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While vision transformers are able to solve a wide variety of computer vision tasks, no pre-training method has yet demonstrated the same scaling laws as observed in language models. Autoregressive models show promising results, but are commonly trained on images that are cropped or transformed into square images, which distorts or destroys information present in the input. To overcome this limitation, we propose NARAIM, a vision model pre-trained with an autoregressive objective that uses images in their native aspect ratio. By maintaining the native aspect ratio, we preserve the original spatial context, thereby enhancing the model's ability to interpret visual information. In our experiments, we show that maintaining the aspect ratio improves performance on a downstream classification task., Comment: Accepted to NeurIPS, see https://openreview.net/forum?id=7Iuh8VWU66

Diffusion models have achieved unprecedented performance in image generation, yet they suffer from slow inference due to their iterative sampling process. To address this, early-exiting has recently been proposed, where the depth of the denoising network is made adaptive based on the (estimated) difficulty of each sampling step. Here, we discover an interesting "phase transition" in the sampling process of current adaptive diffusion models: the denoising network consistently exits early during the initial sampling steps, until it suddenly switches to utilizing the full network. Based on this, we propose accelerating generation by employing a shallower denoising network in the initial sampling steps and a deeper network in the later steps. We demonstrate empirically that our dual-backbone approach, DuoDiff, outperforms existing early-exit diffusion methods in both inference speed and generation quality. Importantly, DuoDiff is easy to implement and complementary to existing approaches for accelerating diffusion., Comment: Accepted to NeurIPS, see https://openreview.net/forum?id=G7E4tNmmHD

Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for biomedical applications like drug discovery. However, since interpretability is broadly defined, there are no clear guidelines for assessing the biological plausibility of model interpretations. To assess interpretability in the context of drug discovery, we devise a novel prediction task, called drug mechanism-of-action (MoA) deconvolution, with an associated, tailored knowledge graph (KG), MoA-net. We then develop the MoA Retrieval System (MARS), a NeSy approach for drug discovery which leverages logical rules with learned rule weights. Using this interpretable feature alongside domain knowledge, we find that MARS and other NeSy approaches on KGs are susceptible to reasoning shortcuts, in which the prediction of true labels is driven by "degree-bias" rather than the domain-based rules. Subsequently, we demonstrate ways to identify and mitigate this. Thereafter, MARS achieves performance on par with current state-of-the-art models while producing model interpretations aligned with known MoAs., Comment: Under review. 10 pages, 5 supplementary pages. Corresponding code is here: https://github.com/laurendelong21/MARS and here: https://github.com/laurendelong21/MoA-Net

Text-to-image generative models often present issues regarding fairness with respect to certain sensitive attributes, such as gender or skin tone. This study aims to reproduce the results presented in "ITI-GEN: Inclusive Text-to-Image Generation" by Zhang et al. (2023a), which introduces a model to improve inclusiveness in these kinds of models. We show that most of the claims made by the authors about ITI-GEN hold: it improves the diversity and quality of generated images, it is scalable to different domains, it has plug-and-play capabilities, and it is efficient from a computational point of view. However, ITI-GEN sometimes uses undesired attributes as proxy features and it is unable to disentangle some pairs of (correlated) attributes such as gender and baldness. In addition, when the number of considered attributes increases, the training time grows exponentially and ITI-GEN struggles to generate inclusive images for all elements in the joint distribution. To solve these issues, we propose using Hard Prompt Search with negative prompting, a method that does not require training and that handles negation better than vanilla Hard Prompt Search. Nonetheless, Hard Prompt Search (with or without negative prompting) cannot be used for continuous attributes that are hard to express in natural language, an area where ITI-GEN excels as it is guided by images during training. Finally, we propose combining ITI-GEN and Hard Prompt Search with negative prompting., Comment: Accepted to TMLR, see https://openreview.net/forum?id=d3Vj360Wi2

