ethicalabs/Research-Intent-Collab huggingface dataset:
Biased Algorithmic Risk Assessment in criminal justice settings: How is COMPAS fraying the fabric of the right to a human decision-making in criminal procedure law

Artificial Intelligence-embedded technologies represent a blazingly new path, a foray into uncharted territory that holds a wealth, and a treasure trove, of scorching challenges that should not leave anyone lukewarm. When it comes to cutting-edge technology, you better not try your luck and the let the chips fall where they may. Artificial Intelligence-embedded technology, albeit a seemingly bedazzling gift to humankind, is not without multifarious caveats. Amongst which stands a slew of perceived perils arising out of biased algorithm risk assessment in criminal justice settings that is shaping up to topple the foundations upon which stands algorithmic fairness. Hardly any bewildering surprise stems from the fact that Artificial Intelligence-embedded technology holds the blueprint of our destiny and the disruptive power it carries. This is about the unfolding of newer and higher forms of intrusiveness in our daily lives that extend beyond our collective grasp. Building upon this, a couple of vexing questions crop up with bursting unease: should relevant stakeholders (i.e. judges, prosecutors, and lawyers) rely upon algorithmic risk assessment (i.e. COMPAS) in the purview of criminal justice? On the flip side, should machines be entrusted with the task of making high-stake decisions in the realm of criminal procedure law settings? Bearing these burning (research) questions very firmly in mind, this paper adamantly contends that machines should not wield the power of making high-stake decisions that may bear on heavily citizens’ fundamental rights (i.e. the right to a due process, a central tenet of criminal procedure law) or inordinate harms may otherwise occur (i.e. the loss of functional reputation of the system of administration of justice).
The Unfolding of the Spectra of Low Energy Galactic Cosmic Ray H and He Nuclei as the Voyager 1 Spacecraft Exits the Region of Heliospheric Modulation

21 pages, 6 figures
Value of resilience-based solutions on critical infrastructure protection: Comparing with robustness-based solutions

Abstract   Additional consideration of recovery rapidity makes resilience-based solutions on critical infrastructure protection conceptually more advantageous than robustness-based solutions, but existing studies have not uncovered how large their differences could be. By considering a pre-event protection strategy frequently adopted both in practice and in the literature, i.e., protecting or retrofitting a set of weak components under a limited budget, this paper introduces four mathematical models and their solution algorithms for exactly identifying the optimal robustness-based and resilience-based protection strategies for critical infrastructure systems against worst-case malicious attacks and natural hazards, respectively. By comparing with the optimal robustness-based protection strategy, the value of resilience-based solution is then quantified by how much the system resilience can be improved and how much the system loss can be mitigated. Taking the electric power transmission system in Shelby County, USA as an example, results show that the optimal resilience-based solutions improve the worst-case system resilience by at most 1.29% and reduce the worst-case system loss by at most 13.25%, and enhance the seismic resilience by at most 0.16% and mitigate the system loss by at most 5.27% under seismic hazards at a 2% probability of being exceeded in 50 years. Other model parameters and several other systems are also investigated.
Approximation of the solution of the backward stochastic differential equation. Small noise, large sample and high frequency cases

The author ``present[s] a review of some recently obtained results on [the] estimation of the solution of a backward stochastic differential equation in the Markovian case.'' Two of these results can be found in [\textit{G. Samvel} and \textit{Y. Kutoyants}, Armen. J. Math. 7, No. 1, 59--79 (2015; Zbl 1322.60123)] and [\textit{Y. A. Kutoyants} and \textit{L. Zhou}, J. Stat. Plann. Inference 150, 111--123 (2014; Zbl 1287.62017)].
Beyond Corporate Social Responsibility Reporting and Indicators.

