ethicalabs/Research-Intent-Collab huggingface dataset:
OP89 The CE-Signal, A New Simplified Health Technology Assessment Method To Determine Whether Interventions Are Cost-Effective

IntroductionConducting a cost-effectiveness analysis (CEA) is resource consuming, and therefore the Dutch National Health Care Institute (ZIN) only performs those for interventions with a high budget impact. Sometimes, cost-effectiveness (CE) estimates are clearly below or far above reference values, which makes full cost-effectiveness assessments less vital. The objective of this study was to develop an efficient and simplified method to identify interventions that are clearly cost-(in)effective.MethodsThe method makes use of headroom analysis. Several HTA experts and other relevant stakeholders have been asked to provide feedback on a preliminary version of the CE signal.ResultsThe method consists of five steps. In the first step (i) the relevant willingness-to-pay threshold is determined. Reference values are used by ZIN for the maximum willingness-to-pay per incremental quality-adjusted life year (QALY), depending on burden of disease.In next step (ii) the health gain that can realistically be obtained with the new treatment is estimated. Hereby the effect of the intervention on the clinical outcomes, quality of life and gained life years is determined to estimate the number of QALYs gained, including uncertainty. Then (iii) the societal cost maximum (SCM) of the new treatment is calculated by multiplying step 2 with step 1. In step four (iv) the incremental treatment costs are estimated looking at both the costs and savings for both treatments options for the average patient. In the final step (v) the incremental treatment costs are compared to the SCM to determine if the intervention is probably cost effective, probably not cost-effective or if a conclusion cannot be drawn.ConclusionsThis method has proven to be feasible and could be a valuable addition to the current cost-effectiveness assessment toolbox. The CE-signal is being validated against performing a full cost-effectiveness analysis.
Artificial intelligence streamlines diagnosis and assessment of prognosis in Brugada syndrome: a systematic review and meta-analysis

Aim:  The objective of this systematic review and meta-analysis was to determine the diagnostic and prognostic utility of artificial intelligence/machine learning (AI/ML) algorithms in Brugada Syndrome (BrS).           Methods:  A systematic review and meta-analysis of the literature was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE, EMBASE, SCOPUS, and WEB OF SCIENCE databases were searched for relevant articles. Abstract and title screening, full-text review, and data extraction were conducted independently by two of the authors. Conflicts were resolved via discussion among authors. A risk-of-bias assessment was performed using the QUADAS-2 tool for diagnostic studies and the PROBAST tool for prognostic studies. Forest plots and the summary area under the receiver operating characteristic (SAUROC) curve were done in R.           Results:  A total of 12 papers were included in our study. Among the best-performing diagnostic algorithms from each study, the sensitivity and specificity ranged from 0.80 to 0.89 and 0.74 to 0.97, respectively. In overall studies, sensitivity was 0.845 ± 0.014 and specificity was 0.892 ± 0.062 using a random effects model. A pooled analysis of the summary area under the receiver operating characteristic curve (SAUROC) was 0.77 for diagnostic studies. Prognostic studies showed good performance as well, with the AUC of the best-performing prognostic algorithms ranging from 0.71 to 0.90.           Conclusions:  Overall, AI/ML algorithms had high diagnostic and prognostic accuracy. These results highlight the potential of AI/ML algorithms for the diagnosis and prognosis of BrS and permit a choice of the best-performing ML algorithms.
YOLO-NAS-Bench: A Surrogate Benchmark with Self-Evolving Predictors for YOLO Architecture Search

