ethicalabs/Research-Intent-Collab huggingface dataset:
DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

Abstract                With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of                                                                                                  ≈                                                  2.3                                                                the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.
Generative AI and the Arts: An exploration of the Social and Cultural Impact of Generative AI in the Visual Arts

This thesis aims to expand the discussion on the potential influence of Artificial Intelligence (AI) on the visual arts and artists, focusing on the role of generative AI on the production of artworks. The study aims to reflect how generative systems may affect the production of art, the understanding of the creative process behind any result as well as the appreciation, or lack thereof, of artworks produced by generative AI systems. For this research, a mixed-methods approach was adopted, combining elements such as individual interviews with three experts in the visual arts and two questionnaires of participants in between the ages of 18 to 64. Results on the analysis of the questionnaires’ responses seems to point to the fact that AI-generated art is received as less authentic and/or valuable, thus significantly impacting any audience’s appreciation and blurring the understanding of the creative process. However, the results also demonstrated a strong correlation between an interest (or curiosity) of AI-generated artwork and age demographics: usually younger generations of respondents indicated that a collaboration with AI systems could produce art which can lead to significant developments in Western culture, concerning the arts. The findings aim to open up a discussion on how audiences perceive AI-generated art in contrast to human art. Furthermore, to share with the reader the potential significance, culturally and sociologically, of this new advent on Artificial Intelligence and its ever-growing presence in many visual arts fields. The study aims to contribute to the literature by providing primarily qualitative insights into the always evolving arts in the digital age, and striving to strike up a conversation on the larger social and cultural perception of large-scale AI models and its marketable perils. Nonetheless, further research is recommended in order to explore, in a much grander scale, the long-term effects of AI-generative models on the arts across diverse demographic groups and cultural contexts.
Open-access mega-journals

PurposeOpen-access mega-journals (OAMJs) represent an increasingly important part of the scholarly communication landscape. OAMJs, such asPLOS ONE, are large scale, broad scope journals that operate an open access business model (normally based on article-processing charges), and which employ a novel form of peer review, focussing on scientific “soundness” and eschewing judgement of novelty or importance. The purpose of this paper is to examine the discourses relating to OAMJs, and their place within scholarly publishing, and considers attitudes towards mega-journals within the academic community.Design/methodology/approachThis paper presents a review of the literature of OAMJs structured around four defining characteristics: scale, disciplinary scope, peer review policy, and economic model. The existing scholarly literature was augmented by searches of more informal outputs, such as blogs and e-mail discussion lists, to capture the debate in its entirety.FindingsWhile the academic literature relating specifically to OAMJs is relatively sparse, discussion in other fora is detailed and animated, with debates ranging from the sustainability and ethics of the mega-journal model, to the impact of soundness-only peer review on article quality and discoverability, and the potential for OAMJs to represent a paradigm-shifting development in scholarly publishing.Originality/valueThis paper represents the first comprehensive review of the mega-journal phenomenon, drawing not only on the published academic literature, but also grey, professional and informal sources. The paper advances a number of ways in which the role of OAMJs in the scholarly communication environment can be conceptualised.
Monitoring change point for diffusion parameter based on discretely observed sample from stochastic differential equation models

Stochastic differential equation (SDE) models are useful in describing complex dynamical systems in science and engineering. In this study, we consider a monitoring procedure for an early detection of dispersion parameter change in SDE models. The proposed scheme provides a useful diagnostic analysis for phase I retrospective study and develops a flexible and effective control chart for phase II prospective monitoring. A standardized control chart is constructed, and a bootstrap method is used to estimate the mean and variance of the monitoring statistic. The control limit is obtained as an upper percentile of the maximum value of a standard Wiener process. The proposed procedure appears to have a manageable computational complexity for online implementation and also to be effective in detecting changes. We also investigate the performance of the exponentially weighted mean squared control charts for the continuous SDE processes. A simulation method is used to study the empirical sizes and the average run length characteristics of the proposed scheme, which also demonstrates the effectiveness of our method. Finally, we provide an empirical example for illustration. Copyright © 2014 John Wiley &amp; Sons, Ltd.
“Brazilian style science”—an analysis of the difference between Brazilian and international Computer Science departments and graduate programs using social networks analysis and bibliometrics