Contribution: This article analyzes the learning effectiveness of a virtual educational escape room for teaching software engineering and compares this activity with traditional teaching through a randomized controlled trial. Background: Educational escape rooms have been used across a wide variety of disciplines at all levels of education and they are becoming increasingly popular among teachers. Nevertheless, there is a clear general need for more robust empirical evidence on the learning effectiveness of these novel activities and, particularly, on their application in software engineering education. Research Questions: Is game-based learning using educational escape rooms more effective than traditional lectures for teaching software engineering? What are the perceptions of software engineering students toward game-based learning using educational escape rooms? Methodology: The study presented in this article is a randomized controlled trial with a pre-and post-test design that was completed by a total of 326 software engineering students. The 164 students belonging to the experimental group learned software modeling by playing an educational escape room whereas the 162 students belonging to the control group learned the same subject matter through a traditional lecture. Findings: The results of the randomized controlled trial show that the students who learned software modeling through the educational escape room had very positive perceptions toward this activity, significantly increased their knowledge, and outperformed those students who learned through a traditional lecture in terms of knowledge acquisition., Comment: 9 pages, 1 figure. Journal article published in IEEE Transactions on Education

Contribution: This article analyzes the learning and motivational impact of teacher-authored educational video games on computer science education and compares its effectiveness in both face-to-face and online (remote) formats. This work presents comparative data and findings obtained from 217 students who played the game in a face-to-face format (control group) and 104 students who played the game in an online format (experimental group). Background: Serious video games have been proven effective at computer science education, however, it is still unknown whether the effectiveness of these games is the same regardless of their format, face-to-face or online. Moreover, the usage of games created through authoring tools has barely been explored. Research Questions: Are teacher-authored educational video games effective in terms of learning and motivation for computer science students? Does the effectiveness of teacher-authored educational video games depend on whether they are used in a face-to-face or online format? Methodology: A quasi-experiment has been conducted by using three instruments (pre-test, post-test, and questionnaire) with the purpose of comparing the effectiveness of game-based learning in face-to-face and online formats. A total of 321 computer science students played a teacher-authored educational video game aimed to learn about software design. Findings: The results reveal that teacher-authored educational video games are highly effective in terms of knowledge acquisition and motivation both in face-to-face and online formats. The results also show that some students' perceptions were more positive when a face-to-face format was used., Comment: 10 pages, 3 figures. Accepted version of a journal article published in IEEE Transactions on Education

Significant research work has been undertaken related to the game-based learning approach over the last years. However, a closer look at this work reveals that further research is needed to examine some types of game-based learning approaches such as virtual reality serious games and LEGO Serious Play. This article examines and compares the effectiveness for learning Scrum and related agile practices of a serious game based on virtual reality and a learning activity based on the LEGO Serious Play methodology. The presented study used a quasi-experimental design with two groups, pre- and post-tests, and a perceptions questionnaire. The sample was composed of 59 software engineering students, 22 of which belonged to group A, while the other 37 were part of group B. The students in group A played the virtual reality serious game, whereas the students in group B conducted the LEGO Serious Play activity. The results show that both game-based learning approaches were effective for learning Scrum and related agile practices in terms of learning performance and motivation, but they also show that the students who played the virtual reality serious game outperformed their peers from the other group in terms of learning performance., Comment: 15 pages, 4 figures. Journal article published in Applied Sciences

Through extensive Monte Carlo simulations, we systematically study the effect of chain stiffness on the packing ability of linear polymers composed of hard spheres in extremely confined monolayers, corresponding effectively to 2D films. First, we explore the limit of random close packing as a function of the equilibrium bending angle and then quantify the local and global order by the degree of crystallinity and the nematic or tetratic orientational order parameter, respectively. A multi-scale wealth of structural behavior is observed, which is inherently absent in the case of athermal individual monomers and is surprisingly richer than its 3D counterpart under bulk conditions. As a general trend an isotropic to nematic transition is observed at sufficiently high surfaces coverages which is followed by the establishment of the tetratic state, which in turn marks the onset of the random close packing. For chains with right-angle bonds, the incompatibility of the imposed bending angle with the neighbor geometry of the triangular crystal leads to a singular intra- and inter-polymer tiling pattern made of squares and triangles with optimal local filling at high surface concentrations. The present study could serve as a first step towards the design of hard colloidal polymers with tunable structural behavior for 2D applications., Comment: 21 pages, 12 figures

