Reviewerly/exHarmony huggingface dataset:
Introduction to this special issue: guiding the conversation: new theory and design perspectives for conversational user interfaces

ABSTRACTABSTRACTThe increased popularity of CUIs has motivated HCI work around specific approaches to research, design, and implementation, while also reflecting on these topics. However, current research is highly fragmented and lacks critical mass around topics such as theory, methods and design. Building this critical mass is a fundamentally multidisciplinary endeavour. CUIs involve language based interaction, either through speech or text, with another agent(s) or device(s). This type of interaction not only needs to engage with traditional HCI approaches, but also to embrace methods from communicative and social sciences. This is crucial for making progress towards human-centred conversational interfaces. Along with the recent ACM SIGCHI Conversational User Interfaces conference (ACM CUI), this special issue showcases research to further solidify the foundations of the field in these areas. Below we outline some key challenges faced by the field, describe the papers in this special issue, and then outline areas for future research.KEYWORDS: Conversational user interfacescommunicationmultimodal UIlinguistic UIHCI design theoryintelligent UI Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsBenjamin R. CowanBenjamin R. Cowan (benjamin.cowan@ucd.ie, https://people.ucd.ie/benjamin.cowan) is a human-computer interaction researcher with an interest in psycholinguistics and conversational user interfaces; he is an Associate Professor at the School of Information and Communication Studies at University College Dublin.Leigh ClarkLeigh Clark (leigh@boldinsight.co.uk, https://lmhclark.com) is a human-computer interaction researcher with an interest in conversational user interfaces, linguistics, and inclusivity; he is a Senior UX Researcher at Bold Insight UK.Heloisa CandelloHeloisa Candello (heloisacandello@br.ibm.com, http://ibm.biz/heloisacandello) is a human-computer interaction researcher with an interest in inclusive technologies, conversational user interfaces, and artificial intelligence systems; she is a Research Scientist in the Responsible and Inclusive Technologies group of the IBM Research Laboratory in Brazil.Janice TsaiJanice Tsai (harraton@gmail.com) is a privacy researcher with an interest in making privacy information usable, emerging technologies, and enabling innovation via privacy and ethics; she is a Staff Privacy Engineer at Google in the United States.
Detecting Breast Arterial Calcifications in Mammograms with Transfer Learning

Cardiovascular diseases, which include all heart and circulatory diseases, are among the major death-causing diseases in women. Cardiovascular diseases are not subject to screening programs, and early detection can reduce their mortal effect. Recent studies have shown a strong association between severe Breast Arterial Calcifications and cardiovascular diseases. The aim of this study is to use the screening programs for breast cancer to detect the high severity of BACs and therefore to obtain indirect information about coronary diseases. Previous attempts in the literature on the detection of BACs from digital mammograms still need improvements to be used as a standalone technique. In this study, a dataset of mammograms with BACs is divided into 4 grades of severity, and this study aims to improve their classification through a transfer learning approach to overcome the need for a large dataset of training. The performances achieved in this study by using pre-trained models to detect four Breast Arterial Calcifications severity grades reached an accuracy of 94% during testing. Therefore, it is possible to benefit from the advantage of Deep Learning models to define a rapid marker of BACs along Brest Cancer screening programs.
A Comparative Analysis of Solar Radiation in Korea’s Typical Meteorological Year for Building Energy Analysis

This research paper examined the variation in solar irradiance quantitatively over time by comparing solar irradiance data from domestic Typical Meteorological Year (TMY) records.Solar radiation can produce different TMY records depending on the year of data collection, even for the same region.Therefore, simulation users should take these factors into consideration and select appropriate TMY records based on their specific needs.The results showed that the recently produced TMY records had higher solar irradiance values than those produced in the past.The average annual deviation between different TMY records was 86.1 kWh/m 2 yr (6.5%).However, a comparison of TMY records from KIRE and SAREK, which have a significant difference in publication date, demonstrated that the deviation increased to an average of 204 kWh/m 2 yr (14.4%), with a maximum of kWh/m 2 yr (16.9%).The study also found that the difference in solar irradiance is related to the ratio of direct solar radiation when converting GHI to inclined plane irradiance.The annual cumulative solar irradiance showed significant variations on the southern inclined planes with a high ratio of direct solar radiation, while minimal changes were observed on northern inclined planes with a high ratio of diffusion solar radiation.
A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm

