Reviewerly/exHarmony huggingface dataset:
Unpaired Underwater Image Enhancement Based on CycleGAN

Underwater image enhancement recovers degraded underwater images to produce corresponding clear images. Image enhancement methods based on deep learning usually use paired data to train the model, while such paired data, e.g., the degraded images and the corresponding clear images, are difficult to capture simultaneously in the underwater environment. In addition, how to retain the detailed information well in the enhanced image is another critical problem. To solve such issues, we propose a novel unpaired underwater image enhancement method via a cycle generative adversarial network (UW-CycleGAN) to recover the degraded underwater images. Our proposed UW-CycleGAN model includes three main modules: (1) A content loss regularizer is adopted into the generator in CycleGAN, which constrains the detailed information existing in one degraded image to remain in the corresponding generated clear image; (2) A blur-promoting adversarial loss regularizer is introduced into the discriminator to reduce the blur and noise in the generated clear images; (3) We add the DenseNet block to the generator to retain more information of each feature map in the training stage. Finally, experimental results on two unpaired underwater image datasets produced satisfactory performance compared to the state-of-the-art image enhancement methods, which proves the effectiveness of the proposed model.
Effective Emotion Recognition Technique in NLP Task over Nonlinear Big Data Cluster

Human-to-human communication can be achieved not only by body language but also by high-level language. Moreover, information can be conveyed in writing. In particular, the high-level and specific process of logical thinking can be expressed in writing. Text is data that we encounter daily, and there are hidden patterns in it. A person’s cognitive activity, that is, text data, contains the author’s emotions. In the existing text analysis method, simply using the frequency of words has limited interpretability. The model proposed in this paper is a nonlinear emotion system based on emotion to increase document diversity. The purpose is to effectively converge features by assigning weights to a nonlinear function with existing training and learning methods. Our study used the confusion matrix, an area under the receiver operating characteristic curve, and F1-score as evaluation methods. This research created a new error function and measured emotions. The accuracy was 0.9447, and the model’s receiver operating curve peak was 0.9845, which is somewhat similar to that of TF-IDF in the evaluation.
Residual geochemical gold grade prediction using extreme gradient boosting

The increasing cost of exploration and decreasing recovery in mineral exploration necessitates the use of reliable and robust models for spatial gold grade prediction. In comparison, ordinary kriging (OK) is widely applied in geospatial data analysis for target generation however, its assumption of spatial variability renders it unsatisfactorily for problems, where geological processes such as faulting have altered the ore deposits. In this study, the accuracy and robustness of the Extreme Gradient Boosting (XGBoost) model for regolith geochemical grade prediction was investigated within the Tarkwaian paleo-placer formation of Ghana, West Africa. The geochemical spatial data (total=891) was partitioned into training and testing for model development and evaluation, respectively. The optimal parameters of the model were selected by grid search with 10-fold cross-validation. The predictive performance of the model was evaluated and compared with Random Forest (RF), Generalised regression neural network (GRNN) and OK. The results show that the XGBoost model used in this study produced the lowest error score (MSE = 0.7107, MAPE = 9.3948, RMSE = 0.8430) and the highest predictive efficiency (R2 = 0.9189). In terms of accuracy and generalization, this investigation has proven that XGBoost is a very efficient and robust model for geochemical gold grade prediction. Importantly, all the machine learning models used in this study proved superiority over the OK.
Connecting Embeddings Based on Multiplex Relational Graph Attention Networks for Knowledge Graph Entity Typing

Knowledge graph entity typing (KGET) aims to infer missing entity typing instances in KGs, which is a significant subtask of KG completion. Despite of its progress, however, it still faces two non-trivial challenges: (i) most existing KGET methods extract features by encoding the existing entity typing tuples, while ignoring rich relational knowledge. (ii) they typically treat each entity typing tuple in KGs independently, and thus inevitably fail to take account of the inherent and valuable neighborhood information surrounding a tuple. To address these challenges, we build a novel Heterogeneous Relational Graph (HRG), and propose a Multiplex Relational Graph Attention Networks (MRGAT) to learn on HRG, and then utilize a Connecting Embeddings model (ConnectE) to make entity type inference. Specifically, the framework contains three components. Firstly, to effectively integrate the entity typing tuples and entity relation triples in KGs, we construct a HRG that consists of three semantic subgraphs. Secondly, we employ MRGAT to learn embeddings on HRG. In MRGAT, each subgraph of HRG is fed to its corresponding model that is capable of capturing neighborhood information. Finally, given the learned embeddings, we make entity type prediction by ConnectE. Experimental results validate the superiority of our model against various state-of-the-art baselines.
SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed.
Generating Personalized Dialogues Based on Conversation Log Summarization and Sentiment Analysis

