ethicalabs/Research-Intent-Collab huggingface dataset:
Towards a high-accuracy framework for quranic reciter recognition using deep learning and a large-scale benchmark dataset

Speaker recognition aims to identify who is speaking from their voice and is widely used in security, personalization, and archival search. A related, culturally significant task is recognizing Qur’ān reciters from their recitations. The Quran is the central religious text of Islam and is recited with codified pronunciation and melodic rules (tajwīd and maqām). Distinguishing reciters can support digital archiving, educational feedback, and retrieval of stylistically similar recitations. We present a controlled comparison of deep learning approaches for Qur’ān reciter recognition, contrasting feature-based pipelines with end-to-end waveform models under a unified protocol. Using ṣūrah Al-Tawbah recitations from 12 reciters (18,540 clips; fixed 2 s segments), an X-Vector architecture with Mel-Frequency Cepstral Coefficients (MFCCs) attains perfect test performance (accuracy/precision/recall/F1 =100%). Convolutional Neural Network (CNN) and Bidirectional LSTM (BLSTM) baselines achieve near-optimal results (99.96% accuracy and F1), while an end-to-end X-Vector trained on raw waveforms reaches 98.77% accuracy (F1 = 0.9877). These findings indicate that explicit spectral features remain advantageous for short segments requiring fine acoustic discrimination, although end-to-end learning is competitive and simplifies preprocessing. We release the curated dataset with standardized splits and training scripts to enable reproducible benchmarking. Overall, feature-informed X-Vectors constitute a strong reference for short-segment reciter identification, and our results motivate hybrid/self-supervised front ends, tajwīd-aware analysis, and real-time, on-device deployment.
Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science

Abstract                   The last several years have seen an explosion of interest in wearable computing, personal tracking devices, and the so-called quantified self (QS) movement. Quantified self involves ordinary people recording and analyzing numerous aspects of their lives to understand and improve themselves. This is now a mainstream phenomenon, attracting a great deal of attention, participation, and funding. As more people are attracted to the movement, companies are offering various new platforms (hardware and software) that allow ever more aspects of daily life to be tracked. Nearly every aspect of the QS ecosystem is advancing rapidly, except for analytic capabilities, which remain surprisingly primitive. With increasing numbers of qualified self participants collecting ever greater amounts and types of data, many people literally have more data than they know what to do with. This article reviews the opportunities and challenges posed by the QS movement. Data science provides well-tested techniques for knowledge discovery. But making these useful for the QS domain poses unique challenges that derive from the characteristics of the data collected as well as the specific types of actionable insights that people want from the data. Using a small sample of QS time series data containing information about personal health we provide a formulation of the QS problem that connects data to the decisions of interest to the user.
Judicial independence as a necessary component of the rule of law: preliminary insights and economic consequences

Law and the State provides a political economy analysis of the legal functioning of a democratic state, illustrating how it builds on informational and legal constraints. It explains, in an organised and thematic fashion, how competitive information enhances democracy while strategic information endangers it, and discusses how legal constraints stress the dilemma of independence versus discretion for judges as well as the elusive role of administrators and experts.
Development of a sensory-neural network for medical diagnosing

Performance of a sensory-neural network developed for diagnosing of diseases is described. Information about patient's condition is provided by answers to the questionnaire. Questions correspond to sensors generating signals when patients acknowledge symptoms. These signals excite neurons in which characteristics of the diseases are represented by synaptic weights associated with indicators of symptoms. The disease corresponding to the most excited neuron is proposed as the result of diagnosing. Its reliability is estimated by the likelihood defined by the ratio of excitation of the most excited neuron and the complete neural network.
Boosting graph neural networks with knowledge for personalized medicine

Tese de mestrado, Engenharia Informática, 2025, Universidade de Lisboa, Faculdade de Ciências
Building Financial Peace: A Conflict Resolution Framework for Money Arguments

This paper presents a well-known and highly utilized conflict resolution framework from the mediation profession and demonstrates how to apply this framework to money arguments. While conflict resolution skills have been identified as important to communication within the financial planning context, an integrated conflict resolution framework has yet to be recognized and understood within the financial planning literature. This paper aims to fill this gap. Ultimately, both mental health professionals and financial planners can benefit from an interdisciplinary approach to resolving money arguments by combining training in personal financial strategies and conflict resolution principles.
Self-Supervised Learning of Audio Representations using Angular Contrastive Loss

