Reviewerly/exHarmony huggingface dataset:
Axiomatic framework of entropy measure for type-2 fuzzy sets with new representation method and its application to product ranking through online reviews

Type-2 fuzzy sets (T2FSs) have the advantage of representing higher-order uncertainty, they have demonstrated superior performance in several applications. In order to fully measure the uncertainty for T2FSs. In this study, several distribution characteristics of uncertainties within T2FSs are analyzed through type-1 fuzzy sets (T1FSs), and the axiomatic framework of fuzzy entropy and overall entropy of T2FSs is established as well. First, a new representation method for T2FSs is given. On this basis, the axiomatic definitions of fuzzy entropy for T1FSs and T2FSs are constructed by analyzing the distribution characteristics of fuzziness, and the construction theorems and basic properties of fuzzy entropy are studied. Further, the overall entropy of T2FSs is given, on this basis, a novel type-2 fuzzy entropy-based decision-making method is proposed, and applied to the selection of cold medicine through online reviews on AliHealth.cn. Finally, the feasibility and effectiveness of the developed definitions and methods are demonstrated by three comparative analyses.
Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records

Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) techniques and the availability of a large amount of healthcare data allow us to investigate T2DM characteristics and evolution with a completely new approach, studying common disease trajectories rather than cross sectional values. We used an Kernelized-AutoEncoder algorithm to map 5 years of data of 11,028 subjects diagnosed with T2DM in a latent space that embedded similarities and differences between patients in terms of the evolution of the disease. Once we obtained the latent space, we used classical clustering algorithms to create longitudinal clusters representing different evolutions of the diabetic disease. Our unsupervised DL clustering algorithm suggested seven different longitudinal clusters. Different mean ages were observed among the clusters (ranging from 65.3±11.6 to 72.8±9.4). Subjects in clusters B (Hypercholesteraemic) and E (Hypertensive) had shorter diabetes duration (9.2±3.9 and 9.5±3.9 years respectively). Subjects in Cluster G (Metabolic) had the poorest glycaemic control (mean glycated hemoglobin 7.99±1.42%), while cluster E had the best one (mean glycated hemoglobin 7.04±1.11%). Obesity was observed mainly in clusters A (Neuropathic), C (Multiple Complications), F (Retinopathy) and G. A dashboard is available at dm2.b2slab.upc.edu to visualize the different trajectories corresponding to the 7 clusters.
Smart Insurance Contracts Shielding Pandemic Business Disruption in Developing Countries and Blockchain Solution

As the Fourth Industrial Revolution gains momentum and involves a plethora of disruptive technology concepts, such as blockchain, they have infiltrated economies that have only experienced a small portion of their scope, consequences, and applications in their different branches. This research aims to examine the potential uses of blockchain technology within the framework of smart contracts in the insurance sector, notably in the event of a pandemic that results in business interruption. Businesses hardly ever take business interruption insurance into account, particularly in a country similar to Lebanon, where natural disasters and pandemics are scarce. Due to the complexity of the task and the numerous requirements for trust in terms of risk consistency, traditional insurance companies are not interested in offering these kinds of insurance contracts. In this current study, a quantitative study was conducted over 213 businesses in various fields and revealed acceptance and socio-demographic differences in the activity sectors of this potentially ground-breaking solution for a developing country that is undergoing a sanitary and economic crisis. As a result, smart contracts and decentralized finance (DeFi) were proposed in the current research as potential solutions to overcome the Lebanese currency devaluation and high insurance costs.
Experimental Comparison of Editor Types for Domain-Specific Languages

The editor type can influence the user experience for a domain-specific language, but empirical evaluation of this factor is still quite limited. In this paper, we present the results of our empirical study, in which we compare the productivity of users with different kinds of editors for the same domain-specific language. We chose the domain of quiz definitions and used three editors: a text editor with syntax highlighting and code completion developed with the Xtext framework, a projectional editor created using JetBrains MPS, and an existing form-based editor—Google Forms. The study was performed on 37 graduate students of computer science. The measured time was lower for the text editor than for the form-based editor, and the form-based editor’s time was lower than the projectional one’s; however, the results were statistically insignificant. The experiment was also complemented with a survey providing insight into the perception of different editor types by users.
Data Matrix Based Low Cost Autonomous Detection of Medicine Packages

