ethicalabs/Research-Intent-Collab huggingface dataset:
Legal Protection for the Rights of Indigenous Peoples in the Exemption of Land Rights, According to Indonesian Law

This study aims to analyze legal protection for indigenous peoples in the release of joint land rights according to positive law in Indonesia and to analyze the legal consequences of the release of joint land rights according to positive law in Indonesia. This study is a type of normative research using the Law approach, the conceptual approach, and the case approach. The results of this study are as follows: First, Legal protection for indigenous peoples in the release of joint land rights, as outlined in positive law in Indonesia, encompasses both preventive and repressive legal protection. Preventive legal protection means that the community can request compensation or compensation for the losses caused by the problem. Meanwhile, repressive legal protection is given to customary law communities, namely that customary law communities can file a complaint with the authorities if they customary law community feel disadvantaged. This is based on Article 53, paragraph (2) of Law Number 2 of 2012 concerning Land Acquisition for Development in the Public Interest. Second, the legal consequences of the release of joint land rights according to the law in Indonesia, among others: Transfer of land rights, compensation, impact on the sustainability of customary rights, potential for disputes and resolution, and loss of access for customary law communities in terms of livelihoods.
The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health

Abstract                 Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under-or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.
Poly(L-lactic Acid) Microspheres Containing Neutron-Activatable Holmium-165: A Study of the Physical Characteristics of Microspheres Before and After Irradiation in a Nuclear Reactor

The solvent evaporation technique was employed to prepare poly(L-lactic acid) (PLA) microspheres with 165Ho acetylacetonate (Ho-AcAc). Particle size, percentage Ho-165, percent residual solvent, and retentive ability of the spheres were found to be strongly affected by preparatory conditions. Differential scanning calorimetry (DSC) thermograms suggested that the Ho-AcAc existed in the PLA matrix as a molecular dispersion. High neutron flux irradiations of the PLA spheres in a nuclear reactor produced Ho-166, a therapeutic radionuclide that emits high-energy negatrons (Emax = 1.84 MeV; half-life = 26.9 hr). The gamma radiation dose (53-75 Mrad) from the core of the reactor provided an overkill of all bioburdens in the PLA spheres. Gel permeation chromatography (GPC) analysis showed that these irradiations caused a reduction in PLA molecular weight. Infrared spectra, 13C NMR spectra, 1H NMR spectra, and DSC thermograms further confirmed the presence of lower molecular weight PLA but proved the overall maintenance of PLA structure.
Regularity analysis for stochastic partial differential equations with nonlinear multiplicative trace class noise

In this article spatial and temporal regularity of the solution process of a stochastic partial differential equation (SPDE) of evolutionary type with nonlinear multiplicative trace class noise is analyzed.
PRIVACY RIGHTS IN THE AGE OF DIGITAL SURVEILLANCE: THE LEGITIMACY OF STATE MASS DATA COLLECTION PRACTICES UNDER INTERNATIONAL LAW