In Self-Supervised Learning (SSL), various pretext tasks are designed for learning feature representations through contrastive loss. However, previous studies have shown that this loss is less tolerant to semantically similar samples due to the inherent defect of instance discrimination objectives, which may harm the quality of learned feature embeddings used in downstream tasks. To improve the discriminative ability of feature embeddings in SSL, we propose a new loss function called Angular Contrastive Loss (ACL), a linear combination of angular margin and contrastive loss. ACL improves contrastive learning by explicitly adding an angular margin between positive and negative augmented pairs in SSL. Experimental results show that using ACL for both supervised and unsupervised learning significantly improves performance. We validated our new loss function using the FSDnoisy18k dataset, where we achieved 73.6% and 77.1% accuracy in sound event classification using supervised and self-supervised learning, respectively.
Feasibility of smartphone-based digital phenotyping to measure visual function and mental health outcomes in patients with inherited retinal diseases

Abstract                                        This study evaluated the feasibility of collecting passive and active digital phenotyping data using the                     OverSight                     iOS application in individuals with inherited retinal diseases (IRDs), and explored associations between digital behavioural markers, visual function and mental health. Participants with IRDs were recruited from Moorfields Eye Hospital (UK) and followed for 12 months.                     OverSight                     passively captures mobility data through HealthKit and typing-derived metrics through SensorKit. Participants completed patient-reported outcome measures (EQ-5D, NEI-VFQ-25, HADS and MRDQ) within the app. Passive data included step count, walking speed, typing speed, total words typed, autocorrections, and sentiment word categories (anxiety, down and health-related terms). Feasibility indices included enrolment, retention and completeness of passive datastreams. Twenty-five participants were enrolled, and 92% were retained at 12 months. Seventeen participants met the validity threshold for HealthKit data, and 16 also met SensorKit thresholds. Median daily step count was 6087, walking speed 1.18 m/s and typing speed 2.19 characters/s. Age was negatively correlated with typing speed and anxiety-related word use, and photopic peripheral visual difficulty was negatively correlated with anxiety- and down-related word use. Digital phenotyping using                     OverSight                     was feasible over 12 months. Exploratory analyses suggest mobility, typing behaviour and sentiment markers may represent useful adjunctive indicators of functional vision and psychological status. These data may support remote monitoring and earlier identification of clinically relevant changes, informing more timely personalised patient management in IRDs.
Patient reported outcome measures in childbirth and postpartum maternal quality of life: a protocol for systematic review of measurement properties. [version 3; peer review: 1 approved, 2 approved with reservations]

Background Patient centred healthcare is the corner stone to many healthcare strategies. Patient specific health needs should be at the fore of healthcare improvements and quality measurements. Patient reported outcome measures (PROM) that support real world clinical effectiveness assessments are increasingly being used to highlight domains where there is the greatest scope for change. Objectives This systematic review aims to identify and evaluate existing patient reported assessment measures/tool(s) that can be used in developing a PROM for postpartum women. We will assess and evaluate their measurement properties in a transparent and structured way in accordance with the COSMIN guidelines. Methods Methodological guidelines for systematic reviews of PROMs have been developed by the COSMIN initiative and will be followed for this systematic review. A systematic literature review will be performed using PubMed, CINAHL and EMBASE from inception to the present day. Two reviewers independently will judge eligibility, conduct data extraction and assess the methodological quality of each study as per COSMIN guidelines. Inclusion criteria: studies should concern PROM with an aim to evaluate measurement properties in the development or the evaluation of a PROM of interest. Included PROM will focus upon postpartum women assessing morbidity and quality of care. All peer reviewed studies with an assessment tool designed for patient completion will be considered. Exclusion criteria; abstract, letters and non-peer reviewed publications. Studies will be graded on measurement properties and quality of evidence as laid out by COSMIN. All studies and characteristics eligible for inclusion will be summarised and a recommendation to the most suitable measurement tool(s) will be given. Discussion We will provide a comprehensive description of all available patient reported assessment tools available for childbirth and postpartum quality of life and recommend based on COSMIN guidelines the most suitable instrument(s) available for use.
THE LEADERSHIP PHENOMENON AS A MECHANISM FOR ORGANIZATIONAL BEHAVIOR MANAGEMENT