The impact of cognitive biases on decision-making in the digital world remains under-explored despite its well-documented effects in physical contexts. This study addresses this gap by investigating the attractiveness halo effect using AI-based beauty filters. We conduct a large-scale online user study involving 2,748 participants who rated facial images from a diverse set of 462 distinct individuals in two conditions: original and attractive after applying a beauty filter. Our study reveals that the same individuals receive statistically significantly higher ratings of attractiveness and other traits, such as intelligence and trustworthiness, in the attractive condition. We also study the impact of age, gender, and ethnicity and identify a weakening of the halo effect in the beautified condition, resolving conflicting findings from the literature and suggesting that filters could mitigate this cognitive bias. Finally, our findings raise ethical concerns regarding the use of beauty filters., Comment: 53 pages, 15 figures, 13 tables

Contribution: The combination of ChatGPT with traditional learning resources is very effective in computer science education. High-performing students are the ones who are using ChatGPT the most. So, a new digital trench could be rising between these students and those with lower degree of fundamentals and worse prompting skills, who may not take advantage of all the ChatGPT possibilities. Background: The irruption of GenAI such as ChatGPT has changed the educational landscape. Therefore, methodological guidelines and more empirical experiences in computer science education are needed to better understand these tools and know how to use them to their fullest potential. Research Questions: This article addresses three questions. The first two explore the degree of use and perceived usefulness of ChatGPT among computer science students to learn database administration, where as the third one explore how the utilization of ChatGPT can impact academic performance. Methodology: This contribution presents an exploratory and correlational study conducted with 37 students who used ChatGPT as a support tool to learn database administration. The student grades and a comprehensive questionnaire were employed as research instruments. Findings: The obtained results indicate that traditional learning resources, such as teacher explanations and student reports, were widely used and correlated positively with student grade. The usage and perceived utility of ChatGPT were moderate, but positive correlations between student grade and ChatGPT usage were found. Indeed, a significantly higher use of this tool was identified among the group of outstanding students.

In response to growing concerns about user privacy, legislators have introduced new regulations and laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) that force websites to obtain user consent before activating personal data collection, fundamental to providing targeted advertising. The cornerstone of this consent-seeking process involves the use of Privacy Banners, the technical mechanism to collect users' approval for data collection practices. Consent management platforms (CMPs) have emerged as practical solutions to make it easier for website administrators to properly manage consent, allowing them to outsource the complexities of managing user consent and activating advertising features. This paper presents a detailed and longitudinal analysis of the evolution of CMPs spanning nine years. We take a twofold perspective: Firstly, thanks to the HTTP Archive dataset, we provide insights into the growth, market share, and geographical spread of CMPs. Noteworthy observations include the substantial impact of GDPR on the proliferation of CMPs in Europe. Secondly, we analyse millions of user interactions with a medium-sized CMP present in thousands of websites worldwide. We observe how even small changes in the design of Privacy Banners have a critical impact on the user's giving or denying their consent to data collection. For instance, over 60% of users do not consent when offered a simple "one-click reject-all" option. Conversely, when opting out requires more than one click, about 90% of users prefer to simply give their consent. The main objective is in fact to eliminate the annoying privacy banner rather the make an informed decision. Curiously, we observe iOS users exhibit a higher tendency to accept cookies compared to Android users, possibly indicating greater confidence in the privacy offered by Apple devices.

Active colloidal systems have emerged as promising contenders for the future of microdevices. While conventional designs have extensively exploited the use of hard colloids, the advancement of cell-inspired architectures represents a pivotal path towards realizing self-regulating and highly functional artificial microswimmers. In this work, we fabricate and actuate Janus lipid vesicles demonstrating reconfigurable motion under an AC electric field. The giant unilamellar vesicles (GUVs) undergo spontaneous phase separation at room temperature leading to Janus-like GUVs with two distinct lipid phases. We report self-propulsion of the Janus GUVs via induced charge electroosmosis, in between parallel electrodes. Remarkably, the fluid nature of the lipid membrane affected by the electric field leads to asymmetry-symmetry transient states resulting in run-and-tumble events supported by structure domain analysis. We characterise an enhanced rotational diffusivity associated with tumble events, decoupled from thermal reorientation. Lastly, we identify cargo-release capabilities and a variety of shape-encoded dynamic modes in these vesicles. This cell-inspired architecture provides an alternative route for creating motile artificial cells and programmable microswimmers.

