ethicalabs/Research-Intent-Collab huggingface dataset:
Temporal Graph Neural Networks for Modeling Student Knowledge Evolution and Predicting Learning Trajectories

Accurately modeling student knowledge evolution is a central challenge in personalized learning and adaptive educational systems. Traditional sequential or static approaches often fail to capture both the temporal dynamics of learning and the relational structure between students and concepts. This study introduces a Temporal Graph Neural Network (TGNN) framework for modeling student knowledge acquisition and predicting learning trajectories using fine-grained interaction data from the ASSISTments_skill dataset. The TGNN represents students and skills as nodes in a dynamic bipartite graph, with temporal edges encoding correctness, attempts, hints, and interaction timestamps. Experiments demonstrate that TGNN significantly outperforms state-of-the-art baselines, including Deep Knowledge Tracing (DKT), Self-Attentive Knowledge Tracing (SAKT), and static graph convolutional networks, achieving an Area Under the Curve (AUC) of 0.892, Accuracy of 0.846, F1 score of 0.842, and a Mean Absolute Error (MAE) of 0.078 for trajectory prediction. Ablation studies reveal the critical role of temporal encoding, edge features, and graph connectivity in accurately modeling learning dynamics. Concept-level analysis indicates high prediction accuracy across both high-frequency and low-frequency skills, while temporal attention mechanisms enable interpretable insights into the influence of prior interactions on future performance. These results highlight the effectiveness of integrating temporal dynamics, graph-based relational modeling, and pedagogically meaningful features in predicting student learning outcomes. These results demonstrate the potential of temporal graph-based modeling for capturing student–skill relationships and learning dynamics in educational interaction data. Rather than introducing a fundamentally new graph architecture, this study systematically adapts the Temporal Graph Network (TGN) framework to educational data and evaluates its effectiveness for modeling knowledge evolution and forecasting student learning trajectories. The findings provide practical insights for applying temporal graph learning methods to personalized learning, adaptive intervention design, and real-time performance forecasting.
Structural relationship among Ethical Leadership of Sports Center Manager, Organizational Trust, Organizational Flow and Organizational Citizenship Behavior

This study has a main purpose that closely examines the structural relationship among ethical leadership of sports center manager, organizational trust, organizational flow and organizational citizenship behavior. In order to achieve it, the sample from employee of sports centers were accumulated by using convenience sampling that chose sports center in Seoul. The samples were 300 people. and we excluded unsuitable sheet like that were decided as untruthful one and an omission and unclear. So the final samples were 286 people. On the basis of that, The statistical techniques, spss 24.0 and amos 24.0 program have been used for the analysis as per research purpose. The statistical analysis used in the research for a data analysis includes frequency analysis, confirmatory factor analysis, reliability analysis, correlation analysis, and structure equation model analysis. The research has drawn the following conclusions, based on the above mentioned research method and its procedures:   First, The ethical leadership influences organizational trust.   Second, The ethical leadership influences organizational flow.   Third, The organizational trust influences organizational citizenship behavior.   Fourth, The organizational flow influences organizational citizenship behavior.
Feature extraction from MEG data using self-supervised learning : Investigating contrastive representation learning methods to f ind informative representations

Modern day society is vastly complex, with information and data constantly being posted, shared, and collected everywhere. There is often an abundance of massive amounts of unlabeled data that can not be leveraged in a supervised machine learning context. Thus, there exists an incentive to research and develop machine learning methods which can learn without labels. Selfsupervised learning (SSL) is a newly emerged machine learning paradigm that aims to learn representations that can later be used in domain specific downstream tasks. In this degree project three SSL models based on the Simple Framework for Contrastive Learning of Visual Representations (SimCLR) are evaluated. Each model aims to learn sleep deprivation related representations on magnetoencephalography (MEG) measurements. MEG is a non-invasive neuroimaging technique that is used on humans to investigate neuronal activity. The data was acquired through a collaboration with Karolinska Institutet and Stockholm University, where the SLEMEG project was conducted to study the neurophysiological response to partial sleep deprivation. The features extracted by the SSL-models are analyzed both qualitatively and quantitatively, and also used to perform classification and regression tasks on subject labels. The results show that the evaluated Signal- and Recording SimCLR models can learn sleep deprivation related features, whilst simultaneously learning other co-occuring information also. Furthermore, the results indicate that the learned representations are informative and can be utilized for multiple downstream tasks. However, it is noted that what has been learned is mostly related to subject-specific individual variance, which leads to poor generalization performance on classification and regression downstream tasks. Thus, it is believed that the models would perform better with access to more MEG data, and that source localized MEG data could remove part of the individual variance that is learned.
Minority Rights and Minority Protection mechanisms