The rapid advancement of digital surveillance technologies has fundamentally challenged traditional conceptions of privacy under international law. This article examines the legitimacy of state mass data collection practices through the dual lenses of international human rights law and contemporary conflict scenarios, with particular focus on the Russia-Ukraine war. Modern surveillance systems - including AI-driven facial recognition, bulk metadata collection, and biometric tracking - increasingly enable governments to monitor populations with unprecedented scope and precision. While these technologies offer security benefits, they frequently violate core privacy protections under Article 17 of the ICCPR and Article 8 of the ECHR, particularly when deployed without adequate safeguards. The article presents a comparative analysis of Russia's expansive SORM surveillance regime versus the EU's rights-based GDPR framework, demonstrating how legal systems diverge in balancing security and privacy. The Russia-Ukraine conflict provides a critical case study of surveillance technologies weaponized in armed conflict. Evidence shows Russia's use of telecom metadata and facial recognition to target anti-war protesters and enforce conscription, while Ukraine's Diia app illustrates the tension between national security needs and privacy rights during wartime. These practices raise fundamental questions about the applicability of international humanitarian law to digital repression and the extraterritorial obligations of occupying powers. Key findings reveal three systemic challenges: (1) the inadequacy of current legal frameworks to regulate AI-enhanced surveillance, (2) the exploitation of "national security" justifications to bypass privacy protections, and (3) the emergence of digital occupation tactics involving mass data appropriation. The article critiques recent ECtHR and ICJ cases addressing these issues, highlighting gaps in enforcement mechanisms. The study proposes concrete policy solutions: strengthening Convention 108+ as a global privacy standard, implementing targeted sanctions on surveillance technology exports, and developing ethical protocols (Nuredin &amp; Nuredin, 2023) for using digital evidence in war crimes prosecutions. These measures aim to reconcile security imperatives with fundamental rights in an era where digital surveillance has become a key battleground for both physical conflicts and information warfare.
AlphaFold-Metainference: Prediction of Structural Ensembles of Disordered Proteins

Abstract                 Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. To address this problem, we introduce the AlphaFold-Metainference method to use AlphaFold-derived distances as structural restraints in molecular dynamics simulations to construct structural ensembles of ordered and disordered proteins. The results obtained using AlphaFold-Metainference illustrate the possibility of making predictions of the conformational properties of disordered proteins using deep learning methods trained on the large structural databases available for folded proteins.
The role of the Model Validation function to manage and mitigate model risk

This paper describes the current taxonomy of model risk, ways for its mitigation and management and the importance of the model validation function in collaboration with other departments to design and implement them.
A High Performance Global Routing Algorithm on Julia Parallel Computing Platform

o keep pace with the design requirements of Integrated Circuits (ICs), parallel processing is adopted. The path to be routed between two nodes may or may not be dependent on the previously routed paths. The solution requires careful attention in distributing the nets to be routed to different processors. Previous work on allocating the tasks to processors has been quite successful, reporting upto 3x improvement on 4 cores and 5x improvement on 8 core machine. The advantage of increasing the number of cores diminishes with each added processor and the challenge lies in being able to maintain the improvement per added core. The existing techniques of distributing the nets cannot provide additional advantage of using more than 8 cores. This paper improves the work on parallelizing global routing using a technique of balancing the load on the processors for better utilization of the resources. A relatively new budding platform Julia has been used which provides the ease of programming while maintaining the performance of the C language. Technique used in this paper has enabled to use 16 cores with routing solutions obtained in 0.8 minutes achieving 12.5 times speedup compared to sequential processing on a single core
Sales Prediction and Product Recommendation Model Through User Behavior Analytics

The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics. RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level. For the purchase prediction model, XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their. For the product recommendation model, the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes. Moreover, some suggestions are proposed for the marketing department according to the outcomes. Overall, the XGBoost model achieved better performance and better accuracy with F1-score around 0.789. The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness. Furthermore, it recommend specific products to new customers. This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.
A Study on Handwritten Digit Categorization of RAM-based Neural Network

A RAM-based neural network is a weightless neural network based on binary neural network(BNN) which is efficient neural network with a one-shot learning. RAM-based neural network has multiful information bits and store counts of training in BNN. Supervised learning based on the RAM-based neural network has the excellent performance in pattern recognition but in pattern categorization with unsupervised learning as unsuitable. In this paper, we propose a unsupervised learning algorithm in the RAM-based neural network to perform pattern categorization. By the proposed unsupervised learning algorithm, RAM-based neural network create categories depending on the input pattern by itself. Therefore, RAM-based neural network for supervised learning and unsupervised learning should proof of all possible complex models. The training data for experiments provided by the MNIST offline handwritten digits which is consist of 0 to 9 multi-pattern.
Analysis of Cognition and Willingness to Use DeepSeek Artificial Intelligence Generated Content (AIGC) among University Faculty and Students

