ethicalabs/Research-Intent-Collab huggingface dataset:
EXPLAINABLE DEEP LEARNING MODELS FOR MEDICAL IMAGE DIAGNOSIS BRIDGING ACCURACY AND INTERPRETABILITY

The growing use of deep learning models in diagnosing medical images has resulted in impressive gains in terms of the accuracy in diagnosis. Still, the lack of transparency in these models tends to increase the resistance of these models in clinical use. This paper will fill the accuracy-interpretability gap by creating explainable deep learning models that do not affect predictive accuracy. An explainability methodology was incorporated into a convolutional neural network (CNN) architecture using the state-of-the-art explainability methods, Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Model-Agnostic Explanations (LIME), and Integrated Gradients, to visualize model reasoning and locate important diagnostic regions of medical images. Benchmark radiology and histopathology data sets were evaluated experimentally to assess their accuracy and F1-score, interpretability measures, and the level of clinician agreement. The proposed model attained a diagnostic accuracy of 95.8 and an interpretability score of 0.89, which indicates a high similarity of model explanations and expert annotations. Using an explainability tool compared to the control group led to a marked increase in clinical trust and comprehension of model predictions. The results highlight that deep learning systems can be highly diagnostic and, at the same time, produce a transparent decision-making process. The study can be considered significant to developing human-centric artificial intelligence in healthcare as it contributes to accountability, reliability, and interpretability in medical image analysis.
Language Model Alignment in Multilingual Trolley Problems

We evaluate the moral alignment of LLMs with human preferences in multilingual trolley problems. Building on the Moral Machine experiment, which captures over 40 million human judgments across 200+ countries, we develop a cross-lingual corpus of moral dilemma vignettes in over 100 languages called MultiTP. This dataset enables the assessment of LLMs' decision-making processes in diverse linguistic contexts. Our analysis explores the alignment of 19 different LLMs with human judgments, capturing preferences across six moral dimensions: species, gender, fitness, status, age, and the number of lives involved. By correlating these preferences with the demographic distribution of language speakers and examining the consistency of LLM responses to various prompt paraphrasings, our findings provide insights into cross-lingual and ethical biases of LLMs and their intersection. We discover significant variance in alignment across languages, challenging the assumption of uniform moral reasoning in AI systems and highlighting the importance of incorporating diverse perspectives in AI ethics. The results underscore the need for further research on the integration of multilingual dimensions in responsible AI research to ensure fair and equitable AI interactions worldwide. Our code and data are at https://github.com/causalNLP/moralmachine
The Influence of Artificial Intelligence-Generated Content on Consumers' Purchase Decision in E-commerce: An Action Mechanism Study

Consumers making choices in e-commerce are now organized by artificial intelligence-generated and -orchestrated content (AIGC). This study looks at two typical surfaces, namely, algorithmic recommendations and urgency cues, through an exploratory secondary analysis of a survey (N=102) of young and digitally active shoppers. This paper has constructed concise variables on the basis of the questionnaire, and reports descriptive statistics, chi-square screens as well as two parsimonious logistic regressions. Results indicate that reported influence by recommendations is positively associated with comfort about data collection and shopping frequency, while chatbot exposure shows a negative association; dynamic-pricing exposure shows a borderline positive signal. Reported influence by urgency messages is predicted by perceived preference accuracy and trust in AI over human service. AI familiarity, perceived intrusiveness, perceived bias, overall satisfaction, and basic demographics do not reach conventional significance in either model; chi-square tests similarly show no detectable segmentation by gender or education in the tested outcomes. Together, the findings suggest that influence depends less on generic awareness and more on situated perceptions (accuracy, trust, data comfort) and context of use (task readiness). This paper translates these patterns into actionable guidance on transparency, explainability, pricing fairness cues, chatbot orchestration, and responsible urgency, while noting limitations of sample scope, self-reporting, and model convergence.
A study of the role of government policies in promoting health insurance coverage and improving healthcare access

Access to healthcare services is essential for individuals' well-being, and health insurance plays a pivotal role in ensuring this access by mitigating financial barriers associated with medical expenses. This research paper aims to explore the correlation between health insurance and healthcare access, focusing on the influence of health insurance coverage on individuals' ability to obtain and utilize healthcare services. The paper begins by defining health insurance and underlining its significance in facilitating healthcare access. It then examines various forms of health insurance coverage, including private insurance, government-sponsored programs such as Medicaid and Medicare, and the role of health insurance exchanges in providing access to different insurance plans. Factors affecting individuals' decisions to enrol in health insurance programs, including affordability and eligibility criteria, are also discussed. Furthermore, the paper investigates the impact of health insurance coverage on healthcare access, emphasizing its positive effects on healthcare utilization, preventive care, and overall health outcomes. It also addresses challenges faced by individuals with limited or no insurance coverage, such as difficulties in accessing timely and appropriate care, financial burdens, and disparities in healthcare access and outcomes. Moreover, the paper explores the role of government policies and regulations in shaping health insurance coverage and healthcare access, with a particular focus on examples like the Affordable Care Act (ACA) in the United States.
Self-Determination and Decolonization

