Reviewerly/exHarmony huggingface dataset:
3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images

Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional (3D) medical images has great potential that can be extended to many medical applications, such as, image enhancement and disease progression modeling. Current GAN technologies for 3D medical image synthesis must be significantly improved to be suitable for real-world medical problems. In this paper, we extend the state-of-the-art StyleGAN2 model, which natively works with two-dimensional images, to enable 3D image synthesis. In addition to the image synthesis, we investigate the behavior and interpretability of the 3D-StyleGAN via style vectors inherited form the original StyleGAN2 that are highly suitable for medical applications: (i) the latent space projection and reconstruction of unseen real images, and (ii) style mixing. The model can be applied to any 3D volumetric images. We demonstrate the 3D-StyleGAN's performance and feasibility with \(\sim \)12,000 three-dimensional full brain MR T1 images. Furthermore, we explore different configurations of hyperparameters to investigate potential improvement of the image synthesis with larger networks. The codes and pre-trained networks are available online: https://github.com/sh4174/3DStyleGAN.
DualFace: Two-stage drawing guidance for freehand portrait sketching

Abstract Special skills are required in portrait painting, such as imagining geometric structures and facial detail for final portrait designs. This makes it a difficult task for users, especially novices without prior artistic training, to draw freehand portraits with high-quality details. In this paper, we propose dualFace, a portrait drawing interface to assist users with different levels of drawing skills to complete recognizable and authentic face sketches. Inspired by traditional artist workflows for portrait drawing, dualFace gives two-stages of drawing assistance to provide global and local visual guidance. The former helps users draw contour lines for portraits (i.e., geometric structure), and the latter helps users draw details of facial parts, which conform to the user-drawn contour lines. In the global guidance stage, the user draws several contour lines, and dualFace then searches for several relevant images from an internal database and displays the suggested face contour lines on the background of the canvas. In the local guidance stage, we synthesize detailed portrait images with a deep generative model from user-drawn contour lines, and then use the synthesized results as detailed drawing guidance. We conducted a user study to verify the effectiveness of dualFace, which confirms that dualFace significantly helps users to produce a detailed portrait sketch.
SmartMedChain: A Blockchain-Based Privacy-Preserving Smart Healthcare Framework

Nowadays, the adoption of Internet of Things (IoT) technology worldwide is accelerating the digital transformation of healthcare industry. In this context, smart healthcare (s-healthcare) solutions are ensuring better and innovative opportunities for healthcare providers to improve patients' care. However, these solutions raise also new challenges in terms of security and privacy due to the diversity of stakeholders, the centralized data management, and the resulting lack of trustworthiness, accountability, and control. In this paper, we propose an end-to-end Blockchain-based and privacy-preserving framework called SmartMedChain for data sharing in s-healthcare environment. The Blockchain is built on Hyperledger Fabric and stores encrypted health data by using the InterPlanetary File System (IPFS), a distributed data storage solution with high resiliency and scalability. Indeed, compared to other propositions and based on the concept of smart contracts, our solution combines both data access control and data usage auditing measures for both Medical IoT data and Electronic Health Records (EHRs) generated by s-healthcare services. In addition, s-healthcare stakeholders can be held accountable by introducing an innovative Privacy Agreement Management scheme that monitors the execution of the service in respect of patient preferences and in accordance with relevant privacy laws. Security analysis and experimental results show that the proposed SmartMedChain is feasible and efficient for s-healthcare environments.
An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.
No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion

No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number and role of digital images in everyday life is continuously growing. Recently, a huge amount of effort has been devoted to exploiting convolutional neural networks and other deep learning techniques for no-reference image quality assessment. Since deep learning relies on a massive amount of labeled data, utilizing pretrained networks has become very popular in the literature. In this study, we introduce a novel, deep learning-based NR-IQA architecture that relies on the decision fusion of multiple image quality scores coming from different types of convolutional neural networks. The main idea behind this scheme is that a diverse set of different types of networks is able to better characterize authentic image distortions than a single network. The experimental results show that our method can effectively estimate perceptual image quality on four large IQA benchmark databases containing either authentic or artificial distortions. These results are also confirmed in significance and cross database tests.
Intra-domain and cross-domain transfer learning for time series data—How transferable are the features?