Non-equilibrium and active effects in mesoscopic scale systems have heralded a new era of scientific inquiries, whether concerning meta-materials or biological systems such as bacteria and cellular components. At mesoscopic scales, experimental and theoretical treatments of membranes, and other quasi-two-dimensional elastic surfaces cannot generically ignore Brownian motion and other thermal effects. In this paper we aim to study the behavior of thermally fluctuating 2-D elastic membranes possessing odd elastic moduli embedded in higher dimensions. We implement an isotropic generalization of the elastic tensor that includes odd elastic moduli, $K_{odd}$ and $A_{odd}$, that break conservation of energy and angular momentum respectively, due to cite{scheibner2020odd}. Naturally this introduces active and non-equilibrium effects. Passive equilibrium thermalized elastic membranes possess effective (renormalized) Lam\'e coefficients that reduce with increasing system size and a diverging effective bending rigidity. Introducing two odd elastic moduli means that deformations from a reference state can induce chiral forces that cannot be derived from a Hamiltonian. Thus, the behavior of odd elastic membranes must instead be investigated with Langevin equations. If fluctuation-dissipation relations hold, we calculate via the renormalization group that at long length scales, active effects due to $K_{odd}$ can be effectively ignored whereas $A_{odd}$ cannot. To validate these findings, we developed an advanced force implementation methodology, inspired by the $(T)$-scheme prevalent in vertex models. This contributed to a new method for the simulation of elastic membranes in higher dimensions, as detailed recently in \cite{matoz2020wrinkle}. The novelty of the method is that microscopic/discrete and continuum in-plane elastic moduli are one-to-one and thus no coarse-graining is needed.

Through extensive molecular simulations we determine a phase diagram of attractive, flexible polymer chains in two and three dimensions. A surprisingly rich collection of distinct crystal morphologies appear, which can be finely tuned through the range of attraction. In three dimensions these include the face centered cubic, hexagonal close packed, simple hexagonal and body centered cubic crystals and the Frank-Kasper phase. A simple geometric model is proposed, based on the concept of cumulative neighbours of ideal crystals, which can accurately predict most of the observed structures and the corresponding transitions. The attraction range can thus be considered as an adjustable parameter for the design of colloidal polymer crystals with tailored morphologies., Comment: 11 pages, 9 figures

Students' perception of excessive difficulty in STEM degrees lowers their motivation and therefore affects their performance. According to prior research, the use of gamification techniques promote engagement, motivation and fun when learning. Badges, which are a distinction that is given as a reward to students, are a well-known gamification tool. This contribution hypothesizes that the use of badges, both physical and virtual, improves student performance and reduces dropout rates. To verify that hypothesis, a case study involving 99 students enrolled in a Databases course of computer engineering degrees was conducted. The results show that the usage of badges improves student performance and reduces dropout rates. However, negligible differences were found between the use of different kind of badges.

DevOps responds the growing need of companies to streamline the software development process and, thus, has experienced widespread adoption in the past years. However, the successful adoption of DevOps requires companies to address significant cultural and organizational changes. Understanding the organizational structure and characteristics of teams adopting DevOps is key, and comprehending the existing theories and representations of team taxonomies is critical to guide companies in a more systematic and structured DevOps adoption process. As there was no unified theory to explain the different topologies of DevOps teams, in previous work, we built a theory to represent the organizational structure and characteristics of teams adopting DevOps, harmonizing the existing knowledge. In this paper, we expand the theory-building in the context of DevOps Team Taxonomies. Our main contributions are presenting and executing the Operationalization and Testing phases for a continuously evolving theory on DevOps team structures. We operationalize the constructs and propositions that make up our theory to generate empirically testable hypotheses to confirm or disconfirm the theory. Specifically, we focus on the research operation side of the theory-research cycle: identifying propositions, deriving empirical indicators from constructs, establishing testable hypotheses, and testing them. We performed the operationalization and testing of the DevOps Team Taxonomies Theory, which resulted in an empirically verified and trustworthy theory. Our theory has 28 propositions representing this model that map properties to the constructs of our theory. The operationalization generated 34 testable hypotheses, and we thoroughly tested 11 of them. The testing has proved the effectiveness of the theoretical framework, while the operationalization of the constructs has enhanced the initial framework., Comment: Submitted to Empirical Software Engineering

Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting, or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges which originate from the outlined factors.