In this paper, we compare the Brazilian Computer Science Graduate Programs of levels 6 and 7 (which CAPES assume to be equivalent to good international ones) and those departments best ranked in three different international rankings. We used both bibliometric and social networks analysis metrics, and we could see that there is a great difference between the results achieved by the national and international programs. The results from a principal component analysis show that the Brazilian programs are a class of their own and do not share the characteristics of the best international departments. We also analyzed the CAPES grade system and show that it is consistent, even if based on different metrics than those used internationally. It is safe to conclude that there is a “Brazilian Science” that is different from the worldwide accepted one and that derives, in a certain way, from an observer effect. We discuss the observer effect in Science and how scientific output could actually be measured.
How are ontologies implemented to represent clinical practice guidelines in clinical decision support systems: protocol for a systematic review

Abstract                 Background                 Clinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems.                                Methods                 This review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data.                                Discussion                 The study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine.                                Systematic review registration                 PROSPERO CRD42018106501
Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

SUMMARYEleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgareL.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.
Molecular Dynamics Simulation of the Diffusion Dynamics of Linear DNA Fragments in Dilute Solution with the Parmbsc1 Force Field and Comparison with Experimental Data and Theoretical Models

Detailed atomistic molecular dynamics (MD) simulations of dilute aqueous solutions of short DNA molecules of linear architecture and molecular length ranging from 8 to 60 base pairs are performed f...
Comprehensive Monitoring of Air Pollution Hotspots Using Sparse Sensor Networks

Urban air pollution hotspots pose significant health risks, yet their detection and analysis remain limited by the sparsity of public sensor networks. This paper addresses this challenge by combining predictive modeling and mechanistic approaches to comprehensively monitor pollution hotspots. We enhanced New Delhi's existing sensor network with 28 low-cost sensors, collecting PM2.5 data over 30 months from May 1, 2018, to Nov 1, 2020. Applying established definitions of hotspots to this data, we found the existence of additional 189 hidden hotspots apart from confirming 660 hotspots detected by the public network. Using predictive techniques like Space-Time Kriging, we identified hidden hotspots with 95% precision and 88% recall with 50% sensor failure rate, and with 98% precision and 95% recall with 50% missing sensors. The projected results of our predictive models were further compiled into policy recommendations for public authorities. Additionally, we developed a Gaussian Plume Dispersion Model to understand the mechanistic underpinnings of hotspot formation, incorporating an emissions inventory derived from local sources. Our mechanistic model is able to explain 65% of observed transient hotspots. Our findings underscore the importance of integrating data-driven predictive models with physics-based mechanistic models for scalable and robust air pollution management in resource-constrained settings.
Topological Signal Processing over Simplicial Complexes

The goal of this paper is to establish the fundamental tools to analyze signals defined over a topological space, i.e. a set of points along with a set of neighborhood relations. This setup does not require the definition of a metric and then it is especially useful to deal with signals defined over non-metric spaces. We focus on signals defined over simplicial complexes. Graph Signal Processing (GSP) represents a special case of Topological Signal Processing (TSP), referring to the situation where the signals are associated only with the vertices of a graph. Even though the theory can be applied to signals of any order, we focus on signals defined over the edges of a graph and show how building a simplicial complex of order two, i.e. including triangles, yields benefits in the analysis of edge signals. After reviewing the basic principles of algebraic topology, we derive a sampling theory for signals of any order and emphasize the interplay between signals of different order. Then we propose a method to infer the topology of a simplicial complex from data. We conclude with applications to real edge signals and to the analysis of discrete vector fields to illustrate the benefits of the proposed methodologies.
Dashboard design and usability study for geospatially enabled information seeking to assist pandemic response and resilience

Counties in Missouri are primarily rural. Rural communities often consist of individuals with poor health, lower economic status, and lack of public health infrastructure. During the COVID- 19 pandemic, most research was centered around urban-based data and thus did not provide the full-picture of vulnerabilities present in rural counties for stakeholders to consider when proactively planning for pandemics and making policies in regards to mitigation. To bridge the gap of urban and rural data availability, our team developed two interactive COVID-19 risk assessment dashboards using a 3-step design process that included identifying dashboard functionality based on the goals of stakeholders, collecting COVID-19 risk factor data, and selecting the appropriate type of dashboard visualizations in order for stakeholder needs to be met. Database processes were also created to promote a dynamic design in which risk factors can be easily updated, added, and removed from the risk assessment as COVID-19 progresses and more evidence is collected, keeping the risk assessment relevant. Using our dashboards, users can create customized risk assessments based on six categories of risk: susceptibility, transmission, accessibility, socioeconomic, health culture, and exposure, and geospatially visualize risk throughout counties with the ability to apply a rural/urban filter. Users can also drill-down to a specific county and learn about the prevalence and magnitude of 87 risk factors while looking for spatial trends and how counties with specific risk profiles were affected by COVID-19. A usability study was conducted to ensure that our platform is meaningful and can be easily navigated to aid with pandemic mitigation, healthcare planning, and research. An optimized version of this tool would not only help with planning for COVID-19 variants, future pandemics, and research in Missouri, but also be applied to all states of the United Stat
Islamic Law on Animal Rights and Welfare in the Sri Lankan Context:  An Islamic Analysis on the Animal Welfare Bill of 2006

