Reviewerly/exHarmony huggingface dataset:
An Efficient Capsule-based Network for 2D Left Ventricle Segmentation in Echocardiography Images

The segmentation of cardiac chambers is essential for the clinical diagnosis and treatment of cardiovascular diseases. It is demonstrated that in cardiac disease, the left ventricle (LV) is extensively involved. Therefore, segmentation of the LV in echocardiographic images is critical for the precise evaluation of factors that influence cardiac function such as LV volume, ejection fraction, and LV mass. Although these measurements could be obtained by manual segmentation of the LV, it would be time-consuming and inaccurate because of the poor quality and low contrast of these images. Convolutional neural networks, commonly referred to as CNNs, have emerged as a highly favored deep learning technique for medical image segmentation. Despite their popularity, the pooling layers in CNNs ignore the spatial information and do not consider the part-whole hierarchy relationships. Furthermore, they require a large training dataset and a large number of parameters. Therefore, Capsule Networks are proposed to address the CNNs limitations. In this study, for the first time, an optimized capsule-based network for object segmentation called SegCaps is proposed to achieve accurate LV segmentation on echocardiography images applied to the CAMUS dataset. The result was compared against the standard 2D-UNet. The modified SegCaps and 2D-UNet achieved an average Dice similarity coefficient (DSC) of 84.48% and 83.28% on LV segmentation, respectively. The capabilities of the CapsNet led to an improvement of 1.44% in DSC with 92.77% fewer parameters than the U-Net. The results indicate that the proposed method leads to accurate and efficient LV segmentation.Clinical Relevance— From a clinical point of view, our findings lead to more precise evaluations of critical cardiac parameters, including ejection fraction as well as left ventricle volume at end-diastole and end-systole.
GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer’s Disease Severity

We introduce an explainable deep neural architecture that combines brain structure with genetic influence to improve disease severity prediction in Alzheimer’s disease. Our framework consists of an encoder, a decoder, and a rank-consistent ordinal regression module. The encoder projects neural imaging and genetics data into a low-dimensional latent space regularized by the decoder. The ordinal regression module guides the feature embedding process to find discriminative patterns representative of disease severity. We also add a learnable dropout layer that learns feature importance and extracts explainable biomarkers from the data. We evaluate our model using structural MRI (sMRI) and Single Nucleotide Polymorphism (SNP) data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. In 2-class severity classification comparison, our model has a median F-score of 0.86 (baseline median F-score range: 0.57-0.81). In 3-class classification comparison, our model’s median F-score is 0.50 (baseline range: 0.17 - 0.41). In 4-class classification comparison, our model’s median F-score is 0.40 (baseline range: 0.14 - 0.39). We demonstrate that our model provides improved disease diagnosis alongside sparse and clinically relevant biomarkers.Clinical relevance—This study provides a deep-learning model that can predict Alzheimer’s disease severity levels while identifying consistent and clinically relevant biomarkers.
IoT Federated Blockchain Learning at the Edge

IoT devices are sorely underutilized in the medical field, especially within machine learning for medicine, yet they offer unrivaled benefits. IoT devices are low cost, energy efficient, small and intelligent devices [1].In this paper, we propose a distributed federated learning framework for IoT devices, more specifically for IoMT (In-ternet of Medical Things), using blockchain to allow for a decentralized scheme improving privacy and efficiency over a centralized system; this allows us to move from the cloud based architectures, that are prevalent, to the edge.The system is designed for three paradigms: 1) Training neural networks on IoT devices to allow for collaborative training of a shared model whilst decoupling the learning from the dataset [2] to ensure privacy [3]. Training is performed in an online manner simultaneously amongst all participants, allowing for training of actual data that may not have been present in a dataset collected in the traditional way and dynamically adapt the system whilst it is being trained. 2) Training of an IoMT system in a fully private manner such as to mitigate the issue with confidentiality of medical data and to build robust, and potentially bespoke [4], models where not much, if any, data exists. 3) Distribution of the actual network training, something federated learning itself does not do, to allow hospitals, for example, to utilize their spare computing resources to train network models.
Named Entity Recognition and Normalization for Alzheimer’s Disease Eligibility Criteria