The number of elderly people living at home (requiring nursing care or living alone) has been increasing due to the aging of society in recent years. As the elderly stay at home for long periods of time, their connection with the local community becomes less and less, and loneliness becomes a serious problem. In our research group, we are developing a system that allows elderly people to interact with the virtual agent. The purpose of this paper is to develop a method for generating personalized dialogue in order to realize continuous and sentimental dialogue. As the approach, morphological analysis and sentiment analysis are performed on the dialogue content (text) obtained from speech recognition. Then, we present a new method for generating personalized dialogues based on related conversation log summarization. In this way, it is promising to realize smarter personalized dialogues that do not rely on manual creation.
Detection of Risky Situations for Frail Adults With Hybrid Neural Networks on Multimodal Health Data

In healthcare applications, the multimedia methodology is applied to multimodal signals and visual data. This article focuses on the detection of risk situations of frail people from lifelog multimodal signals and video recorded with wearable sensors. We propose a hybrid 3D convolutional neural network (3DCNN) and gated recurrent unit (GRU) (3DCNN-GRU) deep architecture with two branches. The first branch is a GRU network with a global attention block for classification of multisensory signal data. The second branch is a 3DCNN with windowing synchronized with multidimensional time-series signals. Two branches of the neural network are fused yielding promising results. The method produces 83.26% accuracy with dataset BIRDS. Benchmarking is also fulfilled on a publicly available dataset in action recognition.
Explainable Thermal to Visible Face Recognition Using Latent-Guided Generative Adversarial Network

One of the main challenges in performing thermal-to-visible face image translation is preserving the identity across different spectral bands. Existing work does not effectively disentangle the identity from other confounding factors. In this paper, we propose a Latent-Guided Generative Adversarial Network (LG-GAN) to explicitly decompose an input image into identity code that is spectral-invariant and style code that is spectral-dependent. By using such a disentanglement, we are able to analyze the identity preservation by interpreting and visualizing the identity code. We present extensive face recognition experiments on two challenging Visible-Thermal face datasets. We show that the learned identity code is effective in preserving the identity, thus offering useful insights on interpreting and explaining thermal-to-visible face image translation.
The Effects on Secondary School Students of Applying Experiential Learning to the Conversational AI Learning Curriculum

The purpose of this study was to design a curriculum of artificial intelligence (AI) application for secondary schools. The learning objective of the curriculum was to allow students to learn the application of conversational AI on a block-based programming platform. Moreover, the empirical study actually implemented the curriculum in the formal learning of a secondary school for a period of six weeks. The study evaluated the learning performance of students who were taught with the cycle of experiential learning in one class, while also evaluating the learning performance of students who were taught with the conventional instruction, which was called the cycle of doing projects. Two factors, learning approach and gender, were taken into account. The results showed that females’ learning effectiveness was significantly better than that of males regardless of whether they used experiential learning or the conventional projects approach. Most of the males tended to be distracted from the conversational AI curriculum because they misbehaved during the conversational AI process. In particular, in their performance using the Voice User Interface with the conventional learning approach, the females outperformed the males significantly. The results of two-way ANCOVA revealed a significant interaction between gender and learning approach on computational thinking concepts. Females with the conventional learning approach of doing projects had the best computational thinking concepts in comparison with the other groups.
Higher education managers’ perspectives on quality management and technology acceptance: A tale of elders, mediators, and working bees in times of Covid-19

The Covid-19 pandemic has emphasized the role of educational management information systems (EMIS) for quality management (QM) in higher education, and set new directions for post-pandemic studies. Successful implementation of QM processes depends largely on managers' perceptions about quality and educational technology. However, higher education managers’ profiles regarding these quality perceptions and their EMIS acceptance have been insufficiently investigated so far. In response to this research gap, we identified such profiles based on a quantitative survey of N = 70 managers from Chilean higher education institutions during the Covid-19 pandemic. A cluster analysis revealed three distinct manager types: “Elders” (oldest participants, almost equally distributed across positions, with least frequent EMIS access, moderate EMIS acceptance, and highest QM perceptions), “Mediators” (in operational and middle-management positions, with moderately frequent access to EMIS, and lowest EMIS acceptance and QM perceptions), and “Working Bees” (younger females in operational positions, with most frequent EMIS access, highest EMIS acceptance, and moderate QM perceptions). Knowledge of these profiles may enable customized training in the recovery after the Covid-19 pandemic.
Promoting Social Justice Through Usability in Technical Communication: An Integrative Literature Review

