ethicalabs/Research-Intent-Collab huggingface dataset:
Recalibrating the anti-corruption, transparency, and accountability formula to advance public health

Policy-makers, implementing organizations, and funders of global health programs aim to improve health care services and health outcomes through specific projects or systemic change. To mitigate the risk of corruption and its harmful effects on those initiatives, health programs often use multiple anti-corruption mechanisms, including codes of conduct, documentation and reporting requirements, and trainings. Unfortunately, the introduction of anti-corruption mechanisms tends to occur without an explicit consideration of how each mechanism will affect health services and health outcomes. This may overlook potentially more effective approaches. In addition, it may result in the introduction of too many controls (thereby stymying service delivery) and a focus on financial or procurement-related issues (at the expense of service delivery objectives). We argue that anti-corruption efforts in health programs can be more effective if they prioritize addressing issues according to their likelihood and level of harm to key program objectives. Recalibrating the anti-corruption formula in this way will require: (i) extending responsibility and ownership over anti-corruption from subject experts to public health and health system specialists, and (ii) enabling those specialists to apply the Fraud Risk Assessment methodology to develop tailored anti-corruption mechanisms. We fill a documented gap in guidance on how to develop anti-corruption mechanisms by walking through the seven analytical steps of the Fraud Risk Assessment methodology as applicable to health programs. We then outline best practices for any anti-corruption mechanism, including a focus on quality health delivery; the alignment of actors' incentives around the advancement of health objectives; and being minimally corruptible by design.
Role of Business Ethics in Raising Corporate Social Responsibility Awareness of Public Institutions (Case Study: Tayma Municipality, Tabuk Region in Saudi Arabia)

The purpose of this study is to investigate the potential relationship between the social responsibility and business ethics of personnel working for the municipality of Taima. as importance, this study shows how much Saudi government employees respect work ethics, which influences social responsibility. This study should also improve organizations' ethics to promote Arab-Islamic values. Our sample included 50 Saudi employees from various Taima Municipality departments. Our investigation relied heavily on quantitative method. A questionnaire is used to collect data in a hypothetical-deductive framework. The form of administration utilized is a self-administered survey conducted using the messaging application WhatsApp. This research's empirical findings show that corporate ethics affect economic, ethical, legal, and charitable social responsibility. These contributions strengthen the notion that Corporate Social Responsibility is inherently connected to ethics. CSR seeks to create socially responsible companies that smoothly integrate into society and operate sustainably in every way. By the end of the research, we recommend that social responsibility is essential for successful organizations to achieve their goals. In addition, conducting conferences on social responsibility, is a key aspect in sustainable development, and promoting the link between social responsibility, ethics, and institutional excellence.
On Representation Knowledge Distillation for Graph Neural Networks

IEEE Transactions on Neural Networks and Learning Representation (TNNLS), Special Issue on Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications
Health Information Exchange in Malaysia: Leveraging Interoperability on International Standards for Health Data Exchange

This research proposal investigates the implementation of Health Information Exchange (HIE) in Malaysia, focusing on understanding the technological, policy, and financial challenges and opportunities associated with its adoption and effectiveness. Through a comprehensive survey approach targeting healthcare practitioners, policymakers, IT professionals, and patients, the study aims to elucidate the current state of HIE, assess interoperability with international standards, and identify pathways for enhancement. Key objectives include analyzing technological barriers, evaluating policy and regulatory impacts, and exploring sustainable financial models for HIE. Findings indicate a positive trajectory towards HIE implementation, underscored by a broad recognition of its potential to transform healthcare delivery. However, challenges such as system integration, policy clarity, infrastructural readiness, and privacy concerns remain. Recommendations for future improvement emphasize strengthening infrastructure, clarifying policies, enhancing security measures, providing continuous training, fostering innovation, and increasing patient engagement. Furthermore, this study highlights the alignment of HIE implementation with the Sustainable Development Goals (SDGs), particularly SDG 3, to ensure universal health coverage and enhance the healthcare workforce's capacity for process innovation. Incorporating international best practices and a validated framework will further strengthen Malaysia’s healthcare system in the digital age.
EVALUATION OF HAND TREMOR SUPPRESSION USING A PASSIVE SURGICAL SUPPORT ROBOT IN SIMULATED RETINAL VASCULAR CANNULATION