Second semester University: University of Deusto, Bilbao.
Temporary Appointments and Judicial Independence: Theoretical Analysis and Empirical Findings from the Supreme Court of Israel

Judicial independence is regarded as an essential condition for the rule of law and separation of powers — two pillars of liberal democracy. Judicial independence ought to include components to secure independence on the individual level, as well as components to secure independence on the institutional level of courts. The most important objects of judicial independence are the other branches of government, but some degree of independence from the general public and from other judges is required as well. These features of independence can be achieved by rigid and entrenched arrangements regarding tenure, immunity from wage decrease, special procedures for appointment and promotion of judges, mechanisms for the allocation of cases to judges and the composition of the benches, and more.
Standards optimization and network lifetime maximization for wireless sensor networks in the Internet of things. (Optimisation de standards et maximisation du temps de vie d'un réseau de capteurs pour l'Internet des objets).

De nouveaux protocoles ont été standardisés afin d'intégrer les réseaux de capteurs sans fil (WSN) dans l'Internet. Parmi eux, RPL pour la couche routage et IEEE 802.15.4 pour la couche MAC. L'objectif de cette thèse est d'améliorer ces protocoles en prenant compte des contraintes énergétiques des dispositifs du WSN. Tout d'abord, nous avons conçu une nouvelle méthode de diffusion dans la norme IEEE 802.15.4, afin d'assurer une livraison fiable des paquets de contrôle des couches supérieures. Ensuite, nous avons fourni une évaluation exhaustive de RPL, en soulignant un problème d'instabilité qui génère une surcharge d'énergie importante. Compte tenu que la durée de vie des WSN est très limitée, nous avons aussi proposé une nouvelle métrique de routage qui identifie les goulets d'étranglement énergétiques afin de maximiser la durée de vie du réseau. Enfin, en couplant cette mesure avec une version multiparent de RPL, nous avons résolu le problème d'instabilité souligné précédemment. ; New protocols have been standardized in order to integrate Wireless Sensor Networks (WSN) in the Internet. Among them, the IEEE 802.15.4 MAC layer protocol, and RPL, the IPv6 Routing Protocol for Low-power and Lossy Networks. The goal of this thesis is to improve these protocols, considering the energy constraints of the devices that compose the WSN. First, we proposed a new MAC layer broadcast mechanism in IEEE 802.15.4, to ensure a reliable delivery of the control packets from the upper layers (especially from RPL). Then, we provided an exhaustive evaluation of RPL and highlighted an instability problem. This instability generates a large overhead, consuming a lot of energy. Since the lifetime of WSN is very limited, we proposed a new routing metric that identifies the energy bottlenecks and maximizes the lifetime of the network. Finally, by coupling this metric with a multipath version of RPL, we are able to solve the instability problem previously highlighted.
Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials

Neural-network-based machine learning interatomic potentials have emerged as powerful tools for predicting atomic energies and forces, enabling accurate and efficient simulations in atomistic modeling. A key limitation of traditional deep learning approaches, however, is their inability to provide reliable estimates of predictive uncertainty. Such uncertainty quantification is critical for assessing model reliability, especially in materials science, where often the model is applied on out-of-distribution data. Different strategies have been proposed to address this challenge, with deep ensembles and Bayesian neural networks being among the most widely used. In this work, we introduce an implementation of Bayesian neural networks with variational inference in the aenet-PyTorch framework. To evaluate their applicability to machine learning interatomic potentials, we systematically compare the performance of variational BNNs and deep ensembles on a dataset of 7,815 TiO$_{2}$ structures. The models are trained on both the full dataset and a subset to assess how variations in data representation influence predictive accuracy and uncertainty estimation. This analysis provides insights into the strengths and limitations of each approach, offering practical guidance for the development of uncertainty-aware machine learning interatomic potentials.
Preventing the dissemination of child sexual abuse material (CSAM) with surveillance technologies: The case of EU Regulation 2021/1232

Online services are increasingly being used to distribute child sexual abuse material. Recognising this problem and responding to public pressure, legislators are introducing new electronic surveillance measures – to be used by private service providers in the performance of public tasks. An example of such a measure is EU Regulation 2021/1232, which provides the legal framework for technology providers to monitor electronic correspondence in order to identify cases of child sexual abuse and subsequently report them to law enforcement authorities. Creating a legal framework for monitoring a large share of electronic communications causes such provisions to be part of a general norm instead of an exception – which is what interference with the secrecy of communications should be. This leads to questions about the necessity and proportionality of such a practice. This article examines the genesis and purpose of the introduction of the Regulation, as well as the key concerns regarding its compatibility with EU law. It also explores the fundamental dilemma of whether modern surveillance measures are the right and necessary tools to not only fight but also to prevent the most serious crimes.
The Impact of Social Media on Adolescents' Mental Health