Over the past few years, the expectations of stakeholders regarding Sustainable Development have strengthened the importance of CSR or Sustainable Development practices in companies. In France, NRE laws and more recently the Grenelle Environment Forum required from companies to disclose and publish information on environmental, social and societal issues. On the international level, the Global Reporting Initiative gives a framework for CSR reporting. Companies tend to improve their CSR policies by setting action plans, objectives, and publishing dedicated reports. Subsequently, to assess their performance, companies need to design specific indicators in order to measure environmental, social and societal information. It is all the more challenging that intangible information, such as biodiversity or human capital, are very difficult to evaluate. Still, when comparing the performance of companies, even in the same sector of activity, it appears that indicators are actually very hetero-geneous and do not allow to perform a proper comparison. This thesis will explore current practices of CSR or Sustainable Development reporting and more specifically the use of indicators in the private sector as well as their limitations and areas for improvement. A mutli-method approach, including a literature review, a case-study and an interview, has been used to perform the research.
GluonTS: Probabilistic Time Series Models in Python

We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and experimentation with time series models for common tasks such as forecasting or anomaly detection. It provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating model accuracy.
Content-Based Search for Deep Generative Models

The growing proliferation of customized and pretrained generative models has made it infeasible for a user to be fully cognizant of every model in existence. To address this need, we introduce the task of content-based model search: given a query and a large set of generative models, finding the models that best match the query. As each generative model produces a distribution of images, we formulate the search task as an optimization problem to select the model with the highest probability of generating similar content as the query. We introduce a formulation to approximate this probability given the query from different modalities, e.g., image, sketch, and text. Furthermore, we propose a contrastive learning framework for model retrieval, which learns to adapt features for various query modalities. We demonstrate that our method outperforms several baselines on Generative Model Zoo, a new benchmark we create for the model retrieval task.
Governing the Automated Welfare State: Translations between AI Ethics and Anti-discrimination Regulation

There is an increasing demand to utilize technological possibilities in the Nordic public sector. Automated decision-making (ADM) has been deployed in some areas towards that end. While ADM is associated with a range of benefits, research shows that its use, with elements of AI, also implicates risks of discrimination and unfair treatment, which has stimulated a flurry of normative guidelines. This article seeks to explore how a sample of these international high-level principled ideas on fairness translate into the specific governance of ADM in national public-sector authorities in Sweden. It does so by answering the question of how ideas about AI ethics and fairness are considered in relation to regulation on anti-discrimination in Swedish public-sector governance. By using a Scandinavian institutionalist approach to translation theory, we trace how ideas about AI governance and public-sector governance translate into state-authority practice; specifically, regarding the definition of ADM, how AI has impacted it as both discourse and technology, and the ideas of ‘ethicsʼ and ‘discriminationʼ. The results indicate that there is a variance in how different organizations understand and translate ideas about AI ethics and discrimination. These tensions need to be addressed in order to develop AI governance practices.
Additional file 7 of The pattern of histone H3 epigenetic posttranslational modifications is regulated by the VRK1 chromatin kinase

Additional file 7. Fig. S7: Effect of the VRK-IN-1 inhibitor on the levels of H3K9 acetylation and methylation in U2OS cells.
Social Media Use and Adverse Mental Health and Sleep Effects among Adolescents and Adults A Systematic Review

Aim: To review the association between social media use and adverse mental health and sleep effects among adolescents and adults.  Background: Previous research indicates that US adolescents and adults spend on average 8 – 10 hours a day on their smartphones. There is considerable variability in social media use within and outside the US. Much and often most of this time is spent on social media platforms. While there are studies that suggest that social media use can have adverse mental health and sleep impacts, few have investigated the relationships in depth, and fewer still are longitudinal analyses.  Results: Systematic search generated 9,202 research studies, of which 73 were included in this literature review based on examination of the following assessed variables: anxiety, depression, loneliness, social media network use and sleep quality. Strong positive associations between social media use and adverse mental health and sleep effects among adolescents and adults were detected overall in these studies.  Conclusion: This literature review confirms and strengthens the evidence that social media use is associated with adverse mental health and sleep effects among both adolescents and adults. Future research using other study designs is needed to better understand and develop interventions to mitigate the adverse effects on mental health and sleep quality of social media use.
FreeMask: Rethinking the Importance of Attention Masks for Zero-Shot Video Editing