Neural Architecture Search (NAS) for object detection is severely bottlenecked by high evaluation cost, as fully training each candidate YOLO architecture on COCO demands days of GPU time. Meanwhile, existing NAS benchmarks largely target image classification, leaving the detection community without a comparable benchmark for NAS evaluation. To address this gap, we introduce YOLO-NAS-Bench, the first surrogate benchmark tailored to YOLO-style detectors. YOLO-NAS-Bench defines a search space spanning channel width, block depth, and operator type across both backbone and neck, covering the core modules of YOLOv8 through YOLO12. We sample 1,000 architectures via random, stratified, and Latin Hypercube strategies, train them on COCO-mini, and build a LightGBM surrogate predictor. To sharpen the predictor in the high-performance regime most relevant to NAS, we propose a Self-Evolving Mechanism that progressively aligns the predictor's training distribution with the high-performance frontier, by using the predictor itself to discover and evaluate informative architectures in each iteration. This method grows the pool to 1,500 architectures and raises the ensemble predictor's R2 from 0.770 to 0.815 and Sparse Kendall Tau from 0.694 to 0.752, demonstrating strong predictive accuracy and ranking consistency. Using the final predictor as the fitness function for evolutionary search, we discover architectures that surpass all official YOLOv8-YOLO12 baselines at comparable latency on COCO-mini, confirming the predictor's discriminative power for top-performing detection architectures. The code is available at https://github.com/VDIGPKU/YOLO-NAS-Bench.
Blended Emotion in-the-Wild: Multi-label Facial Expression Recognition Using Crowdsourced Annotations and Deep Locality Feature Learning

Comprehending different categories of facial expressions plays a great role in the design of computational model analyzing human perceived and affective state. Authoritative studies have revealed that facial expressions in human daily life are in multiple or co-occurring mental states. However, due to the lack of valid datasets, most previous studies are still restricted to basic emotions with single label. In this paper, we present a novel multi-label facial expression database, RAF-ML, along with a new deep learning algorithm, to address this problem. Specifically, a crowdsourcing annotation of 1.2 million labels from 315 participants was implemented to identify the multi-label expressions collected from social network, then EM algorithm was designed to filter out unreliable labels. For all we know, RAF-ML is the first database in the wild that provides with crowdsourced cognition for multi-label expressions. Focusing on the ambiguity and continuity of blended expressions, we propose a new deep manifold learning network, called Deep Bi-Manifold CNN, to learn the discriminative feature for multi-label expressions by jointly preserving the local affinity of deep features and the manifold structures of emotion labels. Furthermore, a deep domain adaption method is leveraged to extend the deep manifold features learned from RAF-ML to other expression databases under various imaging conditions and cultures. Extensive experiments on the RAF-ML and other diverse databases (JAFFE, CK$$+$$+, SFEW and MMI) show that the deep manifold feature is not only superior in multi-label expression recognition in the wild, but also captures the elemental and generic components that are effective for a wide range of expression recognition tasks.
Tallinn manual: Cyber warfare in Indonesian regulation

Abstract                Advances in communication information technology have made various infrastructures dependent on cybernetics technology. However, there are threats to the system. One of the threats discussed in the context of the study of international humanitarian law is cybernetics war, which is carried out by high entities such as the state. The threat is coordinated with the armed forces or military in order to gain an advantage over the opponent. Recognizing that there are ever-evolving and very dynamic threats to the sovereignty of the state and nation, in this case by considering advances in internet technology, the government has formulated regulations regarding cyber defence as an effort to overcome cyberattacks that cause disruption to the implementation of national defence, so that it can realized cyber defence (cyber defence). In general, there have been a number of national laws and regulations governing matters relating to activities in cyberspace. The research method of the paper used normative legal research to collect and analyse legal material regarding two main concepts, namely: cyber warfare regulations and information regulations. The nature of legal research was to provide prescription on legal concept or legal phenomenon. The paper finds out that Tallinn Manual is a legal concept that give an instruction to respond cyber-attack, in which the concept can be adopted. In the meantime, in legal vacuum of cyber warfare regulation, the concept inside Tallinn Manual can be interpret to the existing law in Indonesia. Findings in the paper indicates that cyber-attack according to existing regulation can be classified into three categories, namely: ordinary attack, middle attack or terrorism, and massif attack or cyber warfare.
NOVA3D: Normal Aligned Video Diffusion Model for Single Image to 3D Generation