Possibilistic fuzzy c-means (PFCM) is one of the most widely used clustering algorithm that solves the noise sensitivity problem of Fuzzy c-means (FCM) and coincident clusters problem of possibilistic c-means (PCM). Though PFCM is a highly reliable clustering algorithm but the efficiency of the algorithm can be further improved by introducing the concept of suppression. Suppression-based algorithms employ the winner and non-winner based suppression technique on the datasets, helping in performing better classification of real-world datasets into clusters. In this paper, we propose a suppression-based possibilistic fuzzy c-means clustering algorithm (SPFCM) for the process of clustering. The paper explores the performance of the proposed methodology based on number of misclassifications for various real datasets and synthetic datasets and it is found to perform better than other clustering techniques in the sequel, i.e., normal as well as suppression-based algorithms. The SPFCM is found to perform more efficiently and converges faster as compared to other clustering techniques.
A hybrid algorithm for clinical decision support in precision medicine based on machine learning

The objective of the manuscript is to propose a hybrid algorithm combining the improved BM25 algorithm, k-means clustering, and BioBert model to better determine biomedical articles utilizing the PubMed database so, the number of retrieved biomedical articles whose content contains much similar information regarding a query of a specific disease could grow larger.In the paper, a two-stage information retrieval method is proposed to conduct an improved Text-Rank algorithm. The first stage consists of employing the improved BM25 algorithm to assign scores to biomedical articles in the database and identify the 1000 publications with the highest scores. The second stage is composed of employing a method called a cluster-based abstract extraction to reduce the number of article abstracts to match the input constraints of the BioBert model, and then the BioBert-based document similarity matching method is utilized to obtain the most similar search outcomes between the document and the retrieved morphemes. To realize reproducibility, the written code is made available on https://github.com/zzc1991/TREC_Precision_Medicine_Track .The experimental study is conducted based on the data sets of TREC2017 and TREC2018 to train the proposed model and the data of TREC2019 is used as a validation set confirming the effectiveness and practicability of the proposed algorithm that would be implemented for clinical decision support in precision medicine with a generalizability feature.This research integrates multiple machine learning and text processing methods to devise a hybrid method applicable to domains of specific medical literature retrieval. The proposed algorithm provides a 3% increase of P@10 than that of the state-of-the-art algorithm in TREC 2019.
End-to-End Encryption for Securing Communications in Industry 4.0

In Industry 4.0 (I4.0), reliable data sharing between multiple entities is profoundly significant for both the business-level and the manufacturing operations-level collaboration. Eventually, machine-to-machine (M2M) communication technology can be a key underlying technology for I4.0, where devices (e.g., sensors, actuators, and gateways) can exchange information with each other autonomously, ensuring data confidentiality and integrity. In this paper, we propose a transparent message level end-to-end encryption layer for Message Queue Telemetry Transport (MQTT). We exploit Ciphertext-Policy Attribute-based encryption (CP-ABE) that is implemented using the Fast Attribute Message Encryption (FAME) CP-ABE scheme. We also evaluate the time and space performance of the FAME CP-ABE scheme.
Design of Traditional Cultural Experiences Using Augmented Reality Based on Environmental Presence

Traditional cultural experiences are valuable for tourist destinations. Previous studies have developed systems for experiencing traditional culture using augmented reality (AR). However, few works have considered the effectiveness of different types of AR experiences of traditional cultural elements. In this article, we focus on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">equipment elements</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">environment elements</i> , and propose design recommendations for an AR traditional cultural experience system that considers differences in the environmental presence of traditional cultural experiences. We conducted a comparative study with 49 users to understand the impact of these differences. We compared three options for handling environmental presence, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonpresence</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AR-presence</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">real-presence</i> . The results show that presenting environmental elements in a state of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">real-presence</i> improved an AR experience of traditional culture. Additionally, we found that this setting worked positively for Japanese participants, whereas Chinese participants preferred environmental elements in the state of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonpresence</i> .
A Novel Monitoring System for Contagious Diseases of Patients using a Parallel Planar Robot