Human rights historians have made two erroneous arguments concerning self-determination: first, that anticolonial movements used the discourse of self-determination primarily for instrumental goals, and to achieve broader goals, eg, economic development, racial equality. Second, that, by the 1960s, self-determination’s fiercest advocates in the Global South had reduced the principle cum-right ‘to the receipt of statehood and perpetual non-intervention thereafter,’ diluting its liberal, democratic implications. Both arguments, this chapter argues, oversimplify the myriad ways in which a variety of state and non-state actors deployed self-determination claims to envision sovereignty and human rights in the era of decolonization, where self-determination was a form of claim making about the nature and scope of post-colonial rights and sovereignty, an open-ended, rather than a fixed, concept. This essay focuses on UN debates, over the status of self-determination as a human right, among colonial powers and anti-colonial activists concerning the proper ‘form’ that self-determination could/should take.
Data Flow and Distributed Deep Neural Network based low latency IoT-Edge computation model for big data environment

Abstract   The trillion-fold increase in computing power brings the accessibility of deep learning to everyone. Deep learning offers precise information almost all the time when compared to other learning algorithms. On the other hand, the popularity of Internet of Things (IoT) has increased in various areas such as Smart City, Oil Mining, and Transportation. Edge/Fog computing environment helps to handle significant challenges faced by the IoT, viz. latency, bandwidth consumption, and everlasting network connectivity. For analytics in Edge computing, which is distributed in nature, the trend is more towards distributed machine learning. This research work is focused on the integration of data flow and distributed deep learning in the IoT-Edge environment to bring down the latency and increase accuracy starting from the data generation phase. To this end, a novel Data Flow and Distributed Deep Neural Network (DF-DDNN) based IoT-Edge model for big data environment has been proposed. Our proposed method has resulted in latency reduction of up to 33% when compared to the existing traditional IoT-Cloud model.
N-body simulations in modified Newtonian dynamics

We describe some results obtained with N-MODY, a code for N-body simulations of collisionless stellar systems in modified Newtonian dynamics (MOND). We found that a few fundamental dynamical processes are profoundly different in MOND and in Newtonian gravity with dark matter. In particular, violent relaxation, phase mixing and galaxy merging take significantly longer in MOND than in Newtonian gravity, while dynamical friction is more effective in a MOND system than in an equivalent Newtonian system with dark matter.
THE IMPACT OF CENTRAL BANK DIGITAL CURRENCY ON DIGITAL FINANCIAL INCLUSION: EVIDENCE FROM NIGERIA

Financial inclusion continues to be a significant challenge in Nigeria since many Nigerians are excluded from the financial ecosystem. This article investigates the impact of the Central Bank Digital Currency (CBDC) on digital financial inclusion using a quantitative research design and data collected from Nigerians. This was a correlational quantitative research study using a survey questionnaire distributed to 500 members of the Nigerian public using the eNaira. The results indicate that CBDC has a positive impact on digital financial inclusion; it facilitates improved access to financial services, reduces transaction costs, increases the number of banked members of the population, and enhances the efficiency of digital payments. This study concludes that CBDC is essential to improving financial inclusion in Nigeria. Based on these findings, it is recommended that policymakers in Nigeria continue to promote and prioritize the adoption of CBDC. Therefore, future research could explore the impact of CBDC on other aspects of financial inclusion beyond those explored in this study. For example, the research could be conducted to examine how CBDC impacts access to credit or explore the potential impact of CBDC on financial stability and monetary policy.
Sensing Matrix Design and Sparse Recovery on the Sphere and the Rotation Group

