Daniel E. Rosner

Over the last decade, cities have become increasingly digitalized with the adoption of Artificial Intelligence (AI) technologies. While these changes encompass economic, technological, and infrastructure transformation, citizens remain at the core of the cities as users, co-creators, and beneficiaries of AI urban innovations. Our study presents a bibliometric text-mining analysis of literature from the last 15 years (2010–2024). We used metadata from 1197 publications indexed in the Web of Science on the intersection of AI, cities, and citizens. Our results emphasize the most influential publications, thematic clusters, and trends. Moreover, the analysis shows complementary technologies and techniques used alongside AI technologies. Our analysis also highlights the importance of governance, ethical implications, privacy, security, health, transparency, and participatory design when using and creating AI smart technologies for cities. By providing a mapping of the scientific themes and emerging trends, our study is relevant for researchers and policymakers interested in citizen-centric AI technologies for cities.

Virtual prototypes are increasingly used in the automotive industry to accelerate chip development, offering early-stage validation before physical production. However, their configuration and execution often rely on manual commands or custom scripts, which are error-prone, inefficient, and unfriendly to less technical users. This paper presents a desktop application with a graphical user interface (GUI) developed in Python and PyQt, designed to simplify, automate, and optimize interaction with virtual prototypes. The tool integrates external components such as Keil Microvision and GTKWave, provides dynamic configuration management with visual feedback, and supports automated installation, parameter selection, and prototype execution. Evaluations with internal developers and external clients at Infineon Technologies demonstrate significant improvements in usability, robustness, and speed. Benchmarks show time savings of over 3 × compared to manual operation, along with better resource handling and reduced user errors. The tool is now used as the standard interface in the virtual prototyping workflow and continues to evolve based on real-world feedback.

This article investigates how young participants in the Innovation Labs Hackathon, Romania's largest national educational pre-accelerator program, define the concept of innovation at the entry point into an entrepreneurial support structure. Drawing on Bourdieu's theory of fields and forms of capital, and the concept of boundary objects, we argue that the Innovation Labs program functions as a boundary object and a site of symbolic positioning. The study examines how symbolic meanings are negotiated at the intersection of academic and entrepreneurial logics. Using a meaning-based recoding grid, derived from Bourdieu's theory, and a cluster analysis that grouped similar data points, applied to 316 open-ended survey responses, five repertoires of innovation were identified: Tech Enthusiasts, Problem Solvers, Symbolic Creators, Institutional Learners, and Social Creatives. These reflect distinct configurations of cultural, social, symbolic, and applied capital, shaped by both individual trajectories and broader field dynamics. Minor differences related to gender and prior hackathon experience suggest differentiated access to legitimate forms of expression and recognition.

In this paper, we discuss how to understand and integrate the diverse ethical perspectives which appear in the design, deployment, and application of AI systems. Drawing on qualitative content analysis of comments written on /r/Ethics, /r/Technology and /r/Futurology subreddits (n=1,018), written between July 2023 and June 2025, we identified three main folk theories regarding the ethical future of Artificial Intelligence: the first theory is that AI corporations manipulate truth and emotions through computational performance, the second theory questions the moral limits of AI systems as well as their sentience, and the third theory highlights that tech capitalists prioritize profit over ethical principles. All of these folk theories enact users with the necessary expertise to impose certain forms of human agency in relation to AI.

This study investigates inter-rater reliability among seven large language models (LLMs) when coding justificatory regimes in political discourse using Boltanski and Thévenot's orders of worth framework. We analyze how ChatGPT-4o, Claude 3.7 Sonnet, Perplexity Sonar, Gemini 2.0 Flash, Grok 3, Mistral, and Perplexity R1 identify and score the presence of nine orders of worth in a Reddit thread about EU regulatory action against TikTok. The research employs a two-stage methodology—first measuring baseline agreement across models, then assessing convergence after exposing all models to shared interpretive references. Results reveal structured patterns of interpretive alignment and divergence: civic, green, and projective orders show perfect consensus across all models, while fame, domestic, and market orders generate significant disagreement. After exposure to interpretive scaffolding, models demonstrate convergence, with eight of nine orders achieving perfect consensus. This convergence suggests that while initial outputs vary, LLMs can achieve substantial interpretive alignment when provided with explicit normative cues. The persistent variance in coding domestic order, even after calibration, indicates that certain justificatory regimes remain computationally ambiguous. These findings contribute to methodological discussions about LLM reliability in qualitative content analysis and theoretical debates about the computational legibility of different moral grammars.