Alzheimer's Disease (AD) is a complex neurodegenerative disorder that affects millions of people worldwide. Finding effective treatments for this disease is crucial. Clinical trials play an essential role in developing and testing new treatments for AD. However, identifying eligible participants can be challenging, time-consuming, and costly. In recent years, the development of natural language processing (NLP) techniques, specifically named entity recognition (NER) and named entity normalization (NEN), have helped to automate the identification and extraction of relevant information from the eligibility criteria (EC) more efficiently, in order to facilitate semi-automatic patient recruitment and enable data FAIRness for clinical trial data. Nevertheless, most current biomedical NER models only provide annotations for a restricted set of entity types that may not be applicable to the clinical trial data. Additionally, accurately performing NEN on entities that are negated using a negative prefix currently lacks established techniques. In this paper, we introduce a pipeline designed for information extraction from AD clinical trial EC, which involves preprocessing of the EC data, clinical NER, and biomedical NEN to Unified Medical Language System (UMLS). Our NER model can identify named entities in seven pre-defined categories, while our NEN model employs a combination of exact match and partial match search strategies, as well as customized rules to accurately normalize entities with negative prefixes. To evaluate the performance of our pipeline, we measured the precision, recall, and F1 score for the NER component, and we manually reviewed the top five mapping results produced by the NEN component. Our evaluation of the pipeline's performance revealed that it can successfully normalize named entities in clinical trial ECs with optimal accuracies. The NER component achieved a overall F1 of 0.816, demonstrating its ability to accurately identify seven types of named entities in clinical text. The NEN component of the pipeline also demonstrated impressive performance, with customized rules and a combination of exact and partial match strategies leading to an accuracy of 0.940 for normalized entities.
Wait, wasn’t that code here before? Detecting Outdated Software Documentation

Encountering outdated documentation is not a rare occurrence for developers and users in the software engineering community. To ensure that software documentation is up-to-date, developers often have to manually check whether the documentation needs to be updated whenever changes are made to the source code. In our previous work, we proposed an approach to automatically detect outdated code element references in software repositories and found that more than a quarter of the 1000 most popular projects on GitHub contained at least one outdated reference. In this paper, we present a GitHub Actions tool that builds on our previous work’s approach that GitHub developers can configure to automatically scan for outdated code element references in their GitHub project’s documentation whenever a pull request is submitted.
Dimensions and Precedential Constraint: Factors Deriving from Multiple Dimensions

Current theories of precedential constraint attempt to incorporate dimensions into the reasons for decisions. We argue that this is an unnecessary complication, and precedential constraint can be handled using only factors. In our account the role of dimensions is to organise facts, and their effect operates at the factor ascription level, prior to precedential constraint being applied.
"Research Trends of Academic Discourse in Korean Language Education: A Systematic Literature Review (2005-2022)"

The primary objective of this study is to analyze scholarly articles and research materials in the domain of academic discourse within Korean Language Education. This aims to identify current trends and suggest avenues for future research. A systematic review was executed on a curated corpus of 153 academic journal articles and doctoral dissertations, rigorously assessed for research period, subjects, methodologies, and content. The analysis shows that the field has yielded a diverse and significant body of research in a relatively short time span, initially focusing on panlinguistic or usage-based features but gradually diversifying in both topics and methods. However, the study also reveals imbalances in research focus, methodology, and content, thereby questioning the corpus’s representativeness and objectivity. Based on these findings, targeted recommendations for future research are outlined. The significance of this work lies in its comprehensive analysis of existing trends, serving as a guide for future endeavors in academic discourse within Korean Language Education.(Seoul National University)
Development of Web-Based Health Information Service Systems

Background: Efforts to build services that can provide information quickly and accurately are significant for an agency. The use of the internet in finding health services is a place that can provide information that is not only fast but also provides the right solution. Therefore, a health service website was created containing information about health services, by utilizing the development of information technology, which can carry out health services through a website that can be accessed at any time (Real Time) by the public anywhere and anytime. Laravel is a Hypertext Preprocessor (PHP) programming language framework created by Taylor Otwell that has many modern features that help developers create applications. This study aims to design a technology-based health information service system with a web platform to increase public health awareness and facilitate access to information about health services.&#x0D; Method: The method used is a linear system development method, namely the waterfall method where each stage of development is carried out sequentially, starting from the stages of analysis, design, implementation, testing, and maintenance.&#x0D; Result: The results showed that web-based health information service systems can improve the efficiency and effectiveness of health services, obtain accurate and up-to-date information about health services, and provide easy access to health information for the public.&#x0D; Conclusion: Based on the results of research that has been conducted, it can be concluded that the development of web-based health information service systems has the potential to improve the quality of health services. This system can help health facilities improve the efficiency and effectiveness of health services, by reducing manual processes and improving collaboration between health workers, increasing the accuracy and completeness of health data, reducing data input errors and increasing data accessibility, increasing the security and confidentiality of health data, and increasing patient satisfaction specifically and the public in general, by providing faster services and easy.
Effectiveness of Animated Video Media in Enhancing Oral Health Knowledge Among Children with Intellectual Disabilities: A Quasi-Experimental Study