Motivation: The importance of clinical data in understanding the pathophysiology of complex disorders has prompted the launch of multiple initiatives designed to generate patient-level data from various modalities. While these studies can reveal important findings relevant to the disease, each study captures different yet complementary aspects and modalities which, when combined, generate a more comprehensive picture of disease aetiology. However, achieving this requires a global integration of data across studies, which proves to be challenging given the lack of interoperability of cohort datasets. Results: Here, we present the Data Steward Tool (DST), an application that allows for semi-automatic semantic integration of clinical data into ontologies and global data models and data standards. We demonstrate the applicability of the tool in the field of dementia research by establishing a Clinical Data Model (CDM) in this domain. The CDM currently consists of 277 common variables covering demographics (e.g. age and gender), diagnostics, neuropsychological tests, and biomarker measurements. The DST combined with this disease-specific data model shows how interoperability between multiple, heterogeneous dementia datasets can be achieved., Comment: 13 pages, 5 figures, 2 tables

This article presents several design techniques to fabricate micro-electro-mechanical systems (MEMS) using standard complementary metal-oxide semiconductor (CMOS) processes. They were applied to fabricate high yield CMOS-MEMS shielded Lorentz-force magnetometers (LFM). The multilayered metals and oxides of the back-end-of-line (BEOL), normally used for electronic routing, comprise the structural part of the MEMS. The most important fabrication challenges, modeling approaches and design solutions are discussed. Equations that predict the Q factor, sensitivity, Brownian noise and resonant frequency as a function of temperature, gas pressure and design parameters are presented and validated in characterization tests. A number of the fabricated magnetometers were packaged into Quad Flat No-leads (QFN) packages. We show this process can achieve yields above 95 % when the proper design techniques are adopted. Despite CMOS not being a process for MEMS manufacturing, estimated performance (sensitivity and noise level) is similar or superior to current commercial magnetometers and others built with MEMS processes. Additionally, typical offsets present in Lorentz-force magnetometers were prevented with a shielding electrode, whose efficiency is quantified. Finally, several reliability test results are presented, which demonstrate the robustness against high temperatures, magnetic fields and acceleration shocks.

Integrating security into agile software development is an open issue for research and practice. Especially in strongly regulated industries, complexity increases not only when scaling agile practices but also when aiming for compliance with security standards. To achieve security compliance in a large-scale agile context, we developed S2C-SAFe: An extension of the Scaled Agile Framework that is compliant to the security standard IEC~62443-4-1 for secure product development. In this paper, we present the framework and its evaluation by agile and security experts within Siemens' large-scale project ecosystem. We discuss benefits and limitations as well as challenges from a practitioners' perspective. Our results indicate that \ssafe contributes to successfully integrating security compliance with lean and agile development in regulated environments. We also hope to raise awareness for the importance and challenges of integrating security in the scope of Continuous Software Engineering., Comment: Authors' Copy

In recent years, computer simulations are playing a fundamental role in unveiling some of the most intriguing features of prime numbers. In this work, we define an algorithm for a deterministic walk through a two-dimensional grid that we refer to as Prime Walk. The walk is constructed from a sequence of steps dictated by and dependent on the sequence of last digits of the primes. Despite the apparent randomness of this generating sequence, the resulting structure -- both in 2d and 3d -- created by the algorithm presents remarkable properties and regularities in its pattern that we proceed to analyze in detail., Comment: 7 pages, 7 figures