8 pages, 7 figures, accepted by ICME 2025
The tensions of welfare state reform and the potential of a universal basic income

There are many good reasons to think about a universal basic income while exploring the issues of welfare state reform in developed countries. It is especially true in the context of an increasing emphasis on activation – or even workfare – in almost all welfare states. In this chapter, I intend to show how the discussion about basic income forces us to think about three key tensions in welfare state reform: the tension between universalism and selectivity (I.), between in-cash and in-kind benefits (II.), and between conditionality and unconditionality (III.).
The Right Accounting of Wrongs: Examining Temporal Changes to Human Rights Monitoring and Reporting

AbstractScholars contend that the reason for stasis in human rights measures is a biased measurement process, rather than stagnating human rights practices. We argue that bias may be introduced as part of the compilation of the human rights reports that serve as the foundation of human rights measures. An additional source of potential bias may be human coders, who translate human rights reports into human rights scores. We first test for biases via a machine-learning approach using natural language processing and find substantial evidence of bias in human rights scores. We then present findings of an experiment on the coders of human rights reports to assess whether potential changes in the coding procedures or interpretation of coding rules affect scores over time. We find no evidence of coder bias and conclude that human rights measures have changed over time and that bias is introduced as part of monitoring and reporting.
Tele-operation and Simulation for a New Surgical Robot Design

AbstractFemur fracture is a frequently occurred injury and usually treated by surgeries. However, the treatment of femur fracture exhibits many problems such as excessive radiation exposure to both surgeons and patients and mal-alignment in reduction. In order to overcome these problems, robot-assisted reduction surgery has been researched in recent years. One hybrid robot is introduced in this paper for femur fracture reduction. In order to train the surgeon for pre-operative planning or for intra-operative manipulation, we developed a tele-operation and simulation system for the newly developed hybrid robot. In this system, we construct the virtual hybrid robot by V-Realm Builder 2.0 based on VRML language, use MATLAB Simulink and the virtual reality toolbox to build the simulation model, and tele-operation of the virtual hybrid robot is implemented using the Phantom haptic interface. The virtual system presented in this paper creates a realistic environment for demonstration and provides facilities for...
Student Perspectives on Digital Phenotyping

There is a mental health crisis facing universities internationally. A growing body of interdisciplinary research has successfully demonstrated that using sensor and interaction data from students’ smartphones can give insight into stress, depression, mood, suicide risk and more. The approach, which is sometimes termed Digital Phenotyping, has potential to transform how mental health and wellbeing can be monitored and understood. The approach could also transform how interventions are designed, delivered and evaluated. To date, little work has addressed the human and ethical side of digital phenotyping, including how students feel about being monitored. In this paper we report findings from in-depth focus groups, prototyping and interviews with students. We find they are positive about mental health technology, but also that there are multi-layered issues to address if digital phenotyping is to become acceptable. Using an acceptability framework, we set out the key design challenges that need to be addressed.
Early Palliative Care Integration in End-Stage Liver Disease: A Narrative Review of Clinical Strategies for Symptom Control and Quality of Life

End-stage liver disease (ESLD) is associated with a high symptom burden, poor prognosis, and limited access to curative disease. Despite growing evidence supporting the role of palliative care (PC), its integration into the routine management of ESLD remains limited and inconsistent. To synthesize the current evidence on the role of palliative care in ESLD, emphasizing its impact on quality of life and including strategies for symptom control and effective clinical integration. A narrative review with a systematic approach was conducted. PubMed, Scopus, Embase, and SciELO were searched for English- and Spanish-language studies published between 2015 and 2025. Studies were selected based on methodological rigor and relevance to PC interventions in ESLD. Key barriers to PC implementation include misconceptions about its use being limited to terminal phases, lack of referral criteria, and insufficient coordination between specialties. Evidence shows that early PC involvement improves symptom control (pain, dyspnea, pruritus, encephalopathy), decreases avoidable hospitalizations, and facilitates shared decision-making. Early and structured integration of palliative care into ESLD management is essential. Health systems should prioritize interdisciplinary care models, establish clear referral criteria, and promote a care approach focused on patient well-being, autonomy, and dignity.
A Dataset for Benchmarking Machine Learning Models for Autonomous Deep Vein Thrombosis Detection Based on Compression Ultrasound Videos