Background: Children with intellectual disabilities often face lower oral healthcare knowledge levels than their typically developing peers. This disparity arises from intellectual limitations and adaptive functioning challenges. Effective educational tools are crucial to improving the oral health knowledge of children with intellectual disabilities, and one such tool is animated video media.&#x0D; Method: This study employed a quasi-experimental design, with a purposive sample of 32 respondents. The independent variable was the delivery of oral health education through animated video media, while the dependent variable was the knowledge of oral health maintenance. Data were collected using a questionnaire. Statistical analysis involved the Wilcoxon signed rank test and the Mann-Whitney test.&#x0D; Result: Prior to the educational intervention using animated video media, the average knowledge score regarding oral health maintenance was 4.06. Following the intervention, the average score significantly increased to 9.62. Additionally, the distribution of the difference between the treatment and control groups in terms of oral health knowledge showed a mean difference of 24.25 compared to 8.75. Statistical analysis revealed a significant difference between the treatment and control groups (P=0.000).&#x0D; Conclusion: This study demonstrates the effectiveness of animated video media in significantly improving oral health knowledge among children with intellectual disabilities. The findings underscore the potential of multimedia educational tools to bridge knowledge gaps and enhance oral health outcomes in this vulnerable population.
Answer Set Programming for Legal Decision Support and Explanation

The ANGELIC methodology was successfully used to predict decisions of the European Court of Human Rights based on a set of logical rules, with significantly better accuracy than the one achieved by machine learning approaches, as well as to explain the results of reasoning, quite valuable in order to make them trustworthy. This work demonstrates a different logic-based approach, based on Answer Set Programming for solving and generating explanations for solutions. The use of a general knowledge representation and reasoning system, where representation and inference are not tightly coupled, allows for using the same representation for inference tasks different from prediction, thus getting more value out of the domain model, and opens for integrating further forms of knowledge.
Language Agnostic Ontology Extension Framework

Ontologies are powerful structures used to define the schema and organization of knowledge. They provide a framework for precisely and explicitly organizing concepts, entities, attributes, and relationships within a domain, enabling effective data management, knowledge sharing, and intelligent decisionmaking. Knowledge graphs, on the other hand, store data in a graphical form, facilitating semantically rich and interconnected data analysis, management, and exploration. Nodes represent entities, edges depict relationships between nodes, and attributes showcase node properties. Combining the strengths of ontology, which offers schema and concept-level modeling, with the data richness of knowledge graphs, one can unlock powerful reasoning and inference capabilities that closely resemble facts and truths. The symbiotic relationship between knowledge graphs and ontology, wherein ontology serves as a blueprint for representing knowledge in the graphs, creates a robust foundation for knowledge representation. Traditionally, ontologies have been created by experts or skilled teams with domain knowledge, aiming to build comprehensive ontologies supporting holistic data representation. However, these domain-specific ontologies require manual intervention for updates and remain language-specific, making it challenging to transfer them across different language formats. To address this challenge, a crucial component is an automatic generation framework for language-independent ontologies. Such a capability would provide a data-driven approach for creating necessary ontologies. The process involves taking a text corpus as input and subsequently constructing a knowledge graph through named entity recognition. This graph is then transformed into a concept-level modeling graph, where an ID-based approach is utilized to represent the concepts. The ID-based concepts can be extended to facilitate merging and updating the ontology with new relevant information. Concurrently, this approach can also be expanded to achieve language independence, enabling the conversion of the ontology to any required language. Expressing these ontologies in mathematical terms is essential to achieve language-agnosticism, ensuring completeness, relevance, and independence from specific domains, thereby making them applicable across various linguistic contexts.
Immunohistochemistry Biomarkers-Guided Image Search for Histopathology