The almighty Allah (SWT) has created all living beings, humans, birds, animals and insects.  He is who strongly asks people to treat animals with compassion and not to abuse them which are worthy of consideration and respect. In Sri Lanka, varying concepts are followed among different communities: Buddhists and Hindus consider slaughtering quadrupeds as a cruel act. Meanwhile, Muslims consider the similar act as a divine obligation (Qurbani and Uluhiyya). This study compares the Animal Welfare Bill, initiated by the Commission of Law of Sri Lanka on 2006, with the tenets of Islamic Law (Shariah) through a review of literature and thematic content analysis to identify areas where the right of the animal is violated. According to Animal Welfare Bill of 2006, these areas refer to the slaughter of animals for meals, transport of animals, use of live animals for scientific purposes, and cruelty relating to animals. These identified areas coincide with the identified areas espoused by the Prophet Mohamed (PBUH), and a solution produced to prevent such violation with the instruction of almighty Allah around 1400 years ago.
A collaborative filtering-based recommendation system for an online high-end retailer

In online retail platforms, consumers seek to find the products that are best suited for their needs while limiting their search efforts. With the growing trend in online shopping, retail companies utilize a range of tools to assist customers in their journey and improve their purchase experience. One of the tools that can minimize these exploration efforts is recommendation systems that suggest a tailored set of available product options to consumers based on their preferences. In this thesis, we focus on a high-end Turkish retailer that did not utilize such engines in its practice and study the value that these systems can provide to the company. To that end, we implement a collaborative filtering-based recommendation system that uses the similarity of the consumers to derive their preferences and suggest item sets for their next purchase. We evaluate the recommendation model with transactions data to acquire the hyper-parameters and test it on the transactions made in the last month and provide recommendations on three granularity levels. We also analyze the predicted preferences to suggest bundling options and derive empirical insights. We found that by generating 20 suggestions for each customer in their shopping session, the engine can reach an accuracy of 38% at brand-level and 7% at itemlevel while using cosine similarity as its similarity metric.
Unified benchmarking and characterization protocol for nanomaterial-based heterogeneous photodetector technologies

Over the last few years, there has been a rapid growth towards demonstrating highly sensitive, fast photodetectors using photoactive nano-materials. As with any other developing and highly inter-disciplinary field, the existing reports exhibit a large spread in the data due to less optimized materials and non-standardized characterization protocols. This calls for a streamlined performance benchmarking requirement to accelerate technological adoption of the promising candidates. The goal of this paper is four-fold: (i) to address the key challenges to perform such benchmarking exercise; (ii) to elucidate the right figures of merit to look for, and in particular, to demonstrate that noise-equivalent-power ($N\!E\!P$) is a more reliable sensitivity metric than other commonly used ones, such as responsivity ($R$) and specific detectivity ($D^*$); (iii) to propose $N\!E\!P$ versus frequency of operation ($f$) as a single, unified benchmarking chart that could be used for apple-to-apple comparison among heterogeneous detector technologies; and (iv) to propose a simple, yet streamlined characterization protocol that can be followed while reporting photodetector performance.
Impact of Human Resource Information System (HRIS): Substituting or Enhancing HR Function

This study endeavors at foreseeing the human related facets of a Human Resources Information System (HRIS) in Pakistan. Investing in the HR function has been the least priority of the management however the technological enrichment has led HR function to its strategic role in the organization. The perception of HR function as strategic partner has accentuated the technological advancements in the field of HR and organizations having superior HRIS have a competitive edge over its competitors. Survey results from HR professionals across Pakistan reveal that HRIS has added value to the performance of HR function hence enhanced the performance of the organization. HRIS has not only enhanced the HR functions through technological advancements and value additions but it also has a capability to replace the HR professionals in future.