Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospective analysis of the physiological changes caused by prolonged use of N95-type masks

INTRODUCTION: The clinical and physiological effects of long-duration use of N95-type masks without ventilation valves, on health-care workers during the coronavirus disease-2019 (COVID-19) pandemic, were evaluated. METHODS: All volunteering personnel working in operating theater or intensive care unit, using nonventilated N95 type respiratory masks, minimum for a 2-h noninterrupted duration were observed. The partial oxygen saturation (SpO2) and heart rate (HR) were recorded before wearing the N95 mask and at 1st and 2nd h. Volunteers were then questioned for any symptoms. RESULTS: A total of 210 measurements were completed in 42 (24 males and 18 females) eligible volunteers, each having 5 measurements, on different days. The median age was 32.7. Premask, 1st h, and 2nd h median values for SpO2 were 99%, 97%, and 96%, respectively (P < 0.001). The median HR was 75 premask, 79 at 1st h, and 84/min at 2nd h (P < 0.001). A significant difference between all three consecutive measurements of HR was achieved. Statistical difference was only reached between premask and other SpO2 measurements (1st and 2nd h). Complaints seen in the group were head ache (36%), shortness of breath (27%), palpitation (18%), and nausea feeling (2%). Two individuals took off their masks to breathe, on 87th and 105th min, respectively. CONCLUSIONS: Long duration (>1 h) use of N95-type masks causes a significant reduction in SpO2 measurements and increase in HR. Despite being an essential personal protective equipment in COVID-19 pandemic, it should be used with short intermittent time periods in health-care providers with known heart disease, pulmonary insufficiency, or psychiatric disorders.
COMPARATIVE ANALYSIS OF ANTIMICROBIAL ACTIVITY OF LEAF EXTRACTS FROM THREE SPECIES OF GENUS HYDROCOTYLE ON PATHOGENS

Medicinal plants have been a major source of therapeutic agents for alleviation and cure diseases. The antimicrobial activity of the extracts of three species of genus Hydrocotyle was analyzed by using well diffusion method paper disk diffusion method and minimum inhibitory concentration. In ethanoic extract of H. ramiflora, maximum inhibition zone diameter among all the tested microorganisms was obtained in Enterobacter cloaceae with diameter of 6.1±0.5 mm. H. ramiflora showed maximum inhibition zone with diameter of 6.3±0.3 mm in Escherichia coli, 6.1±0.3 mm in Enterobacter aerogenes, and 6.4±0.4 mm in Klebsiella pneumoniae. For all the tested microorganisms, ethanol extracts of H. maritima showed maximum antibacterial activity in E. coli. Maximum inhibition zone diameter of H. maritima were obtained in E. cloaceae and in E. coli with diameter 6.2±0.4 mm and 6.4±0.5 mm, respectively. In ethanolic extract of H. nepalensis, maximum inhibition zone diameter among all the tested microorganisms was obtained in E. coli DD12 with diameter of 6.4±0.5 mm (Table 3). H. nepalensis showed maximum inhibition zone with diameter of 6.2±0.4 mm in E. cloaceae, 6.3±0.4 mm in E. aerogenes, and 6.3±0.3 mm in K. pneumoniae. Although the two stock strains are different, there was not significant differences (p > 0.05). In addition, for each strain, there was not significant differences among four various concentrations (p > 0.05).
Intrinsic dimensionality and generalization properties of the R-norm inductive bias

We study the structural and statistical properties of $\mathcal{R}$-norm minimizing interpolants of datasets labeled by specific target functions. The $\mathcal{R}$-norm is the basis of an inductive bias for two-layer neural networks, recently introduced to capture the functional effect of controlling the size of network weights, independently of the network width. We find that these interpolants are intrinsically multivariate functions, even when there are ridge functions that fit the data, and also that the $\mathcal{R}$-norm inductive bias is not sufficient for achieving statistically optimal generalization for certain learning problems. Altogether, these results shed new light on an inductive bias that is connected to practical neural network training.
Robots Enact Malignant Stereotypes

Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6], or both, in the case of large image and caption models such as OpenAI CLIP [14]. In this paper, we evaluate how ML bias manifests in robots that physically and autonomously act within the world. We audit one of several recently published CLIP-powered robotic manipulation methods, presenting it with objects that have pictures of human faces on the surface which vary across race and gender, alongside task descriptions that contain terms associated with common stereotypes. Our experiments definitively show robots acting out toxic stereotypes with respect to gender, race, and scientifically-discredited physiognomy, at scale. Furthermore, the audited methods are less likely to recognize Women and People of Color. Our interdisciplinary sociotechnical analysis synthesizes across fields and applications such as Science Technology and Society (STS), Critical Studies, History, Safety, Robotics, and AI. We find that robots powered by large datasets and Dissolution Models (sometimes called “foundation models”, e.g. CLIP) that contain humans risk physically amplifying malignant stereotypes in general; and that merely correcting disparities will be insufficient for the complexity and scale of the problem. Instead, we recommend that robot learning methods that physically manifest stereotypes or other harmful outcomes be paused, reworked, or even wound down when appropriate, until outcomes can be proven safe, effective, and just. Finally, we discuss comprehensive policy changes and the potential of new interdisciplinary research on topics like Identity Safety Assessment Frameworks and Design Justice to better understand and address these harms.
Language variation and algorithmic bias: understanding algorithmic bias in British English automatic speech recognition

All language is characterised by variation which language users employ to construct complex social identities and express social meaning. Like other machine learning technologies, speech and language technologies (re)produce structural oppression when they perform worse for marginalised language communities. Using knowledge and theories from sociolinguistics, I explore why commercial automatic speech recognition systems and other language technologies perform significantly worse for already marginalised populations, such as second-language speakers and speakers of stigmatised varieties of English in the British Isles. Situating language technologies within the broader scholarship around algorithmic bias, consider the allocative and representational harms they can cause even (and perhaps especially) in systems which do not exhibit predictive bias, narrowly defined as differential performance between groups. This raises the question whether addressing or “fixing” this “bias” is actually always equivalent to mitigating the harms algorithmic systems can cause, in particular to marginalised communities.
What People Think AI Should Infer From Faces

Faces play an indispensable role in human social life. At present, computer vision artificial intelligence (AI) captures and interprets human faces for a variety of digital applications and services. The ambiguity of facial information has recently led to a debate among scholars in different fields about the types of inferences AI should make about people based on their facial looks. AI research often justifies facial AI inference-making by referring to how people form impressions in first-encounter scenarios. Critics raise concerns about bias and discrimination and warn that facial analysis AI resembles an automated version of physiognomy. What has been missing from this debate, however, is an understanding of how “non-experts” in AI ethically evaluate facial AI inference-making. In a two-scenario vignette study with 24 treatment groups, we show that non-experts (N = 3745) reject facial AI inferences such as trustworthiness and likability from portrait images in a low-stake advertising and a high-stake hiring context. In contrast, non-experts agree with facial AI inferences such as skin color or gender in the advertising but not the hiring decision context. For each AI inference, we ask non-experts to justify their evaluation in a written response. Analyzing 29,760 written justifications, we find that non-experts are either “evidentialists” or “pragmatists”: they assess the ethical status of a facial AI inference based on whether they think faces warrant sufficient or insufficient evidence for an inference (evidentialist justification) or whether making the inference results in beneficial or detrimental outcomes (pragmatist justification). Non-experts’ justifications underscore the normative complexity behind facial AI inference-making. AI inferences with insufficient evidence can be rationalized by considerations of relevance while irrelevant inferences can be justified by reference to sufficient evidence. We argue that participatory approaches contribute valuable insights for the development of ethical AI in an increasingly visual data culture.
Modding the Pliable Machine: Unpacking the Creative and Social Practice of Upkeep at the Makerspace

As digital fabrication machines have become more accessible and widely available, practitioners in maker communities have become increasingly responsive to the opportunities to achieve bespoke modifications, known colloquially as ‘modding’. Drawing on interviews with five experienced makers who engage in modding a laser cutter, along with ethnographic observations of maker-machine interactions, we analyse makers’ experiences and ‘war stories’ to frame modding as a prevalent but less explored maker activity. We highlight how makers care for machines, how they cope with risks when engaging in modding, and how mods are essentially creative projects. Based on our findings, we present the conceptualisation of the ‘pliable machine’ – a socio-technical system constituted by, (1) an accessible machine that can be altered, (2) maker skills that go beyond intended use, and (3) a surrounding ‘maker culture’ of caring, sharing and experimentation. Treating the machine as a material offers an alternative perspective on our interactions with technology; we show how the laser cutter becomes pliable in the hands of those who mod.
A Data-driven analysis of the interplay between Criminological theory and predictive policing algorithms