Counterfeit medicine is still a crucial problem for healthcare systems, having a huge impact in worldwide health and economy. Medicine packages can be traced from the moment of their production until they are delivered to the costumers through the use of Data Matrix codes, unique identifiers that can validate their authenticity. Currently, many practitioners at hospital pharmacies have to manually scan such codes one by one, a very repetitive and burdensome task. In this paper, a system which can simultaneously scan multiple Data Matrix codes and autonomously introduce them into an authentication database is proposed for the Hospital Pharmacy of the Centro Hospitalar de Vila Nova de Gaia/Espinho, E.P.E. Relevant features are its low cost and its seamless integration in their infrastructure. The results of the experiments were encouraging, and with upgrades such as real-time feedback of the code’s validation and increased robustness of the hardware system, it is expected that the system can be used as a real support to the pharmacists.
MST-VAE: Multi-Scale Temporal Variational Autoencoder for Anomaly Detection in Multivariate Time Series

In IT monitoring systems, anomaly detection plays a vital role in detecting and alerting unexpected behaviors timely to system operators. With the growth of signal data in both volumes and dimensions during operation, unsupervised learning turns out to be a great solution to trigger anomalies thanks to the feasibility of working well with unlabeled data. In recent years, autoencoder, an unsupervised learning technique, has gained much attention because of its robustness. Autoencoder first compresses input data to lower-dimensional latent representation, which obtains normal patterns, then the compressed data are reconstructed back to the input form to detect abnormal data. In this paper, we propose a practical unsupervised learning approach using Multi-Scale Temporal convolutional kernels with Variational AutoEncoder (MST-VAE) for anomaly detection in multivariate time series data. Our key observation is that combining short-scale and long-scale convolutional kernels to extract various temporal information of the time series can enhance the model performance. Extensive empirical studies on five real-world datasets demonstrate that MST-VAE can outperform baseline methods in effectiveness and efficiency.
Dataset Transformation System for Sign Language Recognition Based on Image Classification Network

Among the various fields where deep learning is used, there are challenges to be solved in motion recognition. One is that it is difficult to manage because of the vast amount of data. Another is that it takes a long time to learn due to the complex network and the large amount of data. To solve the problems, we propose a dataset transformation system. Sign language recognition was implemented to evaluate the performance of this system. The system consists of three steps: pose estimation, normalization, and spatial–temporal map (STmap) generation. STmap is a method of simultaneously expressing temporal data and spatial data in one image. In addition, the accuracy of the model was improved, and the error sensitivity was lowered through the data augmentation process. Through the proposed method, it was possible to reduce the dataset from 94.39 GB to 954 MB. It corresponds to approximately 1% of the original. When the dataset created through the proposed method is trained on the image classification model, the sign language recognition accuracy is 84.5%.
Joint Embedding of Semantic and Statistical Features for Effective Code Search

Code search is an important approach to improve effectiveness and efficiency of software development. The current studies commonly search target code based on either semantic or statistical information in large datasets. Semantic and statistical information have hidden relationships between them since they describe code snippets from different perspectives. In this work, we propose a joint embedding model of semantic and statistical features to improve the effectiveness of code annotation. Then, we implement a code search engine, i.e., JessCS, based on the joint embedding model. We evaluate JessCS on more than 1 million lines of code snippets and corresponding descriptions. The experimental results show that JessCS performs more effective than UNIF-based approach, with at least 13% improvements on the studied metrics.
Fuzzy Comprehensive Evaluation of Mixed Reality Seismic Retrofitting Training System

Due to the complexity of the construction environment and retrofitting methods, it is difficult to achieve the expected retrofitting effect. Therefore, effective seismic retrofitting training is a necessary way to ensure retrofitting workers acquire enough professional knowledge, skills and safe behaviors, which are critical to retrofitting. Mixed reality has huge potential in construction training. This paper conducts a fuzzy comprehensive evaluation of the MR seismic retrofitting training system to research the potential of MR in training. The purpose of this research was to provide scientific guidance and reference for the development, improvement and selection of MR training systems in the future. In this research, the evaluation indicators of the MR training system were firstly analyzed. Next, the weight of each evaluation indicator was calculated by the judgment matrix. Then, the evaluation model was established based on the qualitative–quantitative transformation principle of indicators. Finally, the results of the MR seismic retrofitting training system are obtained by combining the evaluation model with the membership matrix. The evaluation result of the training system in this paper is excellent.
A Conceptual Framework for Designing Virtual Environments for Stress Therapy