Purpose:             To quantitatively evaluate hand tremors during retinal vascular cannulation surgery with a microneedle and investigate if a customized passive surgical robot suppresses hand tremors.                                   Methods:             A surgical simulation eye was modified, and an analysis system was used that synchronized the surgical video and accelerometer attached to the tail of the microneedle syringe. A cannulation target mimicking the optic disc and retinal vessels was designed in the bottom of the artificial eye. Two vitreous surgeons used this system to evaluate hand tremor 15 seconds before cannulation and 60 seconds during cannulation with and without the robot.                                   Results:             Fifteen seconds before cannulation, the mean horizontal, vertical, and combined components of hand tremor were 3.8 ± 1.7, 0.93 ± 0.48, and 4.0 ± 1.7 mG without the robot, decreasing to 3.3 ± 1.6, 0.87 ± 0.52, and 3.5 ± 1.8 mG, respectively, with the robot. Significant reductions were observed in the horizontal (P = 0.012) and combined components (P = 0.020). Sixty seconds during cannulation, the mean horizontal, vertical, and combined components of hand tremor were 3.3 ± 1.0, 0.53 ± 0.24, and 3.4 ± 1.0 mG, respectively, without the robot. These components significantly decreased with the robot to 2.2 ± 0.7, 0.40 ± 0.15, and 2.2 ± 0.7 mG, respectively, for all components (P &lt; 0.001). Hand tremor decreased significantly by 35% in the combined components during cannulation with the robot.                                   Conclusion:             The customized passive surgical support robot reduces hand tremor during simulated retinal vascular cannulation surgery.
How evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change

© 2020, The Author(s). Importance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections.
Benchmarking Secure Sampling Protocols for Differential Privacy

Differential privacy (DP) is widely employed to provide privacy protection for individuals by limiting information leakage from the aggregated data. Two well-known models of DP are the central model and the local model. The former requires a trustworthy server for data aggregation, while the latter requires individuals to add noise, significantly decreasing the utility of aggregated results. Recently, many studies have proposed to achieve DP with Secure Multi-party Computation (MPC) in distributed settings, namely, the distributed model, which has utility comparable to central model while, under specific security assumptions, preventing parties from obtaining others' information. One challenge of realizing DP in distributed model is efficiently sampling noise with MPC. Although many secure sampling methods have been proposed, they have different security assumptions and isolated theoretical analyses. There is a lack of experimental evaluations to measure and compare their performances. We fill this gap by benchmarking existing sampling protocols in MPC and performing comprehensive measurements of their efficiency. First, we present a taxonomy of the underlying techniques of these sampling protocols. Second, we extend widely used distributed noise generation protocols to be resilient against Byzantine attackers. Third, we implement discrete sampling protocols and align their security settings for a fair comparison. We then conduct an extensive evaluation to study their efficiency and utility.
EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising

Objective.Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, there is a lack of well-structured and standardized datasets with specific benchmark limit the development of DL solutions for EEG denoising.Approach.Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing DL-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 clean EEG segments, 3400 ocular artifact segments and 5598 muscular artifact segments, allowing users to synthesize contaminated EEG segments with the ground-truth clean EEG.Main results.We used EEGdenoiseNet to evaluate denoising performance of four classical networks (a fully-connected network, a simple and a complex convolution network, and a recurrent neural network). Our results suggested that DL methods have great potential for EEG denoising even under high noise contamination.Significance.Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of DL-based EEG denoising. The dataset and code are available athttps://github.com/ncclabsustech/EEGdenoiseNet.
Interpretable Deep Learning for Heat Demand Forecasting in Buildings : Explaining BiLSTM Predictions with SHAP

Buildings account for a substantial share of global energy consumption. Enhancing energy efficiency in buildings is crucial for both environmental sustainability and economic efficiency. Accurate and interpretable forecasting of energy demand is particularly critical in cold-climate regions to ensure efficient energy management, reduce wastage, and provide stakeholders with transparent insights into decision-making. This thesis presents the application of interpretable deep learning technique to forecast daily heat consumption in a school located in Northern Sweden. It specifically focuses on enhancing prediction accuracy while maintaining model transparency. A Bidirectional Long Short-Term Memory (BiLSTM) neural network is designed to capture temporal dependencies in multivariate time series data, comprising weather variables, calendar features, and historical consumption. To enable model interpretability, SHAP(SHapley Additive exPlanations) is applied to analyze global feature contributions and provide local explanations for individual predictions.
Large Graph Signal Denoising with Application to Differential Privacy