Deep Vein Thrombosis (DVT) is a serious vascular condition with a significant impact on morbidity, mortality, and the burden on healthcare systems. DVT affects approximately 400,000 - 900,000 people in Europe annually, with the associated total annual treatment costs reaching approximately €8.5 billion. Compression ultrasonography, performed and interpreted by specialist physicians, is the primary diagnostic method in clinical practice; however, examination and diagnosis typically occur only after the onset of clinical symptoms. The European research consortium ThrombUS+ is developing cutting-edge technology that enables continuous, real-time monitoring of individuals at high risk for DVT through an innovative wearable system that allows for autonomous compression ultrasonography. The real-time analysis of ultrasound scans is based on machine learning models, the training of which requires a large volume of clinical data. To this end, we are implementing a multicenter clinical study across five European hospitals to collect compression ultrasonography data from individuals with suspected DVT. In this paper, we present the first open-access dataset consisting of approximately 3,000 compression ultrasound videos from 742 patients with suspected DVT. Given that the dataset exhibits great clinical diversity, it can be used both for the development of Artificial Intelligence models and for the training of students and healthcare professionals in the diagnosis of DVT. This study is co-funded by the European Union under the Horizon Europe program.  and announced in: 10th Conference of Education and Research in Medicine, Alexandroupoli, Greece, 27-29 March 2026 [in Greek]  The work has been officilay published: In Proceedings of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: HEALTHINF, ISBN 978-989-758-802-0, ISSN 2184-4305, pages 853-862  Full Citation:  Didaskalou, S.; Portokallidis, N.; Tzatzimaki, K.; Liapi, M.; Moustakidis, N.; Moustakidis, T.; Balciuniene, N.; Macas, A.; Kijauskas, R.; Aladaitis, A.; Sotiriadou, A.; Sarafis, F.; Kynigopoulos, G.; Potoupnis, M.; Grandone, E.; Mastrangelo, G.; Maresca, S.; Gautier, M.; Chaiba, D.; Boussaha, H.; Goulvent, S.; Stylianou, C.; Oglou, E. N.; Chouchos, K.; Deftereos, S.; Papatheodorou, K.; Drougka, I.; Anagnostopoulou, P.; Yu, H. Q.; Kaldoudi, E. and ThrombUS+, (2026). A Dataset for Benchmarking Machine Learning Models for Autonomous Deep Vein Thrombosis Detection Based on Compression Ultrasound Videos.  In Proceedings of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies - https://doi.org/10.5220/0014741500004070
Winning in the Era of Serverless Computing and Function as a Service

Serverless Computing and Function as a Service (FaaS) is gaining traction in cloud-based application architectures used by startups and matured organizations alike. Organizations that are keen to leverage modern technology to gain a disruptive edge, optimal efficiency, advanced agility and save cost are adopting these architectural styles rapidly. Cloud service provider offer and dynamically manages the allocation of machine resources in serverless computing. The serverless architectures allows the developers to focus on business logic exclusively without worrying about preparing the runtime, managing deployment and infrastructure related concerns. FaaS may be assumed as a subset of Serverless Computing, in which, instead of coding a full-fledged cloud based application, the developer just writes (often small) functions which are piece of code (in one of the multiple programming languages supported by the platform) dedicated to do a focused, often single task that are invoked by triggers. It offers dynamic allocation and scaling of the resources and innovative trigger based costing model. This paper introduces Serverless Computing, and Function as a Service (FaaS), explores its advantages and limitations, options available with popular cloud and Platform as a Service (PaaS) providers, and emerging use cases and success stories.
Perancangan Data Warehouse Rumah Sakit Berbasis Online Analytical Processing (OLAP)