Virtual reality for stress therapy has been shown to be effective in reducing stress. However, the majority of the existing studies have only focused on investigating the effectiveness of virtual reality application as a stress reduction tool. Limited information was communicated on how the virtual environments used were designed. Existing design frameworks which may potentially be used as a reference turned out to be providing general guidelines and were not specifically developed for designing a virtual environment for stress therapy. The frameworks were also found to focus on either improving psychological well-being or virtual reality design respectively. To address the identified gap, this study proposed a framework for designing virtual environments for stress therapy. The framework incorporates design requirements from a psychological and information technology point of view. In addition, the framework also highlights the design elements that were specifically needed to design a virtual environment to reduce stress. This paper hence describes the framework and discusses the framework evaluation. Four methods, including (i) questionnaire, (ii) physiological measures, (iii) experiment and (iv) interview were used to evaluate the (i) effectiveness, (ii) usability, (iii) ease of use and (iv) usefulness of the system that implemented the concept of the framework in reducing stress. In addition, the methods were also used to assess the usefulness of the design elements listed in the framework to reduce stress. The results of the evaluation indicated that the system was (i) effective, (ii) usable, (iii) useful and (iv) easy to use to reduce stress. The results also showed that the proposed design elements were useful in aiding the stress-reduction process. As all the results returned positive findings, the framework was thus considered appropriate and suitable to be used as a guideline for designing virtual environments for stress therapy.
Design of an Efficient Face Recognition system using Deep Learning Technique

Greater reliance on smart and portable electronic devices demands engineers to provide solutions with better performance and minimized demerits. Face Recognition involves the method of associating and confirming the faces. It is fit for distinguishing, following, recognizing, or checking human appearances from a picture or video caught utilizing an advanced camera. Feature extraction is the most significant stage for the achievement of the face recognition framework. The different ways of implementing this project depends on the programming language or algorithms used such as MATLAB, OpenCV, visual basics C#, Viola-Jones algorithm and many more while the core functioning remains the same. In this work, we have implemented face recognition in 3 phases, Phase1 consists of detecting faces and collecting images IDs, Phase 2 involves training the Recognizer and Separating interesting elements and the final phase includes grouping them and putting away in XML records.
Collaborating inside and outside the school: together overcoming COVID-19 challenges in Chile

Purpose This research paper explores the value of collaborative inquiry networks of headteachers and curriculum coordinators to cope with 2020's coronavirus pandemic in Chile. Specifically, the authors describe the main challenges that networks identify in their contexts, the collaborative practices performed by different schools to address these challenges, and the influence of the networks on the innovative responses of teachers in their own schools. Design/methodology/approach This is a mixed-method study from a complementary stance using different methods and data of a project implemented with a total of 54 headteachers and curriculum coordinators. The data sources were participants' individual reports, the network teams' reports of their collaborative inquiry projects, and a short open-ended questionnaire responded by teachers that did not participate directly in the networks but benefited from their work. The data were analyzed using content analysis, categories were created to organize and describe the main findings. Findings Participants of the networks reported that their active participation in the collaborative inquiry allows them to share knowledge among different schools and has helped them to support innovative practices in their own schools. Specifically, they have reported that collaborating has permitted them to maintain a pedagogical focus, foster distributed leadership within the school communities, provide them with greater autonomy, and develop skills to favor the emotional containment of their teams. Inquiry teams perform diverse collective practices; they designed and applied virtual surveys, planned and implemented virtual workshops with teachers, and generated meaningful reflection about formative assessment and pedagogical practices. Originality/value This work offers insights into how the Chilean school system has responded to COVID-19 challenges and shows how despite the negative aspects of the pandemic, it has become an opportunity to recognize and enhance teachers' professional development through the collaboration among different schools. Most headteachers and curriculum coordinators reported that an active collaborative inquiry changed how they used to think about their leadership and strengthened the value of professional relationships to address extremely difficult challenges as a result of the pandemic. These lessons can be taken for the future, to rethink and rebuild educational systems.
Facial expression video generation based-on spatio-temporal convolutional GAN: FEV-GAN