A contagious disease can be transmitted very quickly from one person to another through direct contact or indirect contact. Today there are many doctors who become infected while monitoring their patients, this causes them to cease their work and even death. Therefore, this project has the objective of designing a robotic concept that allows remote monitoring of patients with contagious diseases, reducing direct contact between the doctor and the patient. For the design of this robot, the V methodology is proposed, which breakdown a complex prototype into a series of systems and subsystems. It is expected to obtain a robot with free obstacle locomotion applying the similarity law, thermal monitoring based on artificial vision, and facial recognition using Haar cascade. The concept was tested in solidworks motion. Finally, the proposed concept is simulated in ABS for the Housing with the capacity to withstand temperatures of up to 80 degrees Celsius and an aluminium structure with a security factor of 8.17.
Lightweight CNN based on Non-rigid SFM for 3D Reconstruction of Medical Images

We design a lightweight CNN, a practical and inexpensive scheme to reconstruct the geometry of the medical target from a 2D medical sequence. We make full use of natural images with rich texture features to compensate for the low-quality problem of fewer texture conditions in medical images. Besides, we design a Non-rigid Structure from Motion(NRSfM) scheme to estimate the camera structure and pose of consecutive frames with the view synthesis as the supervisory signal. Specifically, for achieving 3D reconstruction with a better time performance, we compress the CNN and medical image 3D prediction can be achieved in the lightweight CNN with the model size of 18M. The experiment results from LITFL of the open clinical library, as well as the Synapse multi-organ segmentation dataset, show that the proposed method can reconstruct the accurate geometry with better time performance.
Predicting The Likelihood of Patients Developing Sepsis Based on Compound Ensemble Learning

Objective: Based on a dataset with few features predicts a patient’s likelihood of developing sepsis in the future. Method: We propose a compound ensemble learning framework. At the first stage, UMAP and XgBoost are explored for dimensionality reduction, meanwhile, weak classifiers such as KELM, 1D Convolutional Neural Networks, and XgBoost are employed as base learners to learn patient characteristics from four different emphases. The output of base learners is processed using the proposed Res-Stacking and modified Bagging-like methods in the first stage of compound ensemble learning framework to reduce the error rate. In the secondary ensemble learning stage, the D-S evidence theory is employed to fuse the results of the above two methods and BiLSTM, thereby diminishing the uncertainty of the final output and obtaining more prominent prediction result. Result: The performance of our proposed compound ensemble framework on the clinical dataset of the First Affiliated Hospital of Dalian Medical University in terms of sensitivity, specificity, diagnostic efficiency, PPV, NPV, F1 score and AUC are 0.875, 0.95, 0.928, 0.95, 0.875, 0.95, 0.9125, respectively, better than the single ensemble learning method.
Multi-scale Label Attention Network based on Abductive Causal Graph for Disease Diagnosis

The auxiliary disease diagnosis based on electronic medical records is of great significance, providing doctors with diagnostic advice and avoiding misdiagnosis. Existing work on disease diagnosis mainly utilizes deep learning models to extract sequence information in electronic medical records, ignoring the interpretability of results and the structural knowledge, especially causal knowledge. In our work, we propose a multiscale label attention network based on abductive causal graph (MSLAN-ACG) to improve model accuracy and interpretability of results. First, we construct multiple encoders in the multiscale label attention network, which can extract n-gram segment information of different lengths for each disease. Meanwhile, to enhance the interpretability of results, we visualize the weight score of different segments for disease results. Second, we propose a disease representation method by defining an abductive causal graph and then using graph convolutional network for knowledge fusion on this graph. The information propagation based on abductive causal graph is consistent with the actual abductive reasoning process from symptoms to diseases, making the model more reasonable. The effectiveness of our model is demonstrated by achieving state-of-the-art results on MIMICIII-50 and ChineseEMR datasets.
A Fast Texture-to-Stain Adversarial Stain Normalization Network for Histopathological Images

Histopathological images, as the gold standard for cancer diagnosis, record abundant information about microscopic structures and morphological characteristics through staining and scanning. However, the stain variation caused by the deviations in the production process not only confuse the pathological diagnosis, but also depress the generalization of computer-aided diagnosis. Stain normalization provides an efficient preprocessing method, which generates images with the same stain style while preserving the texture information. In this paper, we extend the advantages of adversarial transfer learning and auto-encoder structure to propose an adversarial stain normalization network (ASNN). For the stain style alignment, ASNN liberalizes the selection for the specific template image and adopts a domain discriminator after encoding to eliminate the distribution discrepancies of latent features caused by stain variation. To ensure precisely stain style transfer, we reconstruct the target stain style image using a reconstruction loss-constrained autoencoder and by the loss of reconstruction so that the stain decoder learns the rules for texture-to-stain conversions. We simulate the experiments on two publicly available datasets and utilize multiple indicators to evaluate the qualities of generated images. By comparing classification performance on downstream tasks, ASNN exhibits its superiority than traditional stain normalization methods. Furthermore, ASNN adopts a lighter network structure, which is faster than traditional stain normalization methods, and can stain 500 images within 1 second.
DBT-UNETR: Double Branch Transformer with Cross Fusion for 3D Medical Image Segmentation

