The Flux-Scaling Scenario: De Sitter Uplift and Axion Inflation

Non-geometric flux-scaling vacua provide promising starting points to realize axion monodromy inflation via the F-term scalar potential. We show that these vacua can be uplifted to Minkowski and de Sitter by adding an anti D3-brane or a D-term containing geometric and non-geometric fluxes. These uplifted non-supersymmetric models are analyzed with respect to their potential to realize axion monodromy inflation self-consistently. Admitting rational values of the fluxes, we construct examples with the required hierarchy of mass scales.Comment: 30 pages, 7 figures, v2: refs adde

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft

Instructional strategies in the EGRET course: an international graduate forum on becoming a researcher

In today’s knowledge economy, graduate students in the field of Computer Science are increasingly required to develop sophisticated, multi-faceted knowledge of conducting research across multiple contexts and countries. This paper reports the experience of teaching a course to prepare Computer Science graduate students for conducting research in the international community. The course emphasized development of skills critical for a successful research career in computer science, and included construction of knowledge as well as hands-on application of instructional content. The intended learning outcomes included (a) gaining familiarity with research design and methodologies in computer science, (b) preparing and delivering research presentations, (c) reviewing the literature, (d) reading and writing research papers, (e) writing and evaluating research proposals, and (f) networking in the international research community. In this paper, we describe an innovative instructional design that emphasized international collaboration with graduate students from another university on a different continent, namely the Open University in the UK. Our instructional strategies included (a) remote participation of graduate students across universities and countries in real-time, using technologies for synchronous computer mediated communication, (b) incorporation of collaborative activities using online tools scaffolding students’ construction of sophisticated knowledge of key research activities, and (c) providing students with opportunities for hands-on practical application of concepts in collaborative research activities

Inclusiveness Matters: A Large-Scale Analysis of User Feedback

In an era of rapidly expanding software usage, catering to the diverse needs of users from various backgrounds has become a critical challenge. Inclusiveness, representing a core human value, is frequently overlooked during software development, leading to user dissatisfaction. Users often engage in discourse on online platforms where they indicate their concerns. In this study, we leverage user feedback from three popular online sources, Reddit, Google Play Store, and Twitter, for 50 of the most popular apps in the world to reveal the inclusiveness-related concerns from end users. Using a Socio-Technical Grounded Theory approach, we analyzed 23,107 posts across the three sources and identified 1,211 inclusiveness related posts. We organize our empirical results in a taxonomy for inclusiveness comprising 6 major categories: Fairness, Technology, Privacy, Demography, Usability, and Other Human Values. To explore automated support to identifying inclusiveness-related posts, we experimented with five state-of-the-art pre-trained large language models (LLMs) and found that these models' effectiveness is high and yet varied depending on the data source. GPT-2 performed best on Reddit, BERT on the Google Play Store, and BART on Twitter. Our study provides an in-depth view of inclusiveness-related user feedback from most popular apps and online sources. We provide implications and recommendations that can be used to bridge the gap between user expectations and software so that software developers can resonate with the varied and evolving needs of the wide spectrum of users

SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Comment: 45 pages, journal article, 14 figures, 9 tables, CC-BY-NC-ND 4.0 licens