Reviewerly/exHarmony huggingface dataset:
Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature: Modeling Study

With the rapid expansion of biomedical literature, biomedical information extraction has attracted increasing attention from researchers. In particular, relation extraction between 2 entities is a long-term research topic.This study aimed to perform 2 multiclass relation extraction tasks of Biomedical Natural Language Processing Workshop 2019 Open Shared Tasks: relation extraction of Bacteria-Biotope (BB-rel) task and binary relation extraction of plant seed development (SeeDev-binary) task. In essence, these 2 tasks are aimed at extracting the relation between annotated entity pairs from biomedical texts, which is a challenging problem.Traditional research methods adopted feature- or kernel-based methods and achieved good performance. For these tasks, we propose a deep learning model based on a combination of several distributed features, such as domain-specific word embedding, part-of-speech embedding, entity-type embedding, distance embedding, and position embedding. The multi-head attention mechanism is used to extract the global semantic features of an entire sentence. Meanwhile, we introduced a dependency-type feature and the shortest dependency path connecting 2 candidate entities in the syntactic dependency graph to enrich the feature representation.Experiments show that our proposed model has excellent performance in biomedical relation extraction, achieving F1 scores of 65.56% and 38.04% on the test sets of the BB-rel and SeeDev-binary tasks. Especially in the SeeDev-binary task, the F1 score of our model is superior to that of other existing models and achieves state-of-the-art performance.We demonstrated that the multi-head attention mechanism can learn relevant syntactic and semantic features in different representation subspaces and different positions to extract comprehensive feature representation. Moreover, syntactic dependency features can improve the performance of the model by learning dependency relation between the entities in biomedical texts.
Behavior Trees for Smart Robots Practical Guidelines for Robot Software Development

Behavior Trees are a promising approach to model the autonomous behaviour of robots in dynamic environments. Behavior Trees represent action selection decisions as a tree of decision nodes. The hierarchy of these decision nodes provides the planning of actions of the robot including its reactions on exceptions. Behavior Trees enable flexible planning and replanning of robot behavior while supporting better maintainable decision-making than traditional Finite State Machines. This paper presents an overview of lessons, which we have learned when applying Behavior Trees to various autonomous robots. We present these lessons as a sequence of steps that is meant to support robot software practitioners to develop their systems.
Study on the Technology Trend Screening Framework Using Unsupervised Learning

Outliers that deviate from a normal distribution are typically removed during the analysis process. However, the patterns of outliers are recognized as important information in the outlier detection method. This study proposes a technology trend screening framework based on a machine learning algorithm using outliers. The proposed method is as follows: first, we split the dataset by time into training and testing sets for training the Doc2Vec model. Next, we pre-process the patent documents using the trained model. The final outlier documents are selected from the preprocessed document data, through voting for the outlier documents extracted using the IQR, the three-sigma rule, and the Isolation Forest algorithm. Finally, the technical topics of the outlier documents extracted through the topic model are identified. This study analyzes the patent data on drones to describe the proposed method. Results show that, despite cumulative research on drone-related hardware and system technology, there is a general lack of research regarding the autonomous flight field.
Distributed hyperledger technology in <scp>FinTech</scp> with artificial intelligence assisted internet of things platform

Abstract Artificial intelligence (AI) and distributed hyper ledger technology are both at the height of their hype cycles, which means they have the potential to be disruptive. This raises issues about new technologies' influence on future business structures, particularly in service‐based industries like finance. Even though numerous assumptions in reality point to the complementary use of these two technologies to produce new value creation potentials, there is little contemporary literature and study on the subject. Different perspectives on any of the two technologies mentioned above are insufficient for understanding potential synergies in financial services. Similarly, the internet of things (IoT) assistance for FinTech solutions has risen its significance in the upcoming transaction world. Therefore, this research proposes a distributed hyper ledger technology and AI for IoT‐based FinTech platform (DHLT‐AI‐FTP), which incorporates the benefits of each technology for secured and energy‐efficient financial transactions and communication. The AI technology and distributed hyper ledger technology handle the security part, and the communication part holds the efficient design of IoT implementation. The simulation evaluation of the proposed model ensures the improved security and efficiency of FinTech management and processing for real‐time business scenarios. The experimental analysis shows the significant performance of the proposed DHLT‐AI‐FTP with a better adaptability ratio of 98.56% compared to other existing approaches.
Improving sequence labeling with labeled clue sentences