Text-to-video diffusion models have made remarkable advancements. Driven by their ability to generate temporally coherent videos, research on zero-shot video editing using these fundamental models has expanded rapidly. To enhance editing quality, structural controls are frequently employed in video editing. Among these techniques, cross-attention mask control stands out for its effectiveness and efficiency. However, when cross-attention masks are naively applied to video editing, they can introduce artifacts such as blurring and flickering. Our experiments uncover a critical factor overlooked in previous video editing research: cross-attention masks are not consistently clear but vary with model structure and denoising timestep. To address this issue, we propose the metric Mask Matching Cost (MMC) that quantifies this variability and propose FreeMask, a method for selecting optimal masks tailored to specific video editing tasks. Using MMC-selected masks, we further improve the masked fusion mechanism within comprehensive attention features, e.g., temp, cross, and self-attention modules. Our approach can be seamlessly integrated into existing zero-shot video editing frameworks with better performance, requiring no control assistance or parameter fine-tuning but enabling adaptive decoupling of unedited semantic layouts with mask precision control. Extensive experiments demonstrate that FreeMask achieves superior semantic fidelity, temporal consistency, and editing quality compared to state-of-the-art methods.
Standards-based Internet of Things sub-GHz environmental sensor networks.

In recent years there has been shift in the use of wireless sensor networks from standalone systems that use bespoke methods of communication and data transfer to systems that use Internet standards and can interact more directly with the Internet. This has allowed wireless sensor networks to become a key enabler of the Internet of Things; however, the same is not true for environmental sensor networks as the focus of most existing research into Internet of Things wireless sensor networks has been on 2.4 GHz designs for indoor, urban and agricultural applications. In these applications, power, Internet connectivity and physical access are less of a challenge when compared to a typical environmental sensor network. Environmental sensor networks are used for monitoring natural processes and are generally deployed in harsh, remote environments where these factors are more of a concern. Sub-GHz radios are commonly used for communication due to their increased range and desirable propagation characteristics. Unlike wireless sensor networks, environmental sensor networks have been slow to adopt Internet standards and have continued to rely on bespoke methods of communication and data transfer, keeping their usability low. This has impeded the adoption of environmental sensor networks for earth sciences research.This thesis investigates whether the Internet standards that have helped to make wireless sensor network an important part of the Internet of Things can be applied to sub-GHz environmental sensor networks. It is demonstrated that 6LoWPAN can successfully be used with an 868 MHz network in a series of real-world deployments in the Highlands of Scotland that collected usable earth science data and facilitated research in other fields. Additionally, the suitability of these standards for real-world networks is assessed in terms of energy, throughput and latency performance and compared to a theoretical 2.4 GHz network. An publicly available open source Contiki radio driver for the CC1120 was developed as part of this work. Additionally, timing parameters for using ContikiMAC with 868 MHz radios were determines and shared with researchers at other institutions, facilitating further research into sub-GHz IoT ESNs by other researchers.
AI Surveillance Technologies and Cybersecurity: Bridging National Security and Privacy Rights