Purpose : Recently, interest in usability has grown in the technical communication (TC) field, but we lack a current cohesive literature review that reflects this new growth. This article provides an integrative literature review on usability, its goals, and approaches to accomplish those goals in relation to TC's commitment to social justice and empowerment.&lt;br/&gt; Methods : I conducted an integrative literature review on usability to synthesize and characterize TC's growing commitment to social justice and empowerment. I searched scholarly publications and trade literature that included books and book chapters on usability. Adopting grounded theory and content analysis as research techniques to systematically evaluate data corpus, I read and classified selected publications to approach the research questions and iteratively analyzed the data to identify themes within each research question.&lt;br/&gt; Results : Surveying the definitions and descriptions of usability in the literature corpus shows that there is no consensus definition of usability. Findings suggest that the goal of usability can be classified as: a) pragmatic or functional goals, b) user experience goals, and c) sociocultural goals. Given the recent cultural and social justice turns in TC, my findings reveal a number of social justice-oriented design approaches for usability.&lt;br/&gt; Conclusions : Usability should not be viewed solely as a means of achieving pragmatic and/or user experience goals. Practitioners also need to consider usability from sociocultural orientations to accomplish its sociocultural goals. From interconnected global perspectives, the review implies the need for adopting more viable and culturally sustaining design approaches for successfully accommodating cultural differences and complexities for promoting social justice and user empowerment.
Broadband Josephson parametric amplifier using lumped-element transmission line impedance matching architecture

We present a fishbone-like lumped-element artificial transmission line to overcome impedance mismatch in a reflection-type Josephson parametric amplifier between a nonlinear resonator and an external transmission line. Using this easily prepared architecture, we design and fabricate a broadband Josephson parametric amplifier, which has gain in an excess of 20 dB with a bandwidth of hundreds of MHz. Furthermore, by varying the working point of the device, the operating frequency of amplification can be tuned in a wide frequency range of 1 GHz while the amplifier operates in the mode of either three-wave mixing or four-wave mixing. Such a parametric amplifier is suitable for engineering applications of superconducting circuit quantum electrodynamics.
Factors impacting the use of the NZ COVID Tracer application in New Zealand"

The COVID-19 pandemic has clearly created devastating damage on world economics and public health. This project identifies key concerns of end-users toward the NZ COVID-19 contact tracing app in New Zealand. The key research objective in this study is to understand the usage behaviour towards the mobile application NZ COVID-19 Tracer used for contact tracing purposes. Secondly, the study explores the reasons for using the contact tracing app. Thirdly, it examines the relationship between usage behaviour of the NZ-COVID Tracer app with age, the user's perceived health, attitude towards COVID-19, whether family or friends are infected by COVID-19, trust in maintaining social distancing, trust in data privacy, smartphone usage and the media's role in motivating people to use this app. Consequently, understanding these issues and challenges could help improve the usage of this contact tracing app, which in turn would contribute to better public health outcomes in disease management and containment. Findings of the study reveals that age, smartphone usage behaviour, and trust in privacy data protection from the app provider has a statistically significant relationship on usage behaviour of the NZ COVID Tracer app. Self-perceived health status and attitudes towards the COVID-19 pandemic did not have a significant relationship on NZ COVID Tracer app usage behaviour. While social media, such as Facebook, has been shown to be the most popular source of news for COVID-19 among New Zealanders, it is television that acted as a motivational tool to encourage people to use the contact tracing mobile application and to practice other measures to help protect against the COVID-19 pandemic.
An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning

Since the 20th century, cancer has been a growing threat to human health. Cancer is a malignant tumor with high clinical morbidity and mortality, and there is a high risk of recurrence after surgery. At the same time, the diagnosis of whether the cancer is in situ recurrence is crucial for further treatment of cancer patients. According to statistics, about 90% of cancer-related deaths are due to metastasis of primary tumor cells. Therefore, the study of the location of cancer recurrence and its influencing factors is of great significance for the clinical diagnosis and treatment of cancer. In this paper, we propose an assisted diagnosis model for cancer patients based on federated learning. In terms of data, the influencing factors of cancer recurrence and the special needs of data samples required by federated learning were comprehensively considered. Six first-level impact indicators were determined, and the historical case data of cancer patients were further collected. Based on the federated learning framework combined with convolutional neural network, various physical examination indicators of patients were taken as input. The recurrence time and recurrence location of patients were used as output to construct an auxiliary diagnostic model, and linear regression, support vector regression, Bayesling regression, gradient ascending tree and multilayer perceptrons neural network algorithm were used as comparison algorithms. CNN's federated prediction model based on improved under the condition of the joint modeling and simulation on the five types of cancer data accuracy reached more than 90%, the accuracy is better than single modeling machine learning tree model and linear model and neural network, the results show that auxiliary diagnosis model based on the study of cancer patients in assisted the doctor in the diagnosis of patients, As well as effectively provide nutritional programs for patients and have application value in prolonging the life of patients, it has certain guiding significance in the field of medical cancer rehabilitation.
Rare earth elements associated with carbonatite–alkaline complexes in western Rajasthan, India: exploration targeting at regional scale

Abstract. A two-stage fuzzy inference system (FIS) is applied to prospectivity modelling and exploration-target delineation for rare earth element (REE) deposits associated with carbonatite–alkaline complexes in the western part of the state of Rajasthan in India. The design of the FIS and selection of the input predictor map are guided by a generalised conceptual model of carbonatite–alkaline-complex-related REE mineral systems. In the first stage, three FISs are constructed to map the fertility and favourable geodynamic settings, favourable lithospheric architecture for fluid transportation and favourable shallow crustal (near-surface) emplacement architecture, respectively, for REE deposits in the study area. In the second stage, the outputs of the above FISs are integrated to map the prospectivity of REE deposits in the study area. Stochastic and systemic uncertainties in the output prospectivity maps are estimated to facilitate decision-making regarding the selection of exploration targets. The study led to the identification of prospective targets in the Kamthai–Sarnu-Dandeli and Mundwara regions, where detailed project-scale ground exploration is recommended. Low-confidence targets were identified in the Siwana ring complex region, north and northeast of Sarnu-Dandeli, south of Barmer, and south of Mundwara. Detailed geological mapping and geochemical sampling together with high-resolution magnetic and radiometric surveys are recommended in these areas to increase the level of confidence in the prospectivity of these targets before undertaking project-scale ground exploration. The prospectivity-analysis workflow presented in this paper can be applied to the delineation of exploration targets in geodynamically similar regions globally, such as Afar province (East Africa), Paraná–Etendeka (South America and Africa), Siberia (Russia), East European Craton–Kola (eastern Europe), Central Iapetus (North America, Greenland and the Baltic region) and the pan-superior province (North America).
Relevance of blockchain technology and the management of libraries and archives in the 4IR

Purpose This study aims to examine the awareness and perception of the relevance of blockchain technology in the management of libraries and archives in the fourth industrial revolution in a developing country, Nigeria. Design/methodology/approach Ten system librarians selected from five different libraries and ten archivists selected from five different archives in the Southwest geopolitical zone of Nigeria represent the sample for the study. The study adopted a purely qualitative approach where data was collected through an open-ended questionnaire mailed to the participants to guide against physical contact and maintained social distancing to observe COVID-19 protocol. Findings The results reveal that both the librarians and archivists are aware of blockchain technology, its adoption and use in libraries and archives. There is a positive perception about blockchain technology believing that it can assist libraries and archives in storing information in a distributed settings through gathering, preserving and sharing authoritative information, and supports archivists in creating a unique verifiable record that can be accessed by anyone in archives. Implementation, maintenance cost and difficulty in sustainability are identified as the challenges of blockchain adoption. Research limitations/implications This study is limited to one geopolitical zone of Nigeria which necessitates the need for further research on the similar subject matter in other geopolitical zones in Nigeria. Practical implications The findings of the study have identified the need for librarians and archivists to increase their awareness and develop a positive perception toward blockchain technologies. Originality/value To the best of the authors’ knowledge, this study is the first to research the relevance of blockchain technology in Nigerian libraries and archives. This paper reported the benefits libraries and archives could derive from the adoption of blockchain, and how such can improve effective services delivery to the satisfaction of the users of archives and libraries.