Pre-trained language models (PLMs) have achieved noticeable success on a variety of natural language processing tasks, such as sequence labeling. In particular, the existing sequence labeling methods fine-tune PLMs on large-scale labeled data, which can avoid training the sequence labeling models from scratch. The fine-tuning process still requires large amounts of labeled training data so as to be effective. However, obtaining rich annotated data for sequence labeling is a time-consuming and expensive process, creating a substantial barrier for directly applying the PLMs trained on general-purpose large-scale text data to sequence labeling. In this paper, we investigate sequence labeling tasks from a novel perspective and propose a general framework that uses labeled clue sentences to mitigate the problem of insufficient annotation data for sequence labeling. Specifically, we first retrieve the labeled clue sentences for each original sentence in the training set based on the semantic (or syntactic) relevance. Here, the number of annotated clue sentences determines the expansion degree of the training set. Then, we modify the transformer’s self-attention mechanism to not only exploit the contextual information of the original sentence but also leverage the contextual and label information of the labeled clue sentences. In addition, we devise a mask label strategy to further avoid over-fitting by randomly masking out the labels of certain tokens in the clue sentence and then predicting these mask labels based on the context of the tokens corresponding to the mask labels. We verify the effectiveness and generalizability of the proposed framework on three sequence labeling tasks, including Chinese Named Entity Recognition, English Named Entity Recognition, and Aspect Term Extraction. Extensive experimental results show that our method can yield state-of-the-art or competitive results on the three tasks. • A general framework uses labeled clues to mitigate labeled data shortages. • Two retrieved ways of labeled clue sentences are designed. • Mask label strategy is devised to avoid over-fitting. • Transformer’s self-attention is modified to exploit original and clue sentences. • We verify effectiveness of the proposed framework on three sequence labeling tasks.
Information algebras in the theory of imprecise probabilities, an extension

In recent works, we have shown how to construct an information algebra of coherent sets of gambles, considering firstly a particular model to represent questions, called the multivariate model, and then generalizing it. Here we further extend the construction made to the highest level of generality, setting up an associated information algebra of coherent lower previsions, analyzing the connection of both the information algebras constructed with an instance of set algebras and, finally, establishing and inspecting a version of the marginal problem in this framework. Set algebras are particularly important information algebras since they are their prototypical structures. They also represent the algebraic counterparts of classical propositional logic. As a consequence, this paper details as well how propositional logic is naturally embedded into the theory of imprecise probabilities.
Application of Blockchain Technology in Environmental Health: Literature Review and Prospect of Visualization Based on CiteSpace

Blockchain technology and its applications have recently become a research hotspot. Its three core technologies, distributed ledger, smart contract, and consensus mechanism, provide trust-enhancing features such as tamper-proof records, full traceability, and data decentralization for a wide range of applications. This paper investigates the use of blockchain technology in environmental health. It investigates indicators such as the number of articles published, author collaboration network, research institution network, and keyword co-occurrence in this field between 2014 and 2021. It describes and analyzes the development and connotations of these indicators. Many scholars have conducted in-depth studies on blockchain in various areas. Still, there are few cross-over studies on environmental health and a lack of cross-over studies on technology application in multiple fields. The current study investigates the evolution of research on the application of blockchain technology in environmental health, as well as potential development patterns and research trends, to provide a theoretical foundation for the application and sustainability of blockchain technology in this field.
Knowledge base question answering via path matching

Knowledge base question answering (KBQA) refers to combining the information in the knowledge base to obtain an answer for an objective question. However, most of the existing methods tend to add a large number of hand-crafted features or constraints to improve the performance of the model. This makes the KBQA system more and more complex, but the improvement is limited. In this work, we try a novel method without any hand-crafted features or constraints, which transforms this problem into a text matching problem. In this method, a question and a series of edges in knowledge bases (KBs) are treated as two pieces of text that need to be matched. The network to match the two texts called Path Matching Model (PMM). On the WebQuestions benchmark, our method has 3% improvement compared to the state-of-the-art method.
Developing an AR Based Tool for Teaching Motion Analysis on Assembly Tasks