The evolution of national security architectures has begun from reactionary monitoring to proactive, predictive, and "intelligent oversight" in the Indian context. Then, the study examines the impact of deploying advanced surveillance technologies such as facial recognition and behavior analytics on the Smart Cities Mission and their supporting effect on public safety as well as creating significant "regulatory gaps" because of increasing surveillance capabilities that cause fundamental privacy violations. The analysis sheds light on how developing these types of sociotechnical harms may create conditions where algorithmic-biased systems of "digital Jim Crow" continue to reinforce existing inequality based on historical factors but are represented as objective data analysis. Finally, the paper examines how India's continued development trajectory compares to regulators across the globe, specifically contrasting the risk-based regulatory regime of the EU with that of China. China’s State centered model, exporting capabilities of surveillance. Ultimately calls for a coordinated strategy, based on the principles of "Industry 5.0", strong legislative oversight and public knowledge, to reconcile demands for national security while maintaining civil rights in a democracy.
THE RELATIONSHIP BETWEEN OCCUPATIONAL SAFETY AND HEALTH BEHAVIOR AND WORKPLACE ACCIDENTS IN MICRO, SMALL AND MEDIUM RATTAN BUSINESSES IN GRESIK DISTRICT

Work accidents are caused by events in the work environment which are certain to harm various parties and even lead to disability and even the most fatal event, namely death. Work accidents in Gresik Regency Rattan MSME's that appear to dominate are due to a lack of knowledge, attitudes, compliance and Personal Protective Equipment. The aim is to find out how the relationship between Occupational Safety and Health behavior and the incidence of work accidents in Rattan Micro, Small, and Medium Enterprises in Gresik Regency. The study was conducted cross-sectionally and classified as quantitative. The total population involved is 278 workers who worked in 16 semi-finished rattan MSME's. The selected participants were randomized (simple random sampling) and Slovin's formula, resulting in a total of 165 workers as respondents. Data collection was conducted through three main methods, namely direct observation, interviews, and questionnaires. The variables used are OHS behavior which includes OHS knowledge, OHS attitude, OHS compliance and the use of personal protective equipment (independent), while the incidence of work accidents (dependent). Data analysis used 2 statistical test methods, both Chi-square and Fisher's exact test. The results of the study concluded several statements, there is a relationship between knowledge and work accidents (p = 0.008), there is a relationship between attitude, compliance with work accidents (p = 0.000), there is a relationship between Personal Protective Equipment and work accidents (p = 0.004). The dominant factor influencing work accidents is the factor of compliance with work instructions (OR 13.993). Suggestions for improvement are that it is hoped that Gresik Regency Rattan MSME 's will impose regular training on work safety procedures to workers in order to increase workers awareness as well as to ensure that PPE is available and used.
Time, Tense and Aspect in Natural Language Database Interfaces

Most existing natural language database interfaces (NLDBs) were designed to be used with database systems that provide very limited facilities for manipulating time-dependent data, and they do not support adequately temporal linguistic mechanisms (verb tenses, temporal adverbials, temporal subordinate clauses, etc.). The database community is becoming increasingly interested in temporal database systems, that are intended to store and manipulate in a principled manner information not only about the present, but also about the past and future. When interfacing to temporal databases, supporting temporal linguistic mechanisms becomes crucial.   We present a framework for constructing natural language interfaces for temporal databases (NLTDBs), that draws on research in tense and aspect theories, temporal logics, and temporal databases. The framework consists of a temporal intermediate representation language, called TOP, an HPSG grammar that maps a wide range of questions involving temporal mechanisms to appropriate TOP expressions, and a provably correct method for translating from TOP to TSQL2, TSQL2 being a recently proposed temporal extension of the SQL database language. This framework was employed to implement a prototype NLTDB using ALE and Prolog.
Nonperturbative investigations of SU(3) gauge theory with eight dynamical flavors

