ethicalabs/Research-Intent-Collab huggingface dataset:
Magistrates marching in the streets: making and debating judicial independence and the rule of law in Benin

AbstractSince 2012, judges and prosecutors in Benin have repeatedly protested against political interference and demanded compliance with their statutorily guaranteed independence. In 2014 and 2017, magistrates demonstrated in their judicial robes in the streets, protesting against the government's bill to deprive them of their right to strike and other freedoms. Benin has been described as a ‘success story of democracy’ (Stroh and Never, 2006, p. 1) and even as a ‘model democracy’ (cf. Kohnert, 1996, p. 78; Magnusson, 2001, p. 211; Bierschenk, 2009) since its peaceful transition to democratic conditions and its participation in a national conference in 1990/91. So why were magistrates in Benin demonstrating in the streets for the first time in the history of their profession? Based on fieldwork in Benin in 2009 and 2015 and archival research in 2017 in France, my paper analyses the change in the style of interactions between parts of the executive and parts of the judiciary in the history of the profession – a change from political negotiation to confrontation. Through their strikes and industrial action, magistrates fought for judicial independence; yet, at the same time they constructed legality and strengthened democracy because their actions emphasised the rule of law. My paper also considers the specifics of their strikes in the context of other striking civil servants. When magistrates, as bureaucrats, become politically active, it marks a transformation in their self-conception, as they are usually reserved and withdraw themselves from political and public spheres.
AI auditing and impact assessment: according to the UK information commissioner’s office

AbstractAs the use of data and artificial intelligence systems becomes crucial to core services and business, it increasingly demands a multi-stakeholder and complex governance approach. The Information Commissioner's Office’s ‘Guidance on the AI auditing framework: Draft guidance for consultation’ is a move forward in AI governance. The aim of this initiative is toward producing guidance that encompasses both technical (e.g. system impact assessments) and non-engineering (e.g. human oversight) components to governance and represents a significant milestone in the movement towards standardising AI governance. This paper will summarise and critically evaluate the ICO effort and try to anticipate future debates and present some general recommendations.
The lncRNA Signatures of Genome Instability to Predict Survival in Patients with Renal Cancer

Long noncoding RNAs (lncRNAs) exert an increasingly important effect on genome instability and the prognosis of cancer patients. The present research established a computational framework originating from the mutation assumption combining lncRNA expression profile and somatic mutation profile in the genome of renal cancer to assess the effect of lncRNAs on the gene instability of renal cancer. A total of 45 differentially expressed lncRNAs were evaluated to be genome-instability-associated from the high and low cumulative somatic mutations groups. Then we established a prognosis model based on three genome-instability-associated lncRNAs (AC156455.1, AC016405.3, and LINC01234)-GlncScore. The GlncScore was then verified in testing cohort and the total TCGA renal cancer cohort. The GlncScore was evaluated to have an accurate prediction for the survival of patients. Furthermore, GlncScore was associated with somatic mutation patterns, indicating its capacity of reflecting genome instability in renal cancer. In conclusion, this study evaluated the effect of lncRNAs on genome instability of renal cancer and provided new hidden cancer biomarkers related to genome instability in renal cancer.
Integration of Islamic Values and Psychology as a Strategy for Adolescent Mental Health Recovery in The Context of Social Media

This article aims to analyze a holistic strategy based on the integration of psychological approaches and Islamic values in addressing the negative impacts of social media on adolescent mental health. The intended strategy focuses on strengthening mental resilience through Islamic mindfulness combined with spiritual values such as tawakkal (trust in God), patience, gratitude, and dhikrullah (remembrance of God) as forms of spiritual coping. This study employed a qualitative, case-study approach with adolescents in Tlogoretno Village. The research subjects consisted of adolescents aged 15–18 who experienced anxiety, stress, depression, sleep disturbances, and tendencies toward social isolation due to social media use. Data were collected through in-depth interviews, observations, and documentation, and analyzed using data reduction, display, and conclusion drawing. The findings indicate that Islamic mindfulness–based strategies effectively mitigate the negative impacts of social media, with greater effectiveness when combined with the practice of Islamic values. This study recommends a holistic intervention model comprising Islamic-based digital literacy, Islamic mindfulness practices, and strengthening supportive religious communities. These findings affirm that integrating psychology and Islamic values can strategically foster adolescent mental resilience in the digital era.
Whistleblowing in Audit Firms: Do Explicit Protections from Retaliation Activate Implicit Threats of Reprisal?