Previous studies have focused on the biases and feedback loops that occur in predictive policing algorithms. These studies show how systemically and institutionally biased data leads to these feedback loops when predictive policing algorithms are applied in real life. We take a step back, and show that the choice in algorithm can be embedded in a specific criminological theory, and that the choice of a model on its own even without biased data can create biased feedback loops. By synthesizing “historical” data, in which we control the relationships between crimes, location and time, we show that the current predictive policing algorithms create biased feedback loops even with completely random data. We then review the process of creation and deployment of these predictive systems, and highlight when good practices, such as fitting a model to data, “go bad” within the context of larger system development and deployment. Using best practices from previous work on assessing and mitigating the impact of new technologies, we highlight where the design of these algorithms has broken down. The study also found that multidisciplinary analysis of such systems is vital for uncovering these issues and shows that any study of equitable AI should involve a systematic and holistic analysis of their design rationalities.
German AI Start-Ups and “AI Ethics”: Using A Social Practice Lens for Assessing and Implementing Socio-Technical Innovation

The current AI ethics discourse focuses on developing computational interpretations of ethical concerns, normative frameworks, and concepts for socio-technical innovation. There is less emphasis on understanding how AI practitioners themselves understand ethics and socially organize to operationalize ethical concerns. This is particularly true for AI start-ups, despite their significance as a conduit for the cultural production of innovation and progress, especially in the US and European context. This gap in empirical research intensifies the risk of a disconnect between scholarly research, innovation and application. This risk materializes acutely as mounting pressures to identify and mitigate the potential harms of AI systems have created an urgent need to rapidly assess and implement socio-technical innovation focused on fairness, accountability, and transparency. In this paper, we address this need. Building on social practice theory, we propose a framework that allows AI researchers, practitioners, and regulators to systematically analyze existing cultural understandings, histories, and social practices of “ethical AI” to define appropriate strategies for effectively implementing socio-technical innovations. We argue that this approach is needed because socio-technical innovation “sticks” better if it sustains the cultural meaning of socially shared (ethical) AI practices, rather than breaking them. By doing so, it creates pathways for technical and socio-technical innovations to be integrated into already existing routines. Against that backdrop, our contributions are threefold: (1) we introduce a practice-based approach for understanding “ethical AI”; (2) we present empirical findings from our study on the operationalization of “ethics” in German AI start-ups to underline that AI ethics and social practices must be understood in their specific cultural and historical contexts; and (3) based on our empirical findings, suggest that “ethical AI” practices can be broken down into principles, needs, narratives, materializations, and cultural genealogies to form a useful backdrop for considering socio-technical innovations. We conclude with critical reflections and practical implications of our work, as well as recommendations for future research.
How are ML-Based Online Content Moderation Systems Actually Used? Studying Community Size, Local Activity, and Disparate Treatment

Machine learning-based predictive systems are increasingly used to assist online groups and communities in various content moderation tasks. However, there are limited quantitative understandings of whether and how different groups and communities use such predictive systems differently according to their community characteristics. In this research, we conducted a field evaluation of how content moderation systems are used in 17 Wikipedia language communities. We found that 1) larger communities tend to use predictive systems to identify the most damaging edits, while smaller communities tend to use them to identify any edit that could be damaging; 2) predictive systems are used less in content areas where there are more local editing activities; 3) predictive systems have mixed effects on reducing disparate treatment between anonymous and registered editors across communities of different characteristics. Finally, we discuss the theoretical and practical implications for future human-centered moderation algorithms.
Hyper-specific prefixes

Autonomous Systems (ASes) exchange reachability information between each other using BGP---the de-facto standard inter-AS routing protocol. While IPv4 (IPv6) routes more specific than /24 (/48) are commonly filtered (and hence not propagated), route collectors still observe many of them. In this work, we take a closer look at those "hyper-specific" prefixes (HSPs). In particular, we analyze their prevalence, use cases, and whether operators use them intentionally or accidentally. While their total number increases over time, most HSPs can only be seen by route collector peers. Nonetheless, some HSPs can be seen constantly throughout an entire year and propagate widely. We find that most HSPs represent (internal) routes to peering infrastructure or are related to address block relocations or blackholing. While hundreds of operators intentionally add HSPs to well-known routing databases, we observe that many HSPs are possibly accidentally leaked routes.
Achieving Fairness via Post-Processing in Web-Scale Recommender Systems✱