Pemanfaatan teknologi informasi sangat penting bagi rumah sakit, karena berpengaruh pula terhadap kualitas pelayanan kesehatan yang secara manual diubah menjadi digital dengan menggunakan teknologi informasi.Dalam penelitian ini penulis menggunakan metodologi Nine step sebagai acuan dalam merancang suatu data warehouse, untuk pemodelan menggunakan skema konstelasi fakta dengan 3 tabel fakta dan 11 tabel dimensi. Perbedaan penelitian ini dengan penelitian sebelumnya terletak pada sumber data yang diekstrak langsung dari database SIMRS yang digunakan rumah sakit, sehingga tidak ada ekstraksi data secara manual.Penelitian ini bertujuan untuk menghasilkan desain data warehouse berbasis Online Analytical Processing (OLAP) sebagai sarana penunjang kualitas pelayanan kesehatan rumah sakit. OLAP yang dihasilkan akan berupa desain data warehouse dengan berbagai dimensi yang akan menghasilkan tampilan informasi berupa Chart maupun Grafik sehingga informasinya mudah dibaca dan dipahami oleh berbagai pihak. AbtractThe use of information technology is very important for hospitals, because it also affects the quality of health services, which manualy changed to digital using information technology. In this study, the authors used the Nine step methodology as a reference in designing a data warehouse for modeling using a fact constellation schema with 3 fact tables and 11 dimension tables. the different in this study from previous research is that the data source was taken directly from the SIMRS database used by the hospital, so there is no manual data extraction.The aim of this research is to be able to produce a Data Warehouse design based on Online Analytical Processing (OLAP) as a means of supporting the quality of hospital health services. The resulting OLAP will be a data warehouse design with various dimensions will produce the displays information in the form of a graph or chart so that the information is easy to read and understand by various parties.
The emission regions in X-ray binaries: dipping as a diagnostic

X-ray dipping in the black hole binary Cygnus X-1, the Galactic jet source GRO J1655-40 and in low mass X-ray binaries is discussed. It is shown that spectral analysis strongly constrains emission models. Measurement of dip ingress/egress times allows the sizes of extended emission regions to be determined, notably for the Accretion Disk Corona which is responsible for Comptonization in X-ray binaries. In LMXB, the radius of the ADC is shown to be between ~ 10^9 and ~ 5x10^10 cm, an appreciable fraction of the accretion disk radius. This is inconsistent with Comptonization models requiring a localized Comptonizing region, for example, in the immediate neighbourhood of the neutron star. Results from a survey of LMXB using ASCA and BeppoSAX reveal an approximate equality between the height of the blackbody emission region on the neutron star and the height of the inner radiatively-supported disk, suggesting either that there is a direct causal link, such as a radial accretion flow between the inner disk edge and the star, or an indirect link, as in the case of accretion flow creep on the surface of the neutron star as suggested by Inogamov & Sunyaev. Finally, the survey shows that the blackbody cannot originate on the accretion disk as the required inner radii in many sources are substantially less than the neutron star radius.
The elucidation of enzymatic reaction mechanisms by computer simulation: Human immunodeficiency virus protease catalysis

Abstract   The first step in the catalytic mechanism of HIV protease is analyzed using combined quantum-mechanical/classical molecular dynamics simulations of the enzyme dimer with substrate in water. Several mechanistic questions can be answered from simulated atomic trajectories, which are in agreement with the available experimental data.
Perspectives on theory at the interface of physics and biology

Theoretical physics is the search for simple and universal mathematical descriptions of the natural world. In contrast, much of modern biology is an exploration of the complexity and diversity of life. For many, this contrast is prima facie evidence that theory, in the sense that physicists use the word, is impossible in a biological context. For others, this contrast serves to highlight a grand challenge. I'm an optimist, and believe (along with many colleagues) that the time is ripe for the emergence of a more unified theoretical physics of biological systems, building on successes in thinking about particular phenomena. In this essay I try to explain the reasons for my optimism, through a combination of historical and modern examples.