ABSTRACT                Despite the increasing number of statutory protections now provided to whistleblowers, fear of reprisal remains a primary reason why individuals fail to report misconduct. In order to alleviate such fears and encourage reporting, hotline policies often describe explicit whistleblower protections from specific types of retaliation (e.g., harassment, threats or intimidation, loss of job, etc.). However, we posit that such vivid descriptions may actually achieve the opposite of their intended effect by increasing the salience of retaliatory threats, thereby discouraging whistleblower reporting. We conduct an experiment and find evidence that when explicit protections are added to an audit firm's whistleblower hotline policy, auditors assess the risk of reporting as higher and, as a result, are less likely to indicate that the misconduct will be reported through the hotline. To our knowledge, our study is the first to demonstrate that offering explicit protections to whistleblowers can have these unintended and counter-intuitive consequences.
Cell types and ontologies of the Human Cell Atlas

Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body, while at the same time raising the need to formalise this new knowledge. Here, we review current cell ontology efforts to harmonise and integrate different sources of annotations of cell types and states. We illustrate with examples how a unified ontology can consolidate and advance our understanding of cell types across scientific communities and biological domains.
Code Building Genetic Programming

In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genetic Programming, can produce programs that solve problems typically assigned in introductory academic settings. These problems focus on a narrow, predetermined set of simple data structures, basic control flow patterns, and primitive, non-overlapping data types (without, for example, inheritance or composite types). Few, if any, genetic programming methods for program synthesis have convincingly demonstrated the capability of synthesizing programs that use arbitrary data types, data structures, and specifications that are drawn from existing codebases. In this paper, we introduce Code Building Genetic Programming (CBGP) as a framework within which this can be done, by leveraging programming language features such as reflection and first-class specifications. CBGP produces a computational graph that can be executed or translated into source code of a host language. To demonstrate the novel capabilities of CBGP, we present results on new benchmarks that use non-primitive, polymorphic data types as well as some standard program synthesis benchmarks.
Thematic reviews of patient safety incidents as a tool for systems thinking: a quality improvement report

Ensuring organisations learn from patient safety incidents is a key aim for healthcare organisations. The role that human factors and systems thinking can have to enable organisations learn from incidents is well acknowledged. A systems approach can help organisations focus less on individual fallibility and more on setting up resilient and safe systems. Investigation of incidents has previously been rooted in reductionist methodologies, for example, seeking to find the ‘root cause’ to individual incidents. While healthcare has embraced, in some contexts, the option for system-based methodologies—for example, SEIPS and Accimaps—these methodologies and frameworks still operate from a single incident perspective. It has long been acknowledged that healthcare organisations should focus on near misses and low harms with the same emphasis as incidents resulting in high harm. However, logistically, investigating all incidents in the same way is difficult. This paper puts forward an argument for themed reviews of patient safety incidents and provides an illustrative template for theming incidents using a human factors classification tool. This allows groups of incidents relating to the same portfolio, for example, medication errors, falls, pressure ulcer, diagnostic error, to be analysed at the same time and result in recommendations based on a larger sample size of incidents and based on a systems approach. This paper will present extracts of the themed review template trialled and argues that thematic reviews, in this context, allowed for a better understanding of the system of safety around the mismanagement of the deteriorating patient.
Exploring Self-supervised Contrastive Learning of Spatial Sound Event Representation

In this study, we present a simple multi-channel framework for contrastive learning (MC-SimCLR) to encode 'what' and 'where' of spatial audios. MC-SimCLR learns joint spectral and spatial representations from unlabeled spatial audios, thereby enhancing both event classification and sound localization in downstream tasks. At its core, we propose a multi-level data augmentation pipeline that augments different levels of audio features, including waveforms, Mel spectrograms, and generalized cross-correlation (GCC) features. In addition, we introduce simple yet effective channel-wise augmentation methods to randomly swap the order of the microphones and mask Mel and GCC channels. By using these augmentations, we find that linear layers on top of the learned representation significantly outperform supervised models in terms of both event classification accuracy and localization error. We also perform a comprehensive analysis of the effect of each augmentation method and a comparison of the fine-tuning performance using different amounts of labeled data.
Technology-facilitated child sexual abuse material (CSAM): New frontiers and challenges for justice systems

Generative artificial intelligence (AI) and robotics have resulted in new forms of child sexual abuse material (CSAM), raising urgent challenges for criminal justice, psychology, and computer science. Unlike conventional recorded (or live-streamed) CSAM, AI-generated or robotic representations typically do not depict an identifiable individual child victim. Nonetheless, AI-CSAM is not “victimless”, as it is likely trained on existing CSAM and may therefore embed traces of real abuse. This Briefing Paper discusses these issues to inform relevant professionals (e.g., law enforcement) about a rapidly-evolving and challenging child protection concern.
France, humanitarian intervention and the responsibility to protect