Engineering education courses look into processes, equipment and people in order to prepare students to tackle problems in their careers. In manufacturing industry, it is important to use virtual manufacturing tools to analyse processes. This includes both equipment and people. Assembly operations can involve human operators. Modular arrangements of predetermined time standards MODAPTS is a predetermined motion time system method used to analyse assembly processes. Augmented reality (AR) is increasingly being used for industrial processes as well as in education. AR application have been used for maintenance training as a useful way to overly digital instructions to a trainee whilst being able to look at a real object. This is potential beneficial for education. This paper presents the evaluation of a prototype to test a predetermined time standards model using an AR application. An evaluation was carried out comparing AR-based instructions in tablet and PC monitor and paper-written instructions. The results of evaluating the prototype encourage their use as an educational tool in engineering courses.
VR-Based Context Priming to Increase Student Engagement and Academic Performance

Research suggests that virtual environments can be designed to increase engagement and performance on many cognitive tasks. This paper compares the effect of specifically designed 3D environments intended to Prime these effects within Virtual Reality (VR). A 27-minute seminar “The Creative Process of Making an Animated Movie” was presented to 51 participants within three VR learning spaces: two Prime and one No Prime. The Prime conditions included two situated learning environments; an animation studio and a theatre with animation artifacts vs. the No Prime: theatre without artifacts. A 20-question multiple-choice content test, User Experience (UX), and affective (anxiety and positive affect) surveys were completed prior to and immediately after the learning session. Increased academic performance was observed in both Prime conditions compared to the control. UX and affective surveys related to the immersive VR experience were positive, but there were no significant differences observed between the Prime and No Prime conditions in either case.
Quality Control of Whole Slide Images using the YOLO Concept

Computational pathology applies computer vision algorithms on whole slide images. The digitization of tissue glass slides marks a significant change in the clinical diagnostic workflow. One of the challenges in digital pathology is the presence of artifacts such as tissue fold, air bubbles, and ink-markers on archived cases. These artifacts may affect the focus points in digital scanners, and their presence may negatively affect the quality of the output tissue image and the subsequent diagnosis. Manual review of whole slide images requires experts, and it is a laborious and time-consuming task. In this paper, we trained the YOLO-v4 (You-Only-Look-Once) model to detect air bubble edges, tissue folds, which can happen during slide preparation, and ink-marked tissue glass slides, which occur when pathologists highlight regions of interest on glass slides. Our method is not only fast but also highly accurate. The experiments showed 99.5 % IOU calculation (intersection over union, also called Jaccard Index) for locating artifacts.
Teachers’ Attitudes and Technology Acceptance Towards AR Apps for Teaching and Learning

Teacher attitudes and technology acceptance towards Augmented Reality (AR) are important constructs because their development has an impact on the teachers’ use of AR applications in classroom practice. The present study focusses on these two constructs and analyses them from a theoretical and empirical perspective. In this context, results from a pre-test from the European H2020 project ARETE are introduced. In this study, n=129 teachers from 13 countries responded to an online survey which included questions on demographic data as well as two scales to measure teacher attitudes towards AR and teacher technology acceptance towards AR. The results show a mostly highly motivated sample and very positive attitudes towards AR among the participating teachers. An analysis of correlations and predictors was performed by calculating the Pearson Correlation coefficient and a regression analysis, which indicated that teacher attitudes and technology acceptance towards AR are highly correlated. Additionally, the teachers’ previous experience with AR and, in case of technology acceptance, the self-assessed expertise in using digital media for teaching and learning are significant predictors for the two constructs in question.
Developing Natural Language Processing to Extract Complementary and Integrative Health Information from Electronic Health Record Data

Complementary and Integrative Health (CIH) has grown in popularity over the last few decades. The goal of this research project is to: 1) create a data model to represent information about both psychological and physical CIH approaches documented in unstructured clinical notes in the EHR; 2) develop an NLP extraction model to extract CIH information; and 3) use the extracted information to better understand the role of CIH in diseases. To develop the annotation guide, A total of 300 notes (100 notes per CIH approaches) were randomly selected and manually annotated. Annotations were made for status, symptom, and frequency of each approach. In 300 notes, 115 sentences pertaining to music therapy, 128 sentences pertaining to chiropractic and 136 sentences pertaining to aquatic exercise were identified. Status was documented the most among three entities. Our recent investigation discovered a wealth of CIH information in EHR clinical notes, as well as the ability to represent CIH data. The project's next stage will be to train NLP models using our manually annotated data to extract CIH information and investigate the role of CIH techniques in various diseases.
Machine Learning Models To Predict Length Of Stay In Hospitals