In this paper, {the goal is to design deterministic sampling patterns on the sphere and the rotation group} and, thereby, construct sensing matrices for sparse recovery of band-limited functions. It is first shown that random sensing matrices, which consists of random samples of Wigner D-functions, satisfy the Restricted Isometry Property (RIP) with proper preconditioning and can be used for sparse recovery on the rotation group. The mutual coherence, however, is used to assess the performance of deterministic and regular sensing matrices. We show that many of widely used regular sampling patterns yield sensing matrices with the worst possible mutual coherence, and therefore are undesirable for sparse recovery. Using tools from angular momentum analysis in quantum mechanics, we provide a new expression for the mutual coherence, which encourages the use of regular elevation samples. We construct low coherence deterministic matrices by fixing the regular samples on the elevation and minimizing the mutual coherence over the azimuth-polarization choice. It is shown that once the elevation sampling is fixed, the mutual coherence has a lower bound that depends only on the elevation samples. This lower bound, however, can be achieved for spherical harmonics, which leads to new sensing matrices with better coherence than other representative regular sampling patterns. This is reflected as well in our numerical experiments where our proposed sampling patterns perfectly match the phase transition of random sampling patterns.
Transparency, Accountability and Fighting Corruption: A Model on Whistle-blowing Processes

Local governments, as well as other public administrations, are expected to provide the general public with adequate information on how efficiently public resources are managed, in order to guarantee transparency as well as being accountable. Transparency should reduce the likelihood of hiding unfavourable information, which is generally related to corruption, fraud and illegal behaviour. One of the possible reactions against illegality is to encourage reporting of corruption and fraud, providing appropriate protection of whistle-blowers, namely providing them with adequate tools to report on wrongdoing, enhanching transparency and accountability, as well as professional ethics and good governance. Assuming the large body of literature concerning public sector transparency and accountability as scientific background, this chapter aims to model whistle-blowing processes in the local government context integrating managerial, legal and ICT perspectives of whistle-blowing.
Multiresolution quantum chemistry in multiwavelet bases: excited states from time-dependent Hartree–Fock and density functional theory via linear response

A fully numerical method for the time-dependent Hartree–Fock and density functional theory (TD-HF/DFT) with the Tamm–Dancoff (TD) approximation is presented in a multiresolution analysis (MRA) approach.
Religious Leaders And Peace Building: The Role of Tuan Guru and Pedanda in Conflict Resolution in Lombok – Indonesia

In some situations where the state is too weak to gain trust from the people, religious leaders have a significant role in maintaining social harmony. In many cases of the social unrest as what happened in Lombok, Tuan Guru and Pedanda played important roles in building a peaceful condition. Tuan Guru and Pedanda with their authorities have vital influences in calming down their Muslim and Hindu communities respectively. With their own ways, they were able to localize conflict issues, so much so that the social conflicts did not escalate to become greater massive riot. This article describes a number of efforts made by Tuan Guru and Pedanda  to establish peace in Lombok, such as: calming down the followers; localizing issue to reduce mass panic; reminding members of the family or community to resist social prejudice; socializing religious teachings especially the need to forge harmony; promoting “Semeton Sasak and Batur Bali” as a value of brotherhood among Balinese-Hindu and Sasaknese-Muslim; creating social sanctions against those who violated the agreements among communities; and improving the effectiveness of customary law known as awik-awik.[Pemimpin atau tokoh agama memiliki peran signifikan dalam merawat harmoni social, terutama di saat negara kurang memperoleh kepercayaan dari rakyat. Dalam sejumlah kasus kerusuhan social yang terjadi Lombok, Tuan Guru dan Pedanda memainkan peran penting dalam menciptakan kedamaian. Dengan otoritas mereka, Tuan Guru dan Pedanda mampu menenangkan massa. Dengan caranya sendiri, mereka mampu melokalisasi isu, sehingga eskalasi konflik tidak meluas menjadi kerusuhan yang massif. Artikel ini mendeskripsikan sejumlah upaya yang dikembangkan oleh Tuan Guru dan Pedanda dalam rangka bina damai di Lombok, seperti menenangkan jamaah; melokalisasi isu konflik agar massa tidak panic; mengingatkan keluraga atau anggota komunitas untuk menahan diri; mensosialisasikan nilai-nilai harmoni dalam agama, mempromosikan konsep “Semeton Sasak and Batur Bali” sebagai sebuah nilai persaudaraan antara orang Hindu-Bali dan Sasak-Muslim; menciptakan sanksi sosial bagi mereka yang melanggar kesepakatan bersama; dan meningkatkan efektifitas hukum adat yang dikenal dengan sebutan awik-awik.]
Anudo Ochieng Anudo v Tanzania (Judgment) (African Court on Human and Peoples’ Rights, App No 012/2015, 22 March 2018)

N/A
Random Measures, ANOVA Models and Quantifying Uncertainty in Randomized Controlled Trials

The paper introduces a novel approach to global sensitivity analysis, grounded in the variance-covariance structure of random variables derived from random measures. The proposed methodology facilitates the application of information-theoretic rules for uncertainty quantification, offering several advantages. Specifically, the approach provides valuable insights into the decomposition of variance within discrete subspaces, similar to the standard ANOVA analysis. To illustrate this point, the method is applied to datasets obtained from the analysis of randomized controlled trials on evaluating the efficacy of the COVID-19 vaccine and assessing clinical endpoints in a lung cancer study.
Communication and reporting of corporate social responsibility : a case study of ABB