The article presents the concepts of analysis of the phenomenon of leadership and leadership competencies, which are reflected in modern scientific foreign literature. The object of the research was a model of leadership and leadership competencies, based on research in the 6 most developed countries, to identify effective and most popular leadership qualities. 25 leadership competencies were selected and the number of references to leadership competencies over the past ten years was investigated. Relevant leadership theory literature has been analyzed using Google Scholar over the past ten years. The results of the study showed that there is great interest in the selected 25 leadership competencies that can be applied in any field of activity.There are many different meanings of leadership. Researchers usually define leadership according to their individual point of view and the aspect of the phenomenon that interests them most. Today it is recommended to try to study the leadership competencies identified in important areas in order to develop a model that will be scientifically substantiated and used in training programs. The proposed scientific hypothesis is based on the fact that 25 randomly selected leadership competencies are reliable, universal and can be used in any field of activity. Further research should be aimed at reproducing, expanding and confirming real results.The purpose of the article is to study the phenomenon of leadership as a mechanism for managing organizational behavior, which was achieved by solving the following tasks: generalization of leadership competencies, confirmation of the use of leadership competencies, clarification of the leadership qualities of an effective leader, confirmed interest in randomly selected leadership qualities. These 25 competencies can be applied in any field of activity.The goal of researching the phenomenon of leadership as a mechanism for managing organizational behavior by solving certain problems was achieved. Further research in the field of the phenomenon of leadership should be aimed at replicating, expanding and confirming real results. Meanwhile, the presented model can be used for training and further study of leadership competencies in any field of activity.
Development of stable qubits and error correction in quantum computer architecture for superconducting quantum processors

A comprehensive mathematical model formulation is presented, encompassing gate fidelity optimization, coherence time extension, stabilizer code evolution, and surface code implementation. The research demonstrates significant advancements in qubit stability, with a 7% increase in gate fidelity and a remarkable 50% extension in coherence time achieved through optimized gate operations and material improvements. Quantum error correction techniques, guided by the Lindblad master equation and the surface code, result in a 25% reduction in error rates, contributing to the overall stability of the quantum processor. The outcomes not only bring practical quantum computing closer to realization but also provide a foundation for future innovations. The research identifies avenues for continued optimization, including advanced gate designs, exploration of emerging qubit technologies, and the development of sophisticated error correction codes. Further interdisciplinary collaborations and investigations into scalable quantum architectures, materials science, and cryogenic engineering are essential for overcoming remaining challenges. The insights gained contribute to the advancement of fault-tolerant quantum computing systems, offering transformative capabilities for computation and technology.
A Hybrid Bayesian Deep Learning Model for Remaining Useful Life Prognostics and Uncertainty Quantification

Remaining useful life (RUL) prognostics is critical to maintenance decision making and reliability assessment. A significant amount of neural networks research has been reported to develop prognostics models that can provide high prediction accuracy. These models use advanced neural networks to improve prediction performance and provide sole point estimation for RUL. However, accurate uncertainty quantification of the RUL is essential to understand the uncertainty of the degradation process and perform reliable risk analysis and maintenance decisions. This paper proposes a new hybrid Bayesian deep learning (HBDL)-based prognostics approach. It uses long short term memory autoencoder (LSTM-AE) to extract features that include degradation information, and then uses the Bayesian neural network (BNN) to model and predict the equipment degeneration process. It learns network weights through variational inference (VI) and provides RUL prognostics results while obtaining interval estimation. Finally, a general aircraft engine data set is used to verify the proposed model. The experimental results show that this method can achieve satisfying prediction accuracy and uncertainty quantification capability.
Essentiality, conservation, evolutionary pressure and codon bias in bacterial genes

Essential genes constitute the core of genes which cannot be mutated too much nor lost along the evolutionary history of a species. Natural selection is expected to be stricter on essential genes and on conserved (highly shared) genes, than on genes that are either nonessential or peculiar to a single or a few species. In order to further assess this expectation, we study here how essentiality of a gene is connected with its degree of conservation among several unrelated bacterial species, each one characterised by its own codon usage bias. Confirming previous results on E. coli, we show the existence of a universal exponential relation between gene essentiality and conservation in bacteria. Moreover we show that, within each bacterial genome, there are at least two groups of functionally distinct genes, characterised by different levels of conservation and codon bias: i) a core of essential genes, mainly related to cellular information processing; ii) a set of less conserved nonessential genes with prevalent functions related to metabolism. In particular, the genes in the first group are more retained among species, are subject to a stronger purifying conservative selection and display a more limited repertoire of synonymous codons. The core of essential genes is close to the minimal bacterial genome, which is in the focus of recent studies in synthetic biology, though we confirm that orthologs of genes that are essential in one species are not necessarily essential in other species. We also list a set of highly shared genes which, reasonably, could constitute a reservoir of targets for new anti-microbial drugs.
Risk Assessment of Urban Low-Temperature Vulnerability: Climate Resilience and Strategic Adaptations