Object: Artificial Intelligence Generated Content (AIGC), exemplified by DeepSeek, is reshaping university teaching. However, the innovations and risks it presents make faculty and students' understanding and willingness to use AIGC a key factor. Based on the UNESCO humanistic framework and research progress both domestically and internationally, this study aims to provide empirical evidence for the positive integration of technology and education, and the application of AIGC in education, to improve educational quality.Research Method: Generative Artificial Intelligence (AIGC), such as DeepSeek, is profoundly transforming education. This study, conducted through a questionnaire survey, collected data from 216 university faculty and 463 university students across various majors and grades. The study analyzed their understanding of and willingness to use DeepSeek's generative AIGC.Research Results: The results show that university faculties and students are mainly exposed to DeepSeek through social media, course teaching and other channels, and its usage scenarios are concentrated in academic fields such as thesis writing, course design, etc. There is a significant difference between different disciplinary groups in terms of their cognition and willingness to use.Conclusion: The conclusion obtained from further analysis is further analysis reveals that technology trust is positively correlated with willingness to use it in the future, while privacy leakage and academic integrity issues are ethical concerns for university faculty.Suggestions: This study proposes to play a role in providing empirical evidence for the positive integration of technology and education and the application of generative artificial intelligence (AIGC) in education aimed at improving the quality of education.
Super Sparse 3D Object Detection

As the perception range of LiDAR expands, LiDAR-based 3D object detection contributes ever-increasingly to the long-range perception in autonomous driving. Mainstream 3D object detectors often build dense feature maps, where the cost is quadratic to the perception range, making them hardly scale up to the long-range settings. To enable efficient long-range detection, we first propose a fully sparse object detector termed FSD. FSD is built upon the general sparse voxel encoder and a novel sparse instance recognition (SIR) module. SIR groups the points into instances and applies highly-efficient instance-wise feature extraction. The instance-wise grouping sidesteps the issue of the center feature missing, which hinders the design of the fully sparse architecture. To further enjoy the benefit of fully sparse characteristic, we leverage temporal information to remove data redundancy and propose a super sparse detector named FSD++. FSD++ first generates residual points, which indicate the point changes between consecutive frames. The residual points, along with a few previous foreground points, form the super sparse input data, greatly reducing data redundancy and computational overhead. We comprehensively analyze our method on the large-scale Waymo Open Dataset, and state-of-the-art performance is reported. To showcase the superiority of our method in long-range detection, we also conduct experiments on Argoverse 2 Dataset, where the perception range ($200m$) is much larger than Waymo Open Dataset ($75m$). Code is open-sourced at https://github.com/tusen-ai/SST.
States, nations, and self-determination: Afghanistan and decolonization at the United Nations

AbstractAfghanistan is not traditionally seen as a ‘decolonized’ state, given that it was never formally part of any empire. Yet Afghan state leaders embraced the language of anti-colonialism and self-determination to assert influence in the international community, and especially at the UN. This paper explores the interactions between Afghan elites and the UN, particularly the way that Afghanistan fought the growing global consensus that self-determination in the era of decolonization meant the establishment of an international states system. Afghan elites instead argued that self-determination was for peoples, not states. Afghanistan’s stance on self-determination, as an exception to territorial state centrism, provides a way of thinking about decolonization’s universalisms and particularities, as well as how it ultimately complicated Afghanistan’s own place in the international community. The article uses Afghanistan’s engagement with the UN General Assembly and its various subcommittees, from its membership in 1946 to the Soviet withdrawal from Afghanistan in 1989, to reflect on the ways decolonization became a global yet fractured phenomenon that came to mean numerous practices and could be used by different historical actors to articulate multiple, potentially competing visions of political autonomy and rights. International institutions like the UN provided crucial arenas where postcolonial statehood became the norm yet was nevertheless contested and questioned. By providing an exception to the UN’s focus on territorial statehood, Afghanistan demonstrates the ongoing fluidity and complexity of decolonization’s meaning and consequences, as well as the ways in which nations continue to inform the global.
Intellectual Property Challenges for AI-Driven Creativity: A Focus on Copyright and Patents in Emerging Economies