Building fair recommender systems is a challenging and crucial area of study due to its immense impact on society. We extended the definitions of two commonly accepted notions of fairness to recommender systems, namely equality of opportunity and equalized odds. These fairness measures ensure that equally “qualified” (or “unqualified”) candidates are treated equally regardless of their protected attribute status (such as gender or race). We propose scalable methods for achieving equality of opportunity and equalized odds in rankings in the presence of position bias, which commonly plagues data generated from recommender systems. Our algorithms are model agnostic in the sense that they depend only on the final scores provided by a model, making them easily applicable to virtually all web-scale recommender systems. We conduct extensive simulations as well as real-world experiments to show the efficacy of our approach.
Exploring Bodily Heirlooms: Material Ways to Prolong Closeness Beyond Death

The body plays an important role in building and maintaining emotional relationships. When a loved one dies, it is inevitable that their body will decease, meaning that physical closeness can no longer be experienced. In this paper, we engage with material ways to extend experiences of physical and emotional nearness beyond death. We explore the design space of ‘bodily heirlooms’: crafted, precious, interactive objects designed to pass on bodily impressions beyond lifespan. We review cultural practices that intend to bequeath the body, and related work that addresses death, bereavement and digital heirlooms. After that, we describe the design and making process of three bodily heirlooms, reflections in and on action, and reactions of visitors who inspected the heirlooms during an exhibition. We conclude by discussing notions of heirlooms’ preciousness, the emotional bond between heirlooms and their maker and how the process can be understood as a feminist practice.
Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat

Machine translation (MT) is now widely and freely available, and has the potential to greatly improve cross-lingual communication. In order to use MT reliably and safely, end users must be able to assess the quality of system outputs and determine how much they can rely on them to guide their decisions and actions. However, it can be difficult for users to detect and recover from mistranslations due to limited language skills. In this work we collected 19 MT-mediated role-play conversations in housing and employment scenarios, and conducted in-depth interviews to understand how users identify and recover from translation errors. Participants communicated using four language pairs: English, and one of Spanish, Farsi, Igbo, or Tagalog. We conducted qualitative analysis to understand user challenges in light of limited system transparency, strategies for recovery, and the kinds of translation errors that proved more or less difficult for users to overcome. We found that users broadly lacked relevant and helpful information to guide their assessments of translation quality. Instances where a user erroneously thought they had understood a translation correctly were rare but held the potential for serious consequences in the real world. Finally, inaccurate and disfluent translations had social consequences for participants, because it was difficult to discern when a disfluent message was reflective of the other person’s intentions, or an artifact of imperfect MT. We draw on theories of grounding and repair in communication to contextualize these findings, and propose design implications for explainable AI (XAI) researchers, MT researchers, as well as collaboration among them to support transparency and explainability in MT. These directions include handling typos and non-standard grammar common in interpersonal communication, making MT in interfaces more visible to help users evaluate errors, supporting collaborative repair of conversation breakdowns, and communicating model strengths and weaknesses to users.
Boosting Efficiency of External Pipelines by Blurring Application Boundaries

Modern application development addresses increasingly specialized problems using domain-specific utilities, such as Optical Code Recognition and standalone statistical tools. The diversity of tooling, combined with the ever-growing volume of data, requires data pipelines to be both efficient and support a variety of data processing tools within the same pipeline. Existing approaches, however, impose a tradeoff between modularity and performance: on the one hand, data processing systems are specialized for fast execution of complex queries, favoring efficiency at the expense of high development costs and required domain expertise. On the other hand, highly extensible systems opt for composability at the expense of inefficient execution due to minimal assumptions about input and output formats. This paper proposes Generalized OLAP (GOLAP), a new DBMS paradigm that places automatic extensibility of functionality as a first-class design goal. GOLAP ingests external utilities to achieve the functionality provided by external modular data pipelines while maintaining the performance of natively optimized DBMS functions. Through a combination of runtime inspection and static analysis, GOLAP detects inter-utility communication inefficiencies and parallelization opportunities beyond the limits of isolated utility optimizations. It then modifies the utilities to elide unnecessary inter-utility operations and parallelizes the pipeline to increase hardware utilization. To evaluate GOLAP, we build Caesar, a prototype that optimizes simple pipelines, showing up to 22x speedup while introducing a limited instrumentation period with a slow-down of less than 17%.
VOCAL: Video Organization and Interactive Compositional AnaLytics

Current video database management systems (VDBMSs) fail to support the growing number of video datasets in diverse domains because these systems assume clean data and rely on pretrained models to detect known objects or actions. Existing systems also lack good support for compositional queries that seek events consisting of multiple objects with complex spatial and temporal relationships. In this paper, we propose VOCAL, a vision of a VDBMS that supports efficient data cleaning, exploration and organization, and compositional queries, even when no pretrained model exists to extract semantic content. These techniques utilize optimizations to minimize the manual effort required of users.