Context: DevOps can be defined as a cultural movement to improve and accelerate the delivery of business value by making the collaboration between development and operations effective. Objective: This paper aims to help practitioners and researchers to better understand the organizational structure and characteristics of teams adopting DevOps. Method: We conducted an exploratory study by leveraging in depth, semi-structured interviews to relevant stakeholders of 31 multinational software-intensive companies, together with industrial workshops and observations at organizations' facilities that supported triangulation. We used Grounded Theory as qualitative research method to explore the structure and characteristics of teams, and statistical analysis to discover their implications in software delivery performance. Results: We describe a taxonomy of team structure patterns that shows emerging, stable and consolidated product teams that are classified according to six variables, such as collaboration frequency, product ownership sharing, autonomy, among others, as well as their implications on software delivery performance. These teams are often supported by horizontal teams (DevOps platform teams, Centers of Excellence, and chapters) that provide them with platform technical capability, mentoring and evangelization, and even temporarily facilitate human resources. Conclusion: This study aims to strengthen evidence and support practitioners in making better informed about organizational team structures by analyzing their main characteristics and implications in software delivery performance., Comment: 18 pages, 5 figures

Software needs to be secure, in particular, when deployed to critical infrastructures. Secure coding guidelines capture practices in industrial software engineering to ensure the security of code. This study aims to assess the level of awareness of secure coding in industrial software engineering, the skills of software developers to spot weaknesses in software code, avoid them, and the organizational support to adhere to coding guidelines. The approach draws on well-established theories of policy compliance, neutralization theory, and security-related stress and the authors' many years of experience in industrial software engineering and on lessons identified from training secure coding in the industry. The paper presents the questionnaire design for the online survey and the first analysis of data from the pilot study., Comment: Preprint accepted for publication at The 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2020)

Defects in requirements specifications can have severe consequences during the software development lifecycle. Some of them may result in poor product quality and/or time and budget overruns due to incorrect or missing quality characteristics, such as security. This characteristic requires special attention in web applications because they have become a target for manipulating sensible data. Several concerns make security difficult to deal with. For instance, security requirements are often misunderstood and improperly specified due to lack of security expertise and emphasis on security during early stages of software development. This often leads to unspecified or ill-defined security-related aspects. These concerns become even more challenging in agile contexts, where lightweight documentation is typically produced. To tackle this problem, we designed an approach for reviewing security-related aspects in agile requirements specifications of web applications. Our proposal considers user stories and security specifications as inputs and relates those user stories to security properties via Natural Language Processing. Based on the related security properties, our approach identifies high-level security requirements from the Open Web Application Security Project (OWASP) to be verified, and generates a reading technique to support reviewers in detecting defects. We evaluate our approach via three experiment trials conducted with 56 novice software engineers, measuring effectiveness, efficiency, usefulness, and ease of use. We compare our approach against using: (1) the OWASP high-level security requirements, and (2) a perspective-based approach as proposed in contemporary state of the art. The results strengthen our confidence that using our approach has a positive impact (with large effect size) on the performance of inspectors in terms of effectiveness and efficiency., Comment: Preprint accepted for publication at the Requirements Engineering journal. arXiv admin note: text overlap with arXiv:1906.11432

In recent years, the research on empirical software engineering that uses qualitative data analysis (e.g., cases studies, interview surveys, and grounded theory studies) is increasing. However, most of this research does not deep into the reliability and validity of findings, specifically in the reliability of coding in which these methodologies rely on, despite there exist a variety of statistical techniques known as Inter-Coder Agreement (ICA) for analyzing consensus in team coding. This paper aims to establish a novel theoretical framework that enables a methodological approach for conducting this validity analysis. This framework is based on a set of coefficients for measuring the degree of agreement that different coders achieve when judging a common matter. We analyze different reliability coefficients and provide detailed examples of calculation, with special attention to Krippendorff's $\alpha$ coefficients. We systematically review several variants of Krippendorff's $\alpha$ reported in the literature and provide a novel common mathematical framework in which all of them are unified through a universal $\alpha$ coefficient. Finally, this paper provides a detailed guide of the use of this theoretical framework in a large case study on DevOps culture. We explain how $\alpha$ coefficients are computed and interpreted using a widely used software tool for qualitative analysis like Atlas.ti. We expect that this work will help empirical researchers, particularly in software engineering, to improve the quality and trustworthiness of their studies., Comment: 35 pages, 15 figures, 12 tables