Medical practitioners use a number of diagnostic tests to make a reliable diagnosis. Traditionally, Haematoxylin and Eosin (H&E) stained glass slides have been used for cancer diagnosis and tumor detection. However, recently a variety of immunohistochemistry (IHC) stained slides can be requested by pathologists to examine and confirm diagnoses for determining the subtype of a tumor when this is difficult using H&E slides only. Deep learning (DL) has received a lot of interest recently for image search engines to extract features from tissue regions, which may or may not be the target region for diagnosis. This approach generally fails to capture high-level patterns corresponding to the malignant or abnormal content of histopathology images. In this work, we are proposing a targeted image search approach, inspired by the pathologists' workflow, which may use information from multiple IHC biomarker images when available. These IHC images could be aligned, filtered, and merged together to generate a composite biomarker image (CBI) that could eventually be used to generate an attention map to guide the search engine for localized search. In our experiments, we observed that an IHC-guided image search engine can retrieve relevant data more accurately than a conventional (i.e., H&E-only) search engine without IHC guidance. Moreover, such engines are also able to accurately conclude the subtypes through majority votes.
Fall Risk Prediction in Older Adults Using Free-Text Nursing Notes and Medications in Electronic Health Records

Nursing notes in Electronic Health Records (EHR) contain critical health information, including fall risk factors. However, an exploration of fall risk prediction using nursing notes is not well examined. In this study, we explored deep learning architectures to predict fall risk in older adults using text in nursing notes and medications in the EHR. EHR predictor data and fall events outcome data were obtained from 162 older adults living at TigerPlace, a senior living facility located in Columbia, MO. We used pre-trained BioWordVec embeddings to represent the words in the clinical notes and medications and trained multiple recurrent neural network-based natural language processing models to predict future fall events. Our final model predicted falls with an accuracy of 0.81, a sensitivity of 0.75, a specificity of 0.83, and an F1 score of 0.82. This preliminary exploratory analysis provides supporting evidence that fall risk can be predicted from clinical notes and medications. Future studies will utilize additional data modalities available in the EHR to potentially improve fall risk prediction from EHR data.
Pluralising critical technical practice

In this special issue, we turn to ideas of and approaches to critical technical practices (CTPs) as entry points to doing critique and doing things critically in digitally mediated cultures and societies. We explore the pluralisation of ‘critical technical practice’, starting from its early formulations in the context of AI research and development (Agre, 1997a, 1997b) to the many ways in which it has resonated and been taken up by different publications, projects, groups, and communities of practice, and what it has come to mean. Agre defined CTP as a situational, practical, and constructive way of working: ‘a technical practice for which critical reflection upon the practice is part of the practice itself’ (1997a: XII). Communities of practice in which the notion has been adopted, adapted, and put to use range from human–computer interaction (HCI) to media art and pedagogy, from science and technology studies (STS) and computer-supported cooperative work (CSCW) to digital humanities, media studies and data studies. This special issue affirms the pluralisation of CTP, and serves as an invitation to (re)consider what it means to use this notion drawing on a wider body of work, including beyond Agre. In this introduction, we review and discuss CTPs according to (1) Agre, (2) indexed research, and (3) contributors to this special issue. We conclude with some questions and considerations for those interested in working with this notion.
SMS Spam Detection using Machine Learning

SMS spam detection using Naive Bayes algorithm is a widely used technique in the field of text classification. The main aim of this approach is to classify the incoming messages into spam or ham categories. The Naive Bayes algorithm works by calculating the probability of a message belonging to a particular class, based on the occurrence of different words in the message. In this paper, we present an efficient and accurate approach for SMS spam detection using the Naive Bayes algorithm. The proposed approach utilizes a pre- processing step for feature extraction, which includes tokenization, stop-word removal, and stemming. The Naive Bayes algorithm is then trained on a dataset of labeled messages to learn the probability distributions of different words in spam and ham messages. Finally, the trained model is used to classify incoming messages into spam or ham categories. The results of our experiments show that the proposed approach achieves high accuracy in detecting SMS spam messages. Keywords: Naive Bayes Algorithm, Text Classification, Probability, Feature Extraction, Tokenization, StopWord Removal, Stemming, Labeled Dataset, Probability Distribution, Accuracy
Safety Verification of Neural-Network-Based Controllers: A Set Invariance Approach

This paper presents a novel approach to ensure the safety of continuous-time linear dynamical systems controlled by a neural network (NN) based state-feedback. Our method capitalizes on the use of continuous piece-wise affine (PWA) activation functions (e.g. ReLU) which render the NN a PWA continuous function. By computing the affine regions of the latter and applying Nagumo's theorem, a subset of boundary points can effectively verify the invariance of a potentially non-convex set. Consequently, an algorithm that partitions the state space in affine regions is proposed. The scalability of our approach is thoroughly analyzed, and extensive tests are conducted to validate its effectiveness.