The integration of Artificial Intelligence (AI) into scientific advancements has become a crucial part of daily life for many people. In the creative industry, AI has the potential to not only enhance the level of creativity, but also productivity. The legal implications of this development must be carefully analyzed for the development of the creative industry, to establish a legal position on where AI stands in the increasingly competitive market, especially when Intellectual Property Right (IPR) as a key factor is taken into account. This research examines the need for a legal framework for AI. It was conducted through the lens of intellectual property law, to determine its place within existing IPR regimes using the comparative legal method. The results conceptually justify the urgency to regulate AI, and highlights the taxonomy dilemma of AI-generated works within the current dynamics of the creative industry, indicating a taxonomy dilemma. Further analysis also indicates that while there’s a decent urgency to address all of the normative issues that exist within Indonesia’s IPR legal framework, it’s imperative that Indonesia prioritizes the development of the copyright and patent regimes, which are closely related to the nature of AI-generated works.
Multivariate Data Analysis to Assess Process Evolution and Systematic Root Causes Investigation in Tablet Manufacturing at an Industrial Scale—A Case Study Focused on Improving Tablet Hardness

Background/Objectives: Only a few studies performed at industrial scale in non-simulated conditions have investigated the effect of input variability from the product’s lifecycle on product quality. The purpose of this work was to identify the root causes for the low and variable hardness of core tablets prepared using high-shear wet granulation through batch statistical modeling and to verify the short- and long-term effectiveness of the improvement actions. Methods: The novelty of this study is the use of multivariate methods for the complex assessment of a wide data set belonging to two proportional composition strengths, manufactured at an industrial scale, with different tablet shapes and sizes, with the aim of identifying inter-related active ingredient and process variables with the highest impact on hardness value and for defining optimal processing conditions leading to a robust product. Results: Four main variables affecting the output variable were identified: API particle size, nozzle type used for granulation, wet discharge, and drying intensity. These were included in an updated control strategy (3 out of 4 variables having to be within the desired ranges: API d0.5 &lt; 45 microns; granulation nozzle that ensures liquid dispersion into droplets; gentle wet discharge and drying processes). In the case of the product studied, the newly defined process conditions could even accommodate d0.5 up to 70 microns and still ensure adequate core tablet hardness (at least 30% above the lower specification limit) for the successive film-coating step. Conclusions: Besides the beneficial impact of reducing the risk for out-of-specification hardness results, this study also offered the benefit of cost avoidance and yield improvement. The improvement was confirmed through the significant average hardness increase (15–20%) and between-batch variability decrease, leading to decent sigma quality levels (2.5) for the control phase batches.
THE NEED AND THE CHALLENGES OF THE INTRODUCTION OF CENTRAL BANK DIGITAL CURRENCIES

Central banks observe the impact of technological changes and digitalisation on economic activity and changes in preferences and behaviour of the general public which has been showing increased interest in digital assets. The desire to preserve monetary sovereignty and stability, at a time of sharp decline in the use of cash, leads central banks to launch projects aimed at introducing central bank digital currency (CBDC). This paper explains the incentives for the development of CBDC, analyzes the dilemmas in their design and conceptualization, gives an overview and presents the results of the ongoing CBDC projects. The introduction of CBDC entails controversial choices, potentially opens up risks for existing financial institutions and faces the challenge of achieving widespread acceptance. An approach to the development of CBDC that respects all participants in the financial system and assigns them operational roles in the functioning of the digital money system of central banks can prevent disruption and obstructions and at the same time strengthen its likely the benefits, since it is difficult to imagine the future of money without central bank digital money.