Since the end of the Cold War, the protection of human life has been a key priority for the international community. While France has played an important, though at times controversial, role in these humanitarian efforts - steering debates at the United Nations and standing in opposition to conflicts worldwide - its contribution has been overlooked and its long-standing commitment to human protection underestimated. Eglantine Staunton offers a compelling corrective to the prevailing assumptions about France's humanitarian record, examining its relationship to the dominant international principles and norms of human protection, such as humanitarian intervention in the 1990s and the Responsibility to Protect in the 2000s. Combining case studies of the interventions in Rwanda, Kosovo and Iraq, among others, and interviews with key actors including Gareth Evans and Bernard Kouchner, Staunton's innovative theoretical framework offers a valuable tool for understanding the interplay between domestic and international norms.
Batch Reinforcement Learning With a Nonparametric Off-Policy Policy Gradient

arXiv admin note: substantial text overlap with arXiv:2001.02435
Incentive scheme for federated learning based on zero-concentrated differential privacy

To solve problems of unfair client selection and inefficient model training in federated learning, a privacy-preserving federated learning framework was proposed based on the incentive mechanism named zCDP-FL. An incentive mechanism algorithm, SRAI, was designed to maximize system benefits by applying the second price and the reverse auction to the client's selection strategy. In addition, a dynamic allocation algorithm for the privacy budget was proposed based on the zero-concentrated differential privacy to realize the dynamic adjustment of noise scale during the training, which provided a stronger privacy guarantee under the strict privacy constraint. Theoretical analyses and simulation experiments demonstrate that zCDP-FL can effectively prevent privacy leakage and enhance 2.13%~3.62% model training efficiency.
A Survey of Self-Supervised and Few-Shot Object Detection

Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page at https://gabrielhuang.github.io/fsod-survey/
Nanoparticle-based delivery platforms for mRNA vaccine development

Conventional vaccines have saved millions of lives, and new vaccines have also been developed; however, an urgent need for an efficient vaccine against SARS-CoV-2 showed us that vaccine development technologies should be improved more to obtain prophylactic agents rapidly during pandemic diseases. One of the next-generation vaccine technologies is utilization of mRNA molecules encoding antigens. The mRNA vaccines offer many advantages compared to conventional and other subunit vaccines. For instance, mRNA vaccines are relatively safe since they do not cause disease and mRNA does not integrate into the genome. mRNA vaccines also provide diverse types of immune responses resulting in the activation of CD4+ and CD8+ T cells. However, utilization of mRNA molecules also has some drawbacks such as degradation by ubiquitous nucleases in vivo. Nanoparticles (NPs) are delivery platforms that carry the desired molecule, a drug or a vaccine agent, to the target cell such as antigen presenting cells in the case of vaccine development. NP platforms also protect mRNA molecules from the degradation by nucleases. Therefore, efficient mRNA vaccines can be obtained via utilization of NPs in the formulation. Although lipid-based NPs are widely preferred in vaccine development due to the nature of cell membrane, there are various types of other NPs used in vaccine formulations, such as virus-like particles (VLPs), polymers, polypeptides, dendrimers or gold NPs. Improvements in the NP delivery technologies will contribute to the development of mRNA vaccines with higher efficiency.
N-body simulations of gravitational dynamics

We describe the astrophysical and numerical basis of N-body simulations, both of collisional stellar systems (dense star clusters and galactic centres) and collisionless stellar dynamics (galaxies and large-scale structure). We explain and discuss the state-of-the-art algorithms used for these quite different regimes, attempt to give a fair critique, and point out possible directions of future improvement and development. We briefly touch upon the history of N-body simulations and their most important results.
How Can Accountants Enhance (or Save) Natural and Cultural Capital Valuation? Engaging Academics: A Collaboration with <scp>CPA</scp> Canada and the Canadian Commission for <scp>UNESCO</scp>*

ABSTRACTAccountants should engage more with natural and cultural capital accounting to make tools more accessible and to ensure critical information is provided to decision‐makers. While ecological economists have continued to innovate and design tools, corporate‐level accounting has seemingly lagged behind. We argue that the time is now for accounting researchers and practitioners to come together to build upon these tools to advance a sustainable agenda. We examine possibilities through the novel lens of a recent Chartered Professional Accountants of Canada and Canadian Commission for United Nations Educational, Scientific and Cultural Organization (UNESCO) report that explores tools to measure natural and cultural capital at UNESCO designated sites in Canada. From here, we pose a variety of questions, critiques, and pathways for researchers and practitioners alike.
Radio afterglows and host galaxies of gamma-ray bursts