We present our lattice studies of SU(3) gauge theory with $N_f$ = 8 degenerate fermions in the fundamental representation. Using nHYP-smeared staggered fermions we study finite-temperature transitions on lattice volumes as large as $L^3 \times N_t = 48^3 \times 24$, and the zero-temperature composite spectrum on lattice volumes up to $64^3 \times 128$. The spectrum indirectly indicates spontaneous chiral symmetry breaking, but finite-temperature transitions with fixed $N_t \leq 24$ enter a strongly coupled lattice phase as the fermion mass decreases, which prevents a direct confirmation of spontaneous chiral symmetry breaking in the chiral limit. In addition to the connected spectrum we focus on the lightest flavor-singlet scalar particle. We find it to be degenerate with the pseudo-Goldstone states down to the lightest masses reached so far by non-perturbative lattice calculations. Using the same lattice approach, we study the behavior of the composite spectrum when the number of light fermions is changed from eight to four. A heavy flavor-singlet scalar in the 4-flavor theory affirms the contrast between QCD-like dynamics and the low-energy behavior of the 8-flavor theory.
Protected areas, Indigenous rights and land restitution: the Ogiek judgment of the African Court of Human and Peoples’ Rights and community land protection in Kenya

AbstractIn May 2017, the relationship between conservation and human and Indigenous peoples' rights was considered for the first time by the African Court of Human and Peoples' Rights. In a case brought by the Indigenous Ogiek of Kenya, the Court stated that the preservation of the Mau Forest could not justify the lack of recognition of the Indigenous status of the Ogiek, nor the denial of the rights associated with that status. It also confirmed that the Ogiek could not be held responsible for the depletion of the Mau Forest, and that preservation of the ecosystem could not justify their eviction from or the denial of access to their land. Although Kenyan institutions have still failed to remedy Ogiek rights, the Ogiek have identified a pathway for the Kenyan Government to follow to restitute Ogiek land, following principles of conservation and symbolizing the central role that Indigenous forest dwellers can and should play in forest management. They sought a further ruling from the Court to clarify the steps the Government should take. In June 2022, the Court issued a judgment ordering the Government to grant the Ogiek collective title of their lands through a process of delimitation and demarcation. In the meantime, the Ogiek have established community forest scouts in East Mau to replant native trees and protect the forest from illegal logging. In addition, they have developed an Ogiek community Bio-Cultural Protocol. Here we examine the feasibility of restituting Ogiek land both legally and practically. We conclude with some general comments related to global conservation policy and practice on the restitution of lands and support for Indigenous conservation practices, where protected areas have caused displacement and rights abuses of Indigenous peoples.
Toward Climate Justice: Resilience and Adaptation Strategies for Vulnerable Developing Countries

Toward Climate Justice: Resilience and Adaptation Strategies for Vulnerable Developing Countries  Surender Kumar¹, Maureen O'Sullivan², Abhnash K Bains³, Vidya Padmakumar⁴, Silverio Allocca⁵, Gelito Inacio Franco Sululu⁶, Cristiano Carocci⁷, Anu Gauba⁸, Clare Walker⁹, & Progga Parmita Chowdhury¹⁰  ¹ ALA Fellow, Melbourne University, Australia / India, ² Fellow, Oxford Centre for Animal Ethics, University of Galway, Ireland, ³ Ishkama Global Change CIC, London, United Kingdom, ⁴ EcoDiversity Lab, New Hazelton, British Columbia, Canada, ⁵ Researcher & Analyst, Italy, ⁶ National Representative, Commonwealth Youth Climate Change, Mozambique, Africa, ⁷ Political and Cultural Analyst, Italy, ⁸ Principal & Professor, Department of Nursing, GD Goenka University, Gurgaon, Haryana, India, ⁹ Clare Walker Consulting Ltd, England, ¹⁰ Independent Researcher, National University of Bangladesh  AbstractClimate change disproportionately affects developing countries due to their limited adaptive capacity, socio-economic constraints, and increased exposure to climate-related hazards. This paper investigates resilience and adaptation strategies through the lens of climate justice, highlighting the importance of equitable and inclusive approaches. Using a secondary research methodology, it synthesizes theoretical frameworks and empirical data from peer-reviewed literature, international reports, and climate finance databases. A quantitative analysis evaluates adaptation investments and their impact on reducing vulnerability, supported by numerical calculations and country-specific case studies. The study concludes with actionable policy recommendations aimed at achieving both sustainability and justice.