Over the last decade, signal processing on graphs has become a very active area of research. Specifically, the number of applications, for instance in statistical or deep learning, using frames built from graphs, such as wavelets on graphs, has increased significantly. We consider in particular the case of signal denoising on graphs via a data-driven wavelet tight frame methodology. This adaptive approach is based on a threshold calibrated using Stein's unbiased risk estimate adapted to a tight-frame representation. We make it scalable to large graphs using Chebyshev-Jackson polynomial approximations, which allow fast computation of the wavelet coefficients, without the need to compute the Laplacian eigendecomposition. However, the overcomplete nature of the tight-frame, transforms a white noise into a correlated one. As a result, the covariance of the transformed noise appears in the divergence term of the SURE, thus requiring the computation and storage of the frame, which leads to an impractical calculation for large graphs. To estimate such covariance, we develop and analyze a Monte-Carlo strategy, based on the fast transformation of zero mean and unit variance random variables. This new data-driven denoising methodology finds a natural application in differential privacy. A comprehensive performance analysis is carried out on graphs of varying size, from real and simulated data.
SISTEM MONITORING KARBON MONOKSIDA DENGAN METODE WIRELESS SENSOR NETWORK (WSN) BERBASIS INTERNET OF THINGS (IoT)

Karbon monoksida merupakan salah satu jenis polutan udara yang berada di lingkungan sekitar kita. Jika karbon monoksida masuk ke dalam tubuh manusia, maka akan mengikat sel darah merah 200 hingga 250 kali lebih kuat dibandingkan dengan oksigen, sehingga menggantikan peran oksigen dan membentuk senyawa kimia Karboksi Hemoglobin (COHb). Sebagai upaya memperkecil resiko dari keracunan karbon monoksida maka dibutuhkan sistem yang dapat memonitor kadar karbon monoksida sedini dan seakurat mungkin. Semakin dini dan akurat monitoring yang dilakukan, maka dapat memperkecil resiko keracunan karbon monoksida. Maka dalam penelitian ini dibuat alat yang dapat memonitor kadar karbon monoksida dengan teknologi Internet of Things (IoT) yang di padukan dengan sistem Wireless Sensor Network (WSN). Berdasarkan hasil pengujian yang telah dilakukan di lingkungan Fakultas Ilmu Kesehatan, Gor Mashud dan Fakultas Teknik Gedung Baru Universitas Siliwangi di dapatkan kesimpulan rata-rata pengiriman data untuk skema pengujian pertama adalah sekitar 10,1 detik dan untuk skema pengujian kedua adalah sekitar 5,31 detik. Dengan jarak maksimal komunikasi dari Node 2 ke Node 1 adalah 34,5 Meter dan jarak maksimal komunikasi dari Node 1 ke Node Sink adalah 37,3 Meter. Dimana semakin kecil jangkauan area komunikasi dari WSN maka akan semakin cepat juga rata-rata pengiriman data yang dilakukan.
Utilizing de Bruijn graph of metagenome assembly for metatranscriptome analysis

Metagenomics research has accelerated the studies of microbial organisms, providing insights into the composition and potential functionality of various microbial communities. Metatranscriptomics (studies of the transcripts from a mixture of microbial species) and other meta-omics approaches hold even greater promise for providing additional insights into functional and regulatory characteristics of the microbial communities. Current metatranscriptomics projects are often carried out without matched metagenomic datasets (of the same microbial communities). For the projects that produce both metatranscriptomic and metagenomic datasets, their analyses are often not integrated. Metagenome assemblies are far from perfect, partially explaining why metagenome assemblies are not used for the analysis of metatranscriptomic datasets. Here we report a reads mapping algorithm for mapping of short reads onto a de Bruijn graph of assemblies. A hash table of junction k-mers (k-mers spanning branching structures in the de Bruijn graph) is used to facilitate fast mapping of reads to the graph. We developed an application of this mapping algorithm: a reference based approach to metatranscriptome assembly using graphs of metagenome assembly as the reference. Our results show that this new approach (called TAG) helps to assemble substantially more transcripts that otherwise would have been missed or truncated because of the fragmented nature of the reference metagenome. TAG was implemented in C++ and has been tested extensively on the linux platform. It is available for download as open source at http://omics.informatics.indiana.edu/TAG.
Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization

Interpreting the behaviors of Deep Neural Networks (usually considered as a black box) is critical especially when they are now being widely adopted over diverse aspects of human life. Taking the advancements from Explainable Artificial Intelligent, this paper proposes a novel technique called Auto DeepVis to dissect catastrophic forgetting in continual learning. A new method to deal with catastrophic forgetting named critical freezing is also introduced upon investigating the dilemma by Auto DeepVis. Experiments on a captioning model meticulously present how catastrophic forgetting happens, particularly showing which components are forgetting or changing. The effectiveness of our technique is then assessed; and more precisely, critical freezing claims the best performance on both previous and coming tasks over baselines, proving the capability of the investigation. Our techniques could not only be supplementary to existing solutions for completely eradicating catastrophic forgetting for life-long learning but also explainable.
Cost-analysis comparison of clinical risk assessment with and without ROMA for the management of women with pelvic masses