This study examines privacy calculus in online child sexual abuse material (CSAM) detection across Europe, using Flash Eurobarometer 532 data. Drawing on theories of structuration and risk society, we analyze how individual agency and institutional frameworks interact in shaping privacy attitudes in high-stakes digital scenarios. Multinomial regression reveals age as a significant individual-level predictor, with younger individuals prioritizing privacy more. Country-level analysis shows Central and Eastern European nations have higher privacy concerns, reflecting distinct institutional and cultural contexts. Notably, the Digital Economy and Society Index (DESI) shows a positive association with privacy concerns in regression models when controlling for Augmented Human Development Index (AHDI) components, contrasting its negative bivariate correlation. Life expectancy emerges as the strongest country-level predictor, negatively associated with privacy concerns, suggesting deep institutional mechanisms shape privacy attitudes beyond individual factors. This dual approach reveals that both individual factors and national contexts are shaping privacy calculus in CSAM detection. The study contributes to a better understanding of privacy calculus in high-stakes scenarios, with implications for policy development in online child protection.

In this study, we investigate public attitudes toward violence against women, by analyzing the 2022 Violence Against Women (VaW) survey in Romania. The objectives of this research are to study the evolution of attitudes, to explore the typology of tolerance regarding violence against women, and to uncover the social stratification of the typology of tolerance attitudes in this regard. The methodology uses secondary analysis of a nationally representative survey, using both linear and cluster analysis to explore patterns in public tolerance regarding violence against women. We found that violence against women is strongly condemned by a large majority of the Romanian population, albeit with some variability. Men and people with lower formal education, lower household income, and who are more religiously involved were more likely to express weaker condemnation of violence against women. We identified four types of attitudes through cluster analysis, distinguishing people with zero tolerance from a very small cluster with high tolerance for all forms of violence against women. In addition, two types of relative tolerance were identified, namely the cluster of “tolerance for domestic patriarchalism,” which was discursively inclined toward some justification for social violence and for forms of sexual violence, but not for physical violence, and the cluster of “tolerance for domestic violence,” which was inclined toward some justification for verbal, physical, sexual, and social violence located in the household or between men and women who are familiar which each other. This study contributes to the understanding of how social norms and discursive practices influence gender-related attitudes and the legitimization of violence, with implications for policy and public education aimed at reducing tolerance for gender-based violence.

Generative artificial intelligence (AI) represents an increasingly popular topic that is visible even in most research areas within the social sciences and humanities fields. However, little attention has been paid to the knowledge dimensions reflecting the potential macro-social implications of generative technologies. This study utilizes a two-fold methodology, consisting of a bibliometric analysis of articles published in the last decade (N = 484) and a subsequent qualitative thematic review of the most influential articles in each research area (N = 246). The objective is to investigate the main conceptual dimensions associated with generative AI in the social sciences. Applying a thematic analysis framework, we notice that the most popular dimensions are technological, ethical, and social. These dimensions primarily focus on investigating the implications of the generative use of AI on employees in professional sectors as well as on students and teachers in the educational environment. Moreover, the political dimension reflects macro-social consequences on governance and legal components related to ensuring social protection for professions that risk becoming obsolete due to the widespread adoption of ChatGPT-type technologies. Overall, our research emphasizes concrete scholarly tensions through which generative AI-based technologies are predominantly encouraged in the educational and organizational sectors, but the potential risks associated with copyright infringement and job loss might constitute important drivers of social change. We also notice that a Foucauldian power/knowledge framework would prove useful in understanding the underdiscussed effects of generative AI on the societal/macro level.