Considering the contribution of the emission from the host galaxies of gamma-ray bursts (GRBs) to the radio afterglows, we investigate the effect of host galaxies on observations statistically. For the three types of events, e.g. low-luminosity, standard and high-luminosity GRBs, it is found that a tight correlation exists between the ratio of the radio flux (RRF) of host galaxy to the total radio peak emission and the observational frequency. Especially, toward lower frequencies, the contribution from the host increases significantly. The correlation can be used to get a useful estimate for the radio brightness of those host galaxies which only have very limited radio afterglow data. Using this prediction, we re-considered the theoretical radio afterglow light curves for four kinds of events, i.e. high-luminosity, low-luminosity, standard and failed GRBs, taking into account the contribution from the host galaxies and aiming at exploring the detectability of these events by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Lying at a typical redshift of $z=1$, most of the events can be detected by FAST easily. For the less fierce low-luminosity GRBs, their radio afterglows are not strong enough to exceed the sensitivity limit of FAST at such distances. However, since a large number of low luminosity bursts actually happen very near to us, it is expected that FAST will still be able to detect many of them.
Technology-Enhanced Multimodal Learning Analytics in Higher Education: A Systematic Literature Review

Multimodal learning analytics (MMLA) is an emerging field of learning analytics and promises a more comprehensive analysis of the learning process thanks to advances in technological devices and data science. The purpose of this study was to explore technology-enhanced multimodal learning analytics in higher education systematically. A systematic literature review was performed using the PRISMA guidelines, and 45 studies published between January 2012 and June 2024 were determined. The findings demonstrated that China, the USA, Australia, and Chile were the leading contributors to MMLA research, with a notable surge in publications in 2021. Audio recorders, cameras, webcams, eye trackers, and wristbands were the most used devices. Most studies were conducted in experiment rooms or laboratories, though studies in authentic classroom settings have been growing. Data were primarily collected during activities such as programming, simulation exercises, presentations, discussions, writing, watching videos, reading, or exams, as well as throughout the entire instructional process, predominantly in computer science, health, and engineering courses. The studies were mainly predictive or descriptive whereas quite a few studies were prescriptive. Frequently tracked data types included audio, gaze, log, facial expression, physiological, and behavioral data. Traditional machine learning and basic statistics were the commonly used analytical methods whilst advanced statistics and deep learning were relatively less utilized. Test performance, engagement, emotional state, debugging performance, and learning experience were the popular target variables. The studies also pointed out several implications and future directions, with a significant portion highlighting the development of interventions, frameworks, or adaptive systems using MMLA.
Communicative Framework Development for Construction Risk Governance: An Analysis of Risk and Trust Perception for Organizational Sustainability

In the construction industry, risk governance stems from the standardization of safety policies and procedures. Safety governance is typically communicated and enforced through a top-down, command-and-control approach. Procedures and policies are created at the top level and cascaded down to the work. Individual discretion in the workplace permits a choice for worker adherence to safety procedures. Through a literature review, this study identified risk perception as a significant variable in regulatory compliance and fidelity. Individual hazard assessments and risk behaviors are often based on subjective cognitive risk perceptions rather than organizational governance policy. In the current governance system, there is a misalignment of risk perceptions and failure in trust between the workforce and the regulatory process. This study examines the impact of risk perceptions on safety compliance and promotes a more participative risk governance process for sustainable construction practices. A participative framework model is proposed to help align worker risk perceptions with organizational governance through policy creation, policy compliance and stakeholder participation and feedback. Utilizing proper discourse and cyclical communication, the proposed framework aims to increase safety fidelity, compliance, and trust in the construction industry through a more inclusive and collaborative governance strategy.
Self-Supervised Representation Learning for CAD Models

In product development, design decisions and processing steps are closely linked to the geometric shape of the product. Self-supervised representation learning methods make it possible to learn compact, versatile representations from a wide range of unlabeled data. These representations encode basic structures and features of the input data and can be used to solve application tasks in a data-efficient way. A key problem is the formulation of suitable self-supervised tasks that promote the learning of meaningful representations. While existing research in the field of computer vision primarily deals with pure point cloud data, the focus in this work is specifically on the use of CAD data.