Objective: Pelvic masses can be classified as low risk (likely benign) and high risk (likely malignant) based on an initial clinical risk assessment, which involves a detailed history, physical exam, basic laboratory tests, and imaging. In recent years, the Risk of Ovarian Malignancy Algorithm (ROMA), which combines CA125, HE4 and menopausal status, has emerged as a powerful tool in the classification of pelvic masses and triage of patients to either a generalist gynecologist or a gynecologic oncologist for management. The objective of this study was to evaluate whether the use of ROMA, alone or in combination with Initial Clinical Risk Assessment (ICRA), provides cost savings compared to triage based on ICRA alone.  Methods: A health-economic decision model was developed to assess clinical and cost differences associated with three different clinical pathways of risk assessment for a pelvic mass: ICRA alone, ROMA alone, or ICRA + ROMA in combination. Using previously reported accuracy rates and patient characteristics from a prospective, multicenter, blinded clinical trial, total healthcare costs were modeled for each clinical pathway using the Medicare 2020 reimbursement rates.  Results: A total of 461 patients with pelvic masses were included with 10.4% ultimately diagnosed with epithelial ovarian cancer. Total healthcare costs for patients with benign disease, EOC, or low malignant potential tumors (LMP) (n = 441) triaged using ROMA alone were 3.3% lower than when triaged using ICRA alone. While lab costs increased 55% using ROMA, the use of ROMA alone resulted in a 4% decrease in laparoscopy costs and a 3.1% decrease in laparotomy costs compared with ICRA alone. Similarly, total costs associated with a combination of ICRA + ROMA were 3.9% lower than total costs associated with ICRA alone. The model also predicted a 63% reduction in repeat surgeries resulting from false negative ICRA when using ROMA to triage patients.  Conclusion: Triage of women with pelvic masses using the more sensitive ROMA score lowers overall healthcare costs compared to ICRA alone. With fewer false negative results than ICRA alone, the ROMA score improves initial detection of malignancy and reduces second surgical treatments in women with pelvic masses.
Regenerative Medicine Strategies for Spinal Cord Injury: Advances in Stem Cell Therapy

Spinal cord injuries disrupt the motor, sensory, and autonomic functions routinely carried out by the spinal cord, with injury progressing from primary mechanical damage from the initial trauma to a secondary phase driven by inflammation and cellular cascades. This disruption significantly impacts the patient’s ability to perform basic physiological and voluntary functions seen in a normal spinal cord, which often results in long-term disability and dependence on supportive care. Stem cell therapies, including mesenchymal stem cells, neural stem cells, and induced pluripotent stem cells, have been investigated as potential regenerative approaches that may promote repair through neuroprotection, remyelination, and axonal regeneration. Preclinical studies have demonstrated encouraging results in motor and sensory recovery following injury; however, clinical evidence remains limited and variable. Some studies report improvements in motor and sensory function post-injury, with improvements in bladder and bowel management, tissue repair, and other functions. Overall, the outcomes vary based on cell type, delivery method, and the stage of spinal cord injury. The key challenges of stem cell therapy include safety concerns and the limited number of small-scale studies currently available. Additionally, understanding the variability in therapeutic outcomes and identifying optimal treatment conditions are critical steps toward advancing stem cell therapies in spinal cord injury repair. This review aims to characterize and summarize the stem cell approach to the treatment of spinal cord injuries, while also critically highlighting the limitations of current preclinical and clinical evidence, as well as the importance of continued investigation into the long-term and functional recovery processes and possibilities, as well as the patient’s quality of life following treatment with stem cells for spinal cord injury.
Central bank digital currency: The new kid on the block?

The emergence of cryptocurrencies and stablecoins has prompted many central banks to study the issuance of digital central bank money. This raises the question of whether central banks should also use Blockchain technology. A notable challenge here is the scalability of Blockchain technology. Another challenge arises from the need to strike the right balance between know-your-customer requirements and privacy protection. Both problems are easier to solve without Blockchain. Blockchain, however, needs a means of payment. This paper therefore argues that central bank digital currency (CBDC) does not need Blockchain, but that Blockchain needs CBDC.