Context: DevOps can be defined as a cultural movement to improve and accelerate the delivery of business value by making the collaboration between development and operations effective. Although this movement is relatively recent, there exist an intensive research around DevOps. However, the real reasons why companies move to DevOps and the results they expect to obtain have been paid little attention in real contexts. Objective: This paper aims to help practitioners and researchers to better understand the context and the problems that many companies face day to day in their organizations when they try to accelerate software delivery and the main drivers that move these companies to adopting DevOps. Method: We conducted an exploratory study by leveraging in depth, semi-structured interviews to relevant stakeholders of 30 multinational software-intensive companies, together industrial workshops and observations at organizations' facilities that supported triangulation. Additionally, we conducted an inter-coder agreement analysis, which is not usually addressed in qualitative studies in software engineering, to increase reliability and reduce authors bias of the drawn findings. Results: The research explores the problems and expected outcomes that moved companies to adopt DevOps and reveals a set of patterns and anti-patterns about the reasons why companies are instilling a DevOps culture. Conclusions: This study aims to strengthen evidence and support practitioners in making better informed about which problems trigger a DevOps transition and most common expected results., Comment: 47 pages, 9 figures, 17 tables

Scattering experiments have revolutionized our understanding of nature. Examples include the discovery of the nucleus, crystallography, and the discovery of the double helix structure of DNA. Scattering techniques differ by the type of the particles used, the interaction these particles have with target materials and the range of wavelengths used. Here, we demonstrate a new 2-dimensional table-top scattering platform for exploring magnetic properties of materials on mesoscopic length scales. Long lived, coherent magnonic excitations are generated in a thin film of YIG and scattered off a magnetic target deposited on its surface. The scattered waves are then recorded using a scanning NV center magnetometer that allows sub-wavelength imaging and operation under conditions ranging from cryogenic to ambient environment. While most scattering platforms measure only the intensity of the scattered waves, our imaging method allows for spatial determination of both amplitude and phase of the scattered waves thereby allowing for a systematic reconstruction of the target scattering potential. Our experimental results are consistent with theoretical predictions for such a geometry and reveal several unusual features of the magnetic response of the target, including suppression near the target edges and gradient in the direction perpendicular to the direction of surface wave propagation. Our results establish magnon scattering experiments as a new platform for studying correlated many-body systems.

The detection of the radio emission following a neutrino interaction in ice is a promising technique to obtain significant sensitivities to neutrinos with energies above PeV. The detectable radio emission stems from particle showers in the ice. So far, detector simulations have considered only the radio emission from the primary interaction of the neutrino. For this study, existing simulation tools have been extended to cover secondary interactions from muons and taus. We find that secondary interactions of both leptons add up to 25\% to the effective volume of neutrino detectors. Also, muon and tau neutrinos can create several detectable showers, with the result that double signatures do not constitute an exclusive signature for tau neutrinos. We also find that the background of atmospheric muons from cosmic rays is non-negligible for in-ice arrays and that an air shower veto should be considered helpful for radio detectors.

We construct a privacy-preserving, distributed and decentralized marketplace where parties can exchange data for tokens. In this market, buyers and sellers make transactions in a blockchain and interact with a third party, called notary, who has the ability to vouch for the authenticity and integrity of the data. We introduce a protocol for the data-token exchange where neither party gains more information than what it is paying for, and the exchange is fair: either both parties gets the other's item or neither does. No third party involvement is required after setup, and no dispute resolution is needed., Comment: 10 pages

We present a cryptographic primitive called WibsonTree designed to preserve users' privacy by allowing them to demonstrate predicates on their personal attributes, without revealing the values of those attributes. We suppose that there are three types of agents --buyers, sellers and notaries-- who interact in a decentralized privacy-preserving data marketplace (dPDM) such as the Wibson marketplace. We introduce the WibsonTree protocol as an efficient cryptographic primitive that enables the exchange of private information while preserving the seller's privacy. Using our primitive, a data seller can efficiently prove that he/she belongs to the target audience of a buyer's data request, without revealing any additional information., Comment: 8 pages, 2 figures