Facial expression generation has always been an intriguing task for scientists and researchers all over the globe. In this context, we present our novel approach for generating videos of the six basic facial expressions. Starting from a single neutral facial image and a label indicating the desired facial expression, we aim to synthesize a video of the given identity performing the specified facial expression. Our approach, referred to as FEV-GAN (Facial Expression Video GAN), is based on Spatio-temporal Convolutional GANs, that are known to model both content and motion in the same network. Previous methods based on such a network have shown a good ability to generate coherent videos with smooth temporal evolution. However, they still suffer from low image quality and low identity preservation capability. In this work, we address this problem by using a generator composed of two image encoders. The first one is pre-trained for facial identity feature extraction and the second for spatial feature extraction. We have qualitatively and quantitatively evaluated our model on two international facial expression benchmark databases: MUG and Oulu-CASIA NIR&VIS. The experimental results analysis demonstrates the effectiveness of our approach in generating videos of the six basic facial expressions while preserving the input identity. The analysis also proves that the use of both identity and spatial features enhances the decoder ability to better preserve the identity and generate high-quality videos. The code and the pre-trained model will soon be made publicly available.
The effect of gender bias on hate speech detection

Hate speech against individuals or communities with different backgrounds is a major problem in online social networks. The domain of hate speech has spread to various topics, including race, religion, and gender. Although there are many efforts for hate speech detection in different domains and languages, the effects of gender identity are not solely examined in hate speech detection. Moreover, hate speech detection is mostly studied for particular languages, specifically English, but not low-resource languages, such as Turkish. We examine gender identity-based hate speech detection for both English and Turkish tweets. We compare the performances of state-of-the-art models using 20 k tweets per language. We observe that transformer-based language models outperform bag-of-words and deep learning models, while the conventional bag-of-words model has surprising performances, possibly due to offensive or hate-related keywords. Furthermore, we analyze the effect of debiased embeddings for hate speech detection. We find that the performance can be improved by removing the gender-related bias in neural embeddings since gender-biased words can have offensive or hateful implications.
An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering

Knowledge graph-based question answering is an intelligent approach to deducing the answer to a natural language question from structured knowledge graph information. As one of the mainstream knowledge graph-based question answering approaches, information retrieval-based methods infer the correct answer by constructing and ranking candidate paths, which achieve excellent performance in simple questions but struggle to handle complex questions due to rich entity information and diverse relations. In this paper, we construct a joint system with three subsystems based on the information retrieval methods, where candidate paths can be efficiently generated and ranked, and a new text-matching method is introduced to capture the semantic correlation between questions and candidate paths. Results of the experiment conducted on the China Conference on Knowledge Graph and Semantic Computing 2019 Chinese Knowledge Base Question Answering dataset verify the superiority and efficiency of our approach.
Record linkage based patient intersection cardinality for rare disease studies using Mainzelliste and secure multi-party computation

The low number of patients suffering from any given rare diseases poses a difficult problem for medical research: With the exception of some specialized biobanks and disease registries, potential study participants' information are disjoint and distributed over many medical institutions. Whenever some of those facilities are in close proximity, a significant overlap of patients can reasonably be expected, further complicating statistical study feasibility assessments and data gathering. Due to the sensitive nature of medical records and identifying data, data transfer and joint computations are often forbidden by law or associated with prohibitive amounts of effort. To alleviate this problem and to support rare disease research, we developed the Mainzelliste Secure EpiLinker (MainSEL) record linkage framework, a secure Multi-Party Computation based application using trusted-third-party-less cryptographic protocols to perform privacy-preserving record linkage with high security guarantees. In this work, we extend MainSEL to allow the record linkage based calculation of the number of common patients between institutions. This allows privacy-preserving statistical feasibility estimations for further analyses and data consolidation. Additionally, we created easy to deploy software packages using microservice containerization and continuous deployment/continuous integration. We performed tests with medical researchers using MainSEL in real-world medical IT environments, using synthetic patient data.We show that MainSEL achieves practical runtimes, performing 10 000 comparisons in approximately 5 minutes. Our approach proved to be feasible in a wide range of network settings and use cases. The "lessons learned" from the real-world testing show the need to explicitly support and document the usage and deployment for both analysis pipeline integration and researcher driven ad-hoc analysis use cases, thus clarifying the wide applicability of our software. MainSEL is freely available under: https://github.com/medicalinformatics/MainSEL CONCLUSIONS: MainSEL performs well in real-world settings and is a useful tool not only for rare disease research, but medical research in general. It achieves practical runtimes, improved security guarantees compared to existing solutions, and is simple to deploy in strict clinical IT environments. Based on the "lessons learned" from the real-word testing, we hope to enable a wide range of medical researchers to meet their needs and requirements using modern privacy-preserving technologies.