In recent years, the increasing frequency and intensity of climate-related disasters have underscored the urgent need for resilient urban development. In cold-region cities, low temperatures pose a distinct and underexplored threat, with serious implications for human well-being, infrastructure performance, and ecological stability. Despite growing attention to climate resilience, existing urban risk assessments have largely focused on heatwaves and flooding, leaving a notable gap in research on cold-weather vulnerability. To address this gap, this study develops a fine-scale cold-climate vulnerability assessment framework grounded in the widely recognized “Exposure–Sensitivity–Adaptive Capacity” (ESA) model. Using subdistricts as the basic units of analysis, we integrate multi-source spatial data—including demographics, built environment, services, and ecological indicators—to construct a comprehensive evaluation system tailored to low-temperature conditions. The model is applied to the central urban area of Harbin, China, a representative cold-region city. The results reveal distinct spatial disparities in vulnerability: older urban districts exhibit higher vulnerability due to high population density and inadequate public services, while newly developed areas show relatively greater adaptive capacity. Further analysis identifies key drivers of vulnerability in different zones. Based on these insights, the study proposes differentiated, subdistrict-level planning strategies aimed at reducing exposure, mitigating sensitivity, and enhancing adaptive capacity. By extending the ESA model to cold-climate scenarios and operationalizing it at the subdistrict scale, this research contributes both methodologically and practically to the field of urban climate resilience. The findings offer actionable strategies for policymakers and provide a replicable framework applicable to other cold-region cities facing similar challenges.
WIPO Mediation, Arbitration, Expedited Arbitration and Expert Determination Rules and Clauses - Alternative Dispute Resolution

This publication contains the WIPO Mediation, Arbitration and Expedited Arbitration Rules and Clauses, as well as the related Schedule of Fees and Costs.
Vulnerability, adaptation and resilience to climate change of upland farming communities in Davao Oriental, Philippines

The impacts of climate change are inevitable. Farming communities will be vulnerable to these climate impacts, but the proper assessment of site-specific community resilience is the key to improving and strengthening necessary resilience indicators needed to address the ever-increasing effects of climate change, such as drought and flood/landslide. This study highlights the importance of assessing the right resilience indicators to achieve long-lasting sustainability in upland farming communities, particularly in Davao Oriental, Philippines. The study aims to assess climate change effects, determine site-specific resilience indicators, and provide a framework to enhance resilience among upland farmers. The study was conducted in six upland farming communities in Mati City. The paper was based on qualitative interviews using focus groups and participatory approaches to understand and identify climate resilience among participants from various rural communities. Seventy-three farmers (33 male/40 female) participated in the half-day FGD workshops. Results of the workshop showed that each upland community was able to experience two to three climate-related hazards (i.e., floods, drought, and rain-induced landslide). Moreover, communities with more diverse sources of crops, livestock, and income showed higher resilience to climate change impacts than upland farming communities with lesser crop and livestock diversity. The results of this paper could be used as baseline data in drafting site-specific policies to address and mitigate the adverse effects of climate change at the local and national levels.
Is Patient Safety Synonymous With Quality Nursing Care? Should It Be? A Brief Discourse

Over the course of the past several years, patient safety and the initiatives undertaken to assure it have become highly visible issues in health care delivery. Patient safety is now the focus of national regulatory agencies as well as local health care institutions. At both the national and local levels, significant resources are directed at achieving patient safety goals. As nurses strive to implement and comply with the regulatory directives regarding safe care, has the focus of nursing care shifted from quality nursing care, built on a foundation of caring, of recognizing and attending to a patient's individualized needs, to a base that equates "safe care" with "quality care?" Have safety and quality become synonymous? This shift may indeed be occurring; however, quality nursing care--no matter how safe it is--must include caring as a core value and highlight the integral human relationship between the nurse and the patient.
Convergence analysis of Strang splitting for Vlasov-type equations

A rigorous convergence analysis of the Strang splitting algorithm for Vlasov-type equations in the setting of abstract evolution equations is provided. It is shown that under suitable assumptions the convergence is of second order in the time step τ. As an example, it is verified that the Vlasov-Poisson equation in 1+1 dimensions fits into the framework of this analysis. Also, numerical experiments for the latter case are presented.