We found corporate social responsibility as a very interesting subject to study. Today it is essential for multinational companies to implement corporate social responsibility in their organizations and during the last couple of years the concept has drawn great attention. This concept is not only in the spotlight for business leaders, it is also attracting the society at large. Due to the fact that corporate social responsibility is a relatively unexplored area, we have started to explain the concept in general, then communication and reporting within the field have been described. The study has been conducted with a single case study, and our choice of multinational company was ABB due to its long experience within this field. In order to gather information, documentation and interviews were used as data collection methods. We reached the conclusion that multinational companies highly prioritise the communication and reporting within this field. However, there is a lack of models, guidelines and standards concerning social performance, and there still exist a need for distinct measurement tools in order to assess companies’ engagement both within the social and environmental area.
On exact solutions and numerics for cold, shallow, and thermocoupled ice sheets

This three section report can be regarded as an extended appendix to (Bueler, Brown, and Lingle 2006). First we give the detailed construction of an exact solution to a standard continuum model of a cold, shallow, and thermocoupled ice sheet. The construction is by calculation of compensatory accumulation and heat source functions which make a chosen pair of functions for thickness and temperature into exact solutions of the coupled system. The solution we construct here is ``TestG'' in (Bueler and others, 2006) and the steady state solution ``Test F'' is a special case. In the second section we give a reference C implementation of these exact solutions. In the last section we give an error analysis of a finite difference scheme for the temperature equation in the thermocoupled model. The error analysis gives three results, first the correct form of the Courant-Friedrichs-Lewy (CFL) condition for stability of the advection scheme, second an equation for error growth which contributes to understanding the famous ``spokes'' of (Payne and others, 2000), and third a convergence theorem under stringent fixed geometry and smoothness assumptions.
The Role of Financial Institutions in Digital Currency Business Models of Central Banks

The development of central bank digital currencies brings into question the business model to be used for their application in the financial system. Three business models are imposed and considered in search of the one that will best be adopted into the financial system and enable optimal management of the monetary economics. The role of financial institutions and corporate banks varies depending on the business model. Each model, direct, indirect or hybrid, features its own specificities. Central banks have implemented research and analysis of the stated models in order to find the one that would generate maximum potential. The conducted analyses show that central banks lean toward the hybrid model that encompasses the benefits of the direct and indirect models. The hybrid model does not deteriorate financial stability, financial institutions keep their role, while monetary policy management becomes more effective than in the case of the remaining models.
Legal Certainty of Indigenous People's Land Rights of Former Customary Land Ownership Evidence After Its Validity Period Expires

This article examines the legal protection afforded to holders of written proof of customary land ownership in Indonesia, especially after the enactment of Article 96 (2) of Government Regulation No. 18 of 2021, which states that the document is not valid after five years. The provision has raised serious concerns about the legal certainty and constitutional rights of indigenous peoples. Using normative juridical methods and conceptual approaches, this study analyzes the tension between state-imposed administrative requirements and the recognition of land tenure based on historically rooted customary law. The study revealed that the abrupt cancellation of customary land documents without a transitional mechanism creates legal exceptions and exacerbates structural inequalities, especially among rural and indigenous peoples with limited access to legal resources. The analysis finds that such policy undermines the principles of procedural fairness, legitimate expectations, and the protection of property rights as guaranteed by the 1945 Constitution of Indonesia and international human rights instruments. The article argues for a rights-based and restorative framework to ensure transitional legal protection and the recognition of non-formal evidence rooted in Indigenous traditions. This approach is essential to aligning agrarian policy with justice, inclusivity, and constitutional obligations
Compressive Sensing: Performance Comparison Of Sparse Recovery Algorithms

Spectrum sensing is an important process in cognitive radio. A number of sensing techniques that have been proposed suffer from high processing time, hardware cost and computational complexity. To address these problems, compressive sensing has been proposed to decrease the processing time and expedite the scanning process of the radio spectrum. Selection of a suitable sparse recovery algorithm is necessary to achieve this goal. A number of sparse recovery algorithms have been proposed. This paper surveys the sparse recovery algorithms, classify them into categories, and compares their performances. For the comparison, we used several metrics such as recovery error, recovery time, covariance, and phase transition diagram. The results show that techniques under Greedy category are faster, techniques of Convex and Relaxation category perform better in term of recovery error, and Bayesian based techniques are observed to have an advantageous balance of small recovery error and a short recovery time.