This study examines the complex effects of social media on the mental health of adolescents in the digital age. Teenagers between the ages of 12 and 19 now live their lives heavily influenced by social media, which shapes their social interactions, identity formation, and mental health. Frequent use carries serious hazards, such as anxiety, depression, and cyberbullying, even though it offers chances for social interaction and assistance, especially for under-represented groups. According to empirical data, social comparison and body-image issues are exacerbated by algorithm-driven platforms like Instagram and TikTok, with adolescents who are insecurely attached being particularly susceptible to unfavourable comments. Unrealistic self-evaluations are fostered by excessive exposure to idealised content, which can lead to mental pain and, in severe cases, suicidal thoughts or self-harm. Psychological vulnerabilities are made worse by physical health problems, such as sleep disturbances brought on by extended screen time. The relationship between social media and teenage mental health is complicated overall, influenced by usage patterns, platform features, and individual differences, even when favourable outcomes—like peer support and decreased loneliness—are obvious. Generalisability is limited by methodological flaws in current research, such as the use of self-reported cross-sectional data and small, culturally limited groups. Nevertheless, findings highlight the urgent need for evidence-based interventions by parents, schools, and policymakers to mitigate risks and promote adolescents’ healthy development.
Biodiversity of canopy arthropods in Jarrah forest of south-west Western Australia: review of ecological theory and conservation management

A theory proposed in 1996 by Recher, Majer and Ganesh linking biodiversity of forest canopy arthropods to site productivity is analysed. Available evidence from Jarrah Eucalyptus marginata forest is inconsistent with this model. We instead propose that increased habitat variety and temperature and rainfall clines are the major environmental factors that determine canopy arthropod species richness. Biodiversity gradients for mammal, landbird and reptile species across south-west Western Australia appear to provide an appropriate model for forest insect faunas. These gradients predict that the most diverse canopy fauna should occur in the eastern Jarrah and Wandoo forests. Precautionary forest management policies and procedures currently in place to conserve the poorly collected and inadequately known arthropod fauna of tree crowns in Jarrah forest are summarized and discussed. In essence, these maximize habitat diversity at landscape scales. Major conservation threats are considered to be factors that reduce leaf area at large spatial (Phytophthora infection) and temporal scales (summer wildfire and defoliating insect outbreaks). Logging is not considered significant because it is constrained to small spatial scales (10 ha for the most extreme treatment) and long return times (2-3 decades).
Conjugative plasmids disseminating ctx-m-15 among enterobacteriaceae from human, animals and the environment in Mwanza Tanzania: a need to intensify one health approach

Globally, blaCTX-M-15 beta-lactamases are the most popular extended spectrum beta-lactamase alleles that are widely distributed due its mobilization by mobile genetic elements in several compartments. We aimed to determine the conjugation frequencies and replicon types associated with plasmids carrying blaCTX-M-15 gene from Extended Spectrum Beta-lactamase producing isolates in order to understand the dissemination of resistance genes in different compartments. A total of 51 archived isolates carrying blaCTX-M-15 beta-lactamases were used as donors in this study. Antibiotic susceptibility tests were performed as previously described for both donors and transconjugants. Conjugation experiment was performed by a modified protocol of the plate mating experiment, and plasmid replicon types were screened among donor and transconjugant isolates by multiplex Polymerase Chain Reaction in a set of three primer panels. Escherichia coli was recovered from majority of isolates. The conjugation efficiency of plasmids carrying blaCTX-M-15 was 88.2% (45/51) with conjugation frequencies in the order of 10‒1 to 10‒9 and a 100% transfer efficiency observed among E. coli of animal origin. Majority of donors (n = 21) and transconjugants (n = 14) plasmids were typed as either Inc FIA or Inc FIB. Resistance to non-beta-lactam antibiotics was transferrable in 34/45 (75.6%) of events. Ciprofloxacin, tetracycline and sulphamethoxazole-trimethoprim resistance was co-transferred in 29/34 (85.3%) such events. Gentamicin resistance was transferred in 17/34 (50%) of events. Majority of plasmids carrying blaCTX-M-15 were conjugatively transferred by IncF plasmids along with non-beta lactam resistance. There is a need for more research on plasmids to understand how plasmids, especially multi replicon plasmids interact and the effect of such interaction on conjugation. One Health approach is to be intensified to address antimicrobial resistance which is a public health threat
Reconstruction of Solar EUV Irradiance Using CaII K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification

Solar extreme ultraviolet (EUV) irradiance plays a crucial role in heating the Earth's ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time scales. Although significant effort has been spent studying short-term EUV variations from solar transient events, there is little work to explore the long-term evolution of the EUV flux over multiple solar cycles. Continuous EUV flux measurements have only been available since 1995, leaving significant gaps in earlier data. In this study, we propose a Bayesian deep learning model, named SEMNet, to fill the gaps. We validate our approach by applying SEMNet to construct SOHO/SEM EUV flux measurements in the period between 1998 and 2014 using CaII K images from the Precision Solar Photometric Telescope. We then extend SEMNet through transfer learning to reconstruct solar EUV irradiance in the period between 1950 and 1960 using CaII K images from the Kodaikanal Solar Observatory. Experimental results show that SEMNet provides reliable predictions along with uncertainty bounds, demonstrating the feasibility of CaII K images as a robust proxy for long-term EUV fluxes. These findings contribute to a better understanding of solar influences on Earth's climate over extended periods.
An appraisal of the Draft Framework for Reporting and Monitoring Execution of Judgments of the African Court on Human and Peoples’ Rights

Although the African Court on Human and Peoples’ Rights (African Court) delivers landmark judgments on human rights violations by states party to the Protocol to the African Charter on Human and Peoples’ Rights on the Establishment of an African Court (Court Protocol), the record of states’ compliance with its judgments is dire. Execution of judgments and decisions of the African Court has been one of the major challenges facing the African Court since its inception as the Court lacks a monitoring mechanism to compel states to execute its judgments. In response to this challenge, the Executive Council of the African Union (AU Executive Council) authorised the African Court to propose a mechanism for reporting of states’ non-compliance with its decisions. The African Court has come up with a Draft Framework for Reporting and Monitoring Execution of Judgments and other Decisions of the African Court (Draft Framework). The Draft Framework will be submitted to the Assembly through various organs of the AU including the Permanent Representatives Committee (PRC) and the Executive Council of the AU for adoption. The draft framework proposes the adoption of a hybrid model on monitoring state compliance with judgments of the African Court that combines both judicial and political mechanisms of enforcing state compliance. This article appraises the Draft Framework and argues that while the proposed model is a welcome step towards improving the enforcement of the Court’s decisions and, indeed, developing a culture of compliance within the African human rights system, it needs strengthening. The article recommends that the African Court should start laying the foundation for a strong enforcement regime through the localisation of the enforcement of its judgments in member states’ legal framework particularly through domestic courts.
Corporate social responsibility, sustainability, and business ethics

This chapter discusses business ethics, corporate social responsibility (CSR), and sustainability. Business ethics deals primarily with the principles that should be used to govern business conduct, how people should act, and the underlying philosophies behind ethical actions. CSR looks at the wider social and environmental responsibilities that businesses have and how they should meet them. Meanwhile, sustainability focuses on increasing concerns with the environment (particularly climate change), but it also has the wider meaning of the long-term sustainability of the firm itself. Should privately owned, profit-seeking companies be concerned with acting in a socially responsible, moral, and humane way? The response that one makes to this question depends, to a large degree, on one's beliefs as to the central purpose and responsibility of businesses, which can be summed up in the following five categories: shareholder capitalism, stakeholder capitalism, ethical capitalism, ethical within capitalism, and ethical against capitalism.
Aligning Large Language Models with Searcher Preferences

The paradigm shift from item-centric ranking to answer-centric synthesis is redefining the role of search engines. While recent industrial progress has applied generative techniques to closed-set item ranking in e-commerce, research and deployment of open-ended generative search on large content platforms remain limited. This setting introduces challenges, including robustness to noisy retrieval, non-negotiable safety guarantees, and alignment with diverse user needs. In this work, we introduce SearchLLM, the first large language model (LLM) for open-ended generative search. We design a hierarchical, multi-dimensional reward system that separates bottom-line constraints, including factual grounding, basic answer quality and format compliance, from behavior optimization objectives that promote robustness to noisy retrieval and alignment with user needs. Concretely, our reward model evaluates responses conditioned on the user query, session history, and retrieved evidence set, combining rule-based checks with human-calibrated LLM judges to produce an interpretable score vector over these dimensions. We introduce a Gated Aggregation Strategy to derive the training reward for optimizing SearchLLM with Group Relative Policy Optimization (GRPO). We deploy SearchLLM in the AI search entry of RedNote. Offline evaluations and online A/B tests show improved generation quality and user engagement, increasing Valid Consumption Rate by 1.03% and reducing Re-search Rate by 2.81%, while upholding strict safety and reliability standards.
FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record

Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. We propose a new method, called Federated-Autonomous Deep Learning (FADL) that trains part of the model using all data sources in a distributed manner and other parts using data from specific data sources. We observed that FADL outperforms traditional federated learning strategy and conclude that balance between global and local training is an important factor to consider when design distributed machine learning methods , especially in healthcare.