Introduction The rapid evolution of Artificial Intelligence (AI) has introduced transformative potential across various sectors, while simultaneously posing significant cybersecurity risks. Methods The aim of this paper is to examine the debates on AI-related cybersecurity risks through the lens of Beck’s theory of the risk society. Utilizing thematic content analysis, we explored public discourse on AI and cybersecurity as presented in articles published by WIRED. Results Our analysis identified several key themes: the global nature of AI risks, their pervasive influence across multiple sectors, the alteration of public trust, the individualization of risk, and the uneven distribution of AI risks and benefits. Discussion The editorial choices in WIRED predominantly favor a functionalist and solutionist perspective on AI cybersecurity risks, often marginalizing the opinions of ordinary individuals and non-Western voices. This editorial bias tends to limit diversity and underrepresent key opposing viewpoints, potentially hindering a more comprehensive and nuanced debate on AI and cybersecurity issues.

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device’s continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%.

Surveillance footage represents a valuable resource and opportunities for conducting gait analysis. However, the typical low quality and high noise levels in such footage can severely impact the accuracy of pose estimation algorithms, which are foundational for reliable gait analysis. Existing literature suggests a direct correlation between the efficacy of pose estimation and the subsequent gait analysis results. A common mitigation strategy involves fine-tuning pose estimation models on noisy data to improve robustness. However, this approach may degrade the downstream model's performance on the original high-quality data, leading to a trade-off that is undesirable in practice. We propose a processing pipeline that incorporates a task-targeted artifact correction model specifically designed to pre-process and enhance surveillance footage before pose estimation. Our artifact correction model is optimized to work alongside a state-of-the-art pose estimation network, HRNet, without requiring repeated fine-tuning of the pose estimation model. Furthermore, we propose a simple and robust method for obtaining low quality videos that are annotated with poses in an automatic manner with the purpose of training the artifact correction model. We systematically evaluate the performance of our artifact correction model against a range of noisy surveillance data and demonstrate that our approach not only achieves improved pose estimation on low-quality surveillance footage, but also preserves the integrity of the pose estimation on high resolution footage. Our experiments show a clear enhancement in gait analysis performance, supporting the viability of the proposed method as a superior alternative to direct fine-tuning strategies. Our contributions pave the way for more reliable gait analysis using surveillance data in real-world applications, regardless of data quality.

Surveillance footage represents a valuable resource and opportunities for conducting gait analysis. However, the typical low quality and high noise levels in such footage can severely impact the accuracy of pose estimation algorithms, which are foundational for reliable gait analysis. Existing literature suggests a direct correlation between the efficacy of pose estimation and the subsequent gait analysis results. A common mitigation strategy involves fine-tuning pose estimation models on noisy data to improve robustness. However, this approach may degrade the downstream model's performance on the original high-quality data, leading to a trade-off that is undesirable in practice. We propose a processing pipeline that incorporates a task-targeted artifact correction model specifically designed to pre-process and enhance surveillance footage before pose estimation. Our artifact correction model is optimized to work alongside a state-of-the-art pose estimation network, HRNet, without requiring repeated fine-tuning of the pose estimation model. Furthermore, we propose a simple and robust method for obtaining low quality videos that are annotated with poses in an automatic manner with the purpose of training the artifact correction model. We systematically evaluate the performance of our artifact correction model against a range of noisy surveillance data and demonstrate that our approach not only achieves improved pose estimation on low-quality surveillance footage, but also preserves the integrity of the pose estimation on high resolution footage. Our experiments show a clear enhancement in gait analysis performance, supporting the viability of the proposed method as a superior alternative to direct fine-tuning strategies. Our contributions pave the way for more reliable gait analysis using surveillance data in real-world applications, regardless of data quality.

This study uses bibliometric analysis and a systematic literature review to map the conceptual structure of artificial intelligence innovations (AI-I) in the social sciences between 2000 and 2023. It explicitly focuses on non-economic aspects conducive to AI-I, namely social, technological, cultural, sustainable, personal, moral, and ethical. Our analysis reveals that 1225 articles and proceeding papers have been published, and terms such as “technology,” “big data,” “management,” “performance,” “future,” and “impact” are the most frequently used when discussing innovation and AI. According to our time-zone analysis, the last two years have shown a significant emphasis on concepts such as “transformation,” “corporate social responsibility,” and “resource-based view.” In terms of citations, the countries that receive the highest number of references in the AI-I field are the United Kingdom, the United States, Germany, Australia, and China. The most prolific authors in terms of publications are David Teece, Erik Brynjolfsson, and Anjan Chatterjee. Given that most studies highlight the economic side of AI-I, we selected the most prolific 163 articles from all social science research areas. These studies legitimize the main non-economic aspects that highlight both certainties and uncertainties conducive to such innovations. Although the technological component is the most popular in our analysis of the non-economic aspects of the AI-I subfield, we find an important emphasis on ethical/moral dimensions conducive to slow innovation principles. We also observe a growing interest in the cultural dimension, specifically exploring potential factors that can lead to better human acceptance of these innovations.