The goal of our study is to develop machine learning models to predict the hospital length of stay from open healthcare data. The length of stay is an important metric of the performance of healthcare systems. It can be used to drive cost efficiencies and allocate resources. We analyzed de-identified patient data from New York State SPARCS (statewide planning and research cooperative system), consisting of 2.3 million patient records. We investigated multiple model categories consisting of regression, decision trees, random forests, and XGBoost. The most important features we identified were the diagnostic related group and the severity of illness code. The best regression model was XGBoost, with an R2 value of 0.44.
Sharing Time-to-Event Data with Privacy Protection

Sharing time-to-event data is beneficial for enabling collaborative research efforts (e.g., survival studies), facilitating the design of effective interventions, and advancing patient care (e.g., early diagnosis). Despite numerous privacy solutions for sharing time-to-event data, recent research studies have shown that external information may become available (e.g., self-disclosure of study participation on social media) to an adversary, posing new privacy concerns. In this work, we formulate a cohort inference attack for time-to-event data sharing, in which an informed adversary aims at inferring the membership of a target individual in a specific cohort. Our study investigates the privacy risks associated with time-to-event data and evaluates the empirical privacy protection offered by popular privacy-protecting solutions (e.g., binning, differential privacy). Furthermore, we propose a novel approach to privately release individual level time-to-event data with high utility, while providing indistinguishability guarantees for the input value. Our method TE-Sanitizer is shown to provide effective mitigation against the inference attacks and high usefulness in survival analysis. The results and discussion provide domain experts with insights on the privacy and the usefulness of the studied methods.
An xAI Thick Data Assisted Caption Generation for Labeling Severity of Ulcerative Colitis Video Colonoscopy

Deep-learning convolutional neural networks (DCNNs) has made significant success in the area of medical image analysis and in particular in the area of colonoscopy. However, DCNNs are largely black-box predictors with no power to provide explanation for the underlying reasons of classification which is so important for evidence-based care practice. Providing thick data as additional heuristics in the form of generating relevant captions according to the features predicted by the machine learning can provide the explainable artificial intelligence (xAI) component to transfer those black boxes into more explainable components. This paper presented an approach that uses Siamese neural network for identifying the ulcerative colitis features from small training samples as well as to use an LSTM model to combine and embed relevant captions for providing that power of explainability to the Siamese classifier. Our modeling uses the Glove as an embedding model but did not use a copra that are dedicated for clinical practice. In our next research we are going to add this Glove based clinical copra to enhance our caption prediction accuracy.
A Federated Learning Paradigm for Heart Sound Classification

Cardiovascular diseases (CVDs) have been ranked as the leading cause for deaths. The early diagnosis of CVDs is a crucial task in the medical practice. A plethora of efforts were given to the automated auscultation of heart sound, which leverages the power of computer audition to develop a cheap, non-invasive method that can be used at any time and anywhere for measuring the status of the heart. Nevertheless, previous works ignore an important factor, namely, the privacy of the user data. On the one hand, learnt models are always hungry for bigger data. On the other hand, it can be difficult to protect personal private information when collecting such large amount of data. In this dilemma, we propose a federated learning (FL) framework for the heart sound classification task. To the best of our knowledge, this is the first time to introduce FL to this field. We conducted multiple experiments, analysed the impact of data distribution across collaborative institutions on model quality and learning patterns, and verified the feasibility and effectiveness of FL based on real data. Non- independent identically distributed (Non-IID) data and model quality can be effectively improved by adding a strategy of globally sharing data.
Dual Discriminator-Based Unsupervised Domain Adaptation Using Adversarial Learning for Liver Segmentation on Multiphase CT Images

Multiphase computed tomography (CT) images are widely used for the diagnosis of liver disease. Since each phase has different contrast enhancement (i.e., different domain), the multiphase CT images should be annotated for all phases to perform liver or tumor segmentation, which is a time-consuming and labor-expensive task. In this paper, we propose a dual discriminator-based unsupervised domain adaptation (DD-UDA) for liver segmentation on multiphase CT images without annotations. Our framework consists of three modules: a task-specific generator and two discriminators. We have performed domain adaptation at two levels: one is at the feature level, and the other is at the output level, to improve accuracy by reducing the difference in distributions between the source and target domains. Experimental results using public data (PV phase only) as the source domain and private multiphase CT data as the target domain show the effectiveness of our proposed DD-UDA method. Clinical relevance- This study helps to efficiently and accurately segment the liver on multiphase CT images, which is an important preprocessing step for diagnosis and surgical support. By using the proposed DD-UDA method, the segmentation accuracy has improved from 5%, 8%, and 6% respectively, for all phases of CT images with comparison to those without UDA.