outlined how a carefully designed evaluation setup should be implemented, but it remains unknown to what extent their recommendations have been adopted in practice. In this work, we systematically analyze the evaluation of 150 fuzzing papers published at the top venues between 2018 and 2023. We study how existing guidelines are implemented and observe potential shortcomings and pitfalls. We find a surprising disregard of the existing guidelines regarding statistical tests and systematic errors in fuzzing evaluations. For example, when investigating reported bugs, we find that the search for vulnerabilities in real-world software leads to authors requesting and receiving CVEs of questionable quality. Extending our literature analysis to the practical domain, we attempt to reproduce claims of eight fuzzing papers. These case studies allow us to assess the practical reproducibility of fuzzing research and identify archetypal pitfalls in the evaluation design. Unfortunately, our reproduced results reveal several deficiencies in the studied papers, and we are unable to fully support and reproduce the respective claims. To help the field of fuzzing move toward a scientifically reproducible evaluation strategy, we propose updated guidelines for conducting a fuzzing evaluation that future work should follow
Designing Optimal Behavioral Experiments Using Machine Learning
Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely, and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools are ill-suited to designing experiments to test and compare these models. To avoid these pitfalls and realize the full potential of computational modeling, we require tools to design experiments that provide clear answers about what models explain human behavior and the auxiliary assumptions those models must make. Bayesian optimal experimental design (BOED) formalizes the search for optimal experimental designs by identifying experiments that are expected to yield informative data. In this work, we provide a tutorial on leveraging recent advances in BOED and machine learning to find optimal experiments for any kind of model that we can simulate data from, and show how by-products of this procedure allow for quick and straightforward evaluation of models and their parameters against real experimental data. As a case study, we consider theories of how people balance exploration and exploitation in multi-armed bandit decision-making tasks. We validate the presented approach using simulations and a real-world experiment. As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model. At the same time, formalizing a scientific question such that it can be adequately addressed with BOED can be challenging and we discuss several potential caveats and pitfalls that practitioners should be aware of. We provide code and tutorial notebooks to replicate all analyses
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea
ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
Film, Propaganda, and the “New Nigeria” National Philosophy
This qualitative research examined the utility of film for political propaganda purposes and behavioral change objectives. It critically assesses how film, as a communication medium, has been optimized in constructing a national rebirth philosophy tagged “New Nigeria” political constructs. The objectives of the study were to demystify the political propaganda strategies adopted by the filmmakers in propagating the “new Nigeria” national philosophy and to unearth the latent and manifest socio-political propaganda themes embedded in the film that reinforce the ‘new Nigeria’ political philosophy.
In ascertaining the potentials of film in projecting propagandists’ ideologies, the film “If I am President” (Bright Obasi, 2018) was thematically deconstructed using the qualitative content analysis research design. The “New Nigeria” political constructs content-analyzed were placed into discourse paradigms, and discussions were attempted using the critical discourse analytical method.
A deconstruction of the latent socio-political themes in the film revealed the filmmakers’ subtle utilisation of mental conditioning, mental provocativeness, ‘scapegoatism’, psycho-emotional articulation, appeal to socio-political action, and entertainment-education political propaganda strategies in projecting the “New Nigeria” national philosophy for social action through behavior change.
The dominant “New Nigeria” political philosophies expressed in the film included, but were not limited to, leanings suggesting national rebirth, nation-building, socio-political egalitarianism, youth activism, digitization of national politics, techno-democracy, zero tolerance for corruption, and tolerance across racial, religious, tribal, ethnic, and political lines. These philosophies are the ideals suggested in the film as cardinal constructs and conditions for the rebirth of a new nation, thus, “New Nigeria”
The politics of content prioritisation online governing prominence and discoverability on digital media platforms
This thesis examines the governing systems and industry practices shaping online content prioritisation processes on digital media platforms. Content prioritisation, and the relative prominence and discoverability of content, are investigated through a critical institutional lens as digital decision guidance processes that shape online choice architecture and influence users’ access to content online. This thesis thus shows how prioritisation is never neutral or static and cannot be explained solely by political economic or neoclassical economics approaches. Rather, prioritisation is dynamically shaped by the institutional environment and by the clash between existing media governance systems and those emerging for platform governance. As prioritisation processes influence how audiovisual media services are accessed online, posing questions about the public interest in such forms of intermediation is key. In that context, this research asks how content prioritisation is governed on digital media platforms, and what the elements of a public interest framework for these practices might be. To address these questions, I use a within case study comparative research design focused on the United Kingdom, collecting data by means of semi-structured interviews and document analysis. Through a thematic analysis, I then investigate how institutional arrangements influence both organisational strategies and interests, as well as the relationships among industry and policy actors involved, namely, platform organisations, pay-TV operators, technology manufacturers, content providers including public service media, and regulators. The results provide insights into the ‘black box’ of content prioritisation across three interconnected dimensions: technical, market, and regulatory. In each dimension, a battle between industry and policy actors emerges to influence prioritisation online. As the UK Government and regulator intend to develop new prominence rules, the dispute takes on a normative dimension and gives rise to contested visions of what audiovisual services should be prioritised to the final users, and which private- and public-interest-driven criteria are (or should) be used to determine that. Finally, the analysis shows why it is crucial to reflect on how the public interest is interpreted and operationalised as new prominence regulatory regimes emerge with a variety of sometimes contradictory implications for media pluralism, diversity and audience freedom of choice. The thesis therefore indicates the need for new institutional arrangements and a public interest-driven framework for prioritisation on digital media platforms. Such a framework conceives of public interest content standards as an institutional imperative for media and platform organisations and prompts regulators to develop new online content regulation that is appropriate to changing forms of digital intermediation and emerging audiovisual market conditions. While the empirical focus is on the UK, the implications of the research findings are also considered in the light of developments in the European Union and Council of Europe initiatives that bear on the future discoverability of public interest media services and related prominence regimes
Optimising Human-AI Collaboration by Learning Convincing Explanations
Machine learning models are being increasingly deployed to take, or assist in
taking, complicated and high-impact decisions, from quasi-autonomous vehicles
to clinical decision support systems. This poses challenges, particularly when
models have hard-to-detect failure modes and are able to take actions without
oversight. In order to handle this challenge, we propose a method for a
collaborative system that remains safe by having a human ultimately making
decisions, while giving the model the best opportunity to convince and debate
them with interpretable explanations. However, the most helpful explanation
varies among individuals and may be inconsistent across stated preferences. To
this end we develop an algorithm, Ardent, to efficiently learn a ranking
through interaction and best assist humans complete a task. By utilising a
collaborative approach, we can ensure safety and improve performance while
addressing transparency and accountability concerns. Ardent enables efficient
and effective decision-making by adapting to individual preferences for
explanations, which we validate through extensive simulations alongside a user
study involving a challenging image classification task, demonstrating
consistent improvement over competing systems
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