There is considerable evidence that, in the United States, public distrust in science is amplified by a conservative ideology and by lower levels of scientific literacy. By emphasizing the discussion on reflexive modernity and (de)politicization of science and politics, we use the Eurobarometer 95.2 to explore these relationships in present-day European Union. We document a significant relationship between conservatively oriented opinions and lower scores on the scientific literacy scale and EU respondents’ levels of distrust in science. We notice that conservative attitudes – measured by dummy statements such as focus on morality instead of innovation, and national isolation due to fear of international crime instead of international co-operation – cause higher distrust in science and scientists. Unlike several studies carried out in the United States, we observe that in the European Union countries, trust in private companies to tackle with scientific issues such as climate change does not predict much when it comes to trust in science and scientists. The obtained results highlight the conceptual confluence between politicization of EU politics and expertization when it comes to policymaking at the EU level, emphasizing the debate regarding the ideological tension that fuels the distrust in science and scientists.

We are currently living in societies that are profoundly concerned about the impact of current and potential technologies on our present and future lives. How is the future of Artificial Intelligence (AI) impact perceived in the general population of the European Union? How are its effects evaluated as regards the job markets? These are just two questions that require, from the general public, an exercise of information and also an exercise of the imagination. A survey is a useful instrument to collect these public perceptions for in-depth analysis of the respective societies. This paper is based on a secondary study of a 2021 Eurobarometer survey that provides insights into the perceived future impact of AI and the social sources of its variability. It reveals evolving aspects about AI’s perception that help us better understand ourselves. The identified trends, relationships, as well as the lack of some expected correlations, offer useful information for various stakeholders interested in the social life of AI.

Dataset for the paper "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices" The dataset contains localization measurements acquired with UWB devices. We compare the proposed localization method, called FlexTDOA, with a classic TDOA implementation, and with TWR-based localization. For more information about the localization methods, please refer to the paper. The dataset contains the measurements necessary to generate all the plots in the paper. For code examples on how to read and plot the data, please check out the associated Github repository: https://github.com/lauraflu/flextdoa If you find the dataset useful, please consider citing our work: Pătru, G. C., Flueratoru, L., Vasilescu, I., Niculescu, D., &amp; Rosner, D. (2023). FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices. <em>IEEE Access</em>.

Dataset for the paper "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices" The dataset contains localization measurements acquired with UWB devices. We compare the proposed localization method, called FlexTDOA, with a classic TDOA implementation, and with TWR-based localization. For more information about the localization methods, please refer to the paper. The dataset contains the measurements necessary to generate all the plots in the paper. For code examples on how to read and plot the data, please check out the associated Github repository: https://github.com/lauraflu/flextdoa If you find the dataset useful, please consider citing our work: Pătru, G. C., Flueratoru, L., Vasilescu, I., Niculescu, D., &amp; Rosner, D. (2023). FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices. <em>IEEE Access</em>.

In this paper, we propose FlexTDOA, an indoor localization method and a system using ultra-wideband (UWB) radios. Our method uses time-difference of arrival (TDOA) localization so that the user device remains passive and is able to compute its location simply by listening to the communication between the fixed anchors, ensuring the scalability of our system. The anchors communicate using a custom-designed, flexible time-division multiple-access (TDMA) scheme in which time is divided in slots, and in each slot one anchor interrogates one or more anchors which respond in the same slot. Clock synchronization between the anchors is not needed. We implemented FlexTDOA on in-house designed hardware using a commercial UWB module. We compare FlexTDOA against the classic TDOA approach and range-based localization in a deployment of ten anchors and one tag, both with and without obstructions. Results show that FlexTDOA achieves the highest localization accuracy in most of the scenarios. We also evaluate the localization accuracy under multiple conditions by varying parameters such as the number of responses, their order, and the number of anchors. We simulate and evaluate the effect of the physical speed of the tag on the choice of optimum system parameters.