Medical image segmentation is significant for diagnosis and prognosis. Inspired by the success of Transformer for Natural Language Processing, this paper presents DBT-UNETR, a novel double branch Transformer architecture with cross fusion for multi-scale feature representation, which enhances the model performance of 3D medical image segmentation. The DBT-UNETR consists of a single scale Transformer and a multiple scale Transformer to capture informative feature. These two branch features are cross fused and then they are combined together. Finally, these feature representations are sent to decoders via skip connection for final segmentation. Moreover, in order to further improve the model performance, an Early Fusion module is designed. Comprehensive experiments are conducted on public available dataset, Multi-Atlas Labeling Beyond The Cranial Vault (BTCV), and it demonstrates the proposed model outperforms the comparative baseline methods.
Collaborative Transformer-CNN Learning for Semi-supervised Medical Image Segmentation

Convolutional Neural Networks (CNNs) and Transformers have recently demonstrated promising performance in a multitude of supervised medical image segmentation tasks. However, the success of these methods heavily depends on massive annotated data for training, which is time-consuming and laborious to acquire, especially for pixel-wise annotation that requires medical expertise and clinical experience. To address this issue, we present a novel framework called Collaborative Transformer-CNN Learning (CTCL) for semi-supervised medical image segmentation. Specifically, Our CTCL combines cross teaching with hard pseudo labels (CTH) and mutual learning with soft pseudo labels (MLS) to simultaneously train the Transformer-CNN models, thus making full use of the unlabeled data for the supervision of the segmentation task while reducing the uncertainty and noises of pseudo labels. Furthermore, considering the large structural differences between the Transformer and CNN models, we utilize consistency regularization (CR) to make the predictions consistent and determined via minimizing classifier determinacy discrepancy. Extensive experiments on a popular medical image benchmark demonstrate that our method yields superior results compared with existing state-of-the-art semi-supervised learning methods.
Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19.Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events.In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C) 1 to find the risk factors contributing to developing Long COVID.Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48%.We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score.The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient.We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis.
Optimization of Biomedical Language Model with Optuna and a Sentencepiece Tokenization for NER

Raw medical documents come with in-domain challenges that need to be addressed with appropriate methods, such as a suitable tokenizer and tailored model via hyperparameters. Most available pre-trained models have been trained on clean biomedical data from published documents without considering the characteristics of raw medical texts. This approach drastically limits the performance of those models on real-world data such as electronic health record documents. This work introduces a fine-grained language model trained over a SentencePiece tokenizer with various biomedical and clinical data. Through this RoBERTa-based model, referred to as BioBERTa, we demonstrated the importance of data-dependant optimization by evaluating our model on several Named Entity Recognition tasks. Our approach showed state-of-the-art results on NCBI-disease and BC5-disease datasets. We demonstrated the effectiveness of an in-domain tokenizer where ours improved the embedding length by 15.1% of its original model. Our approach also shows that fine-tuning the hyperparameters can significantly improve the model accuracy(up to 2% F1 score).
Generative Adversarial Neural Networks based Oversampling Technique for Imbalanced Credit Card Dataset

The imbalanced dataset is a challenging issue in many classification tasks. Because it leads a machine learning algorithm to poor generalization and performance. The imbalanced dataset is characterized as having a huge difference between the number of samples that contain each class. Unfortunately, various resampling methods are proposed to solve this problem. In our work, we target enhancing the handling of the imbalanced dataset using a new oversampling technique based on generative adversarial neural networks. Our method is benchmarked against the widely used oversampling technique including the synthetic minority oversampling technique (SMOTE), random oversampling technique (ROS), and the adaptive synthetic sampling approach(ADSYN). Additionally, three machine learning algorithms are used for evaluation. The outcome of our experiments on a real-world credit card dataset shows the strong ability of the proposed solution against the competitive oversampling techniques to overcome the imbalanced problem in the European credit card dataset.