In practice, it is very challenging and sometimes impossible to collect datasets of labelled data large enough to successfully train a machine learning model, and one possible solution to this problem is using transfer learning. In this study, we investigate how transferable are features between different domains of time series data and under what conditions. The effects of transfer learning are observed in terms of the predictive performance of the models and their convergence rate during training. In our experiment, we used reduced datasets of 1500 and 9000 data instances to mimic real-world conditions. We trained two sets of models (four different architectures) on the reduced datasets: those trained with transfer learning and those trained from scratch. Knowledge transfer was performed both within the same application domain (seismology) and between different application domains (seismology, speech, medicine, finance). We observed the prediction performance of the models and their training convergence rate. We repeated the experiments seven times and applied statistical tests to confirm the validity of the results. The overall conclusion of our study is that transfer learning is highly likely to either increase or not negatively affect the model’s predictive performance or its training convergence rate. We discuss which source and target domains are compatible for knowledge transfer. We also discuss the effect of the target dataset size and the choice of the model and its hyperparameters on transfer learning.
IoT-based Lava Flood Early Warning System with Rainfall Intensity Monitoring and Disaster Communication Technology

A lava flood disaster is a volcanic hazard that often occurs when heavy rains are happening at the top of a volcano. This flood carries volcanic material from upstream to downstream of the river, affecting populous areas located quite far from the volcano peak. Therefore, an advanced early warning system of cold lava floods is inarguably vital. This paper aims to present a reliable, remote, Early Warning System (EWS) specifically designed for lava flood detection, along with its disaster communication system. The proposed system consists of two main subsystems: lava flood detection and disaster communication systems. It utilizes a modified automatic rain gauge; a novel configured vibration sensor; Fuzzy Tree Decision algorithm; ESP microcontrollers that support IoT, and disaster communication tools (WhatsApp, SMS, radio communication). According to the experiment results, the prototype of rainfall detection using the tipping bucket rain gauge sensor can measure heavy and moderate rainfall intensities with 81.5% accuracy. Meanwhile, the prototype of earthquake vibration detection using a geophone sensor can remove noise from car vibrations with a Kalman filter and measure vibrations in high and medium intensity with an accuracy of 89.5%. Measurements from sensors are sent to the webserver. The disaster mitigation team uses data from the webserver to evacuate residents using the disaster communication method. The proposed system was successfully implemented in Mount Merapi, Indonesia, coordinated with the local Disaster Deduction Risk (DDR) forum. Doi: 10.28991/esj-2021-SP1-011 Full Text: PDF
Quantum communication with itinerant surface acoustic wave phonons

Abstract Surface acoustic waves are commonly used in classical electronics applications, and their use in quantum systems is beginning to be explored, as evidenced by recent experiments using acoustic Fabry–Pérot resonators. Here we explore their use for quantum communication, where we demonstrate a single-phonon surface acoustic wave transmission line, which links two physically separated qubit nodes. Each node comprises a microwave phonon transducer, an externally controlled superconducting variable coupler, and a superconducting qubit. Using this system, precisely shaped individual itinerant phonons are used to coherently transfer quantum information between the two physically distinct quantum nodes, enabling the high-fidelity node-to-node transfer of quantum states as well as the generation of a two-node Bell state. We further explore the dispersive interactions between an itinerant phonon emitted from one node and interacting with the superconducting qubit in the remote node. The observed interactions between the phonon and the remote qubit promise future quantum-optics-style experiments with itinerant phonons.
Graph neural architecture search: A survey

In academia and industries, graph neural networks (GNNs) have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks. GNN models are usually handcrafted. However, building handcrafted GNN models is difficult and requires expert experience because GNN model components are complex and sensitive to variations. The complexity of GNN model components has brought significant challenges to the existing efficiencies of GNNs. Hence, many studies have focused on building automated machine learning frameworks to search for the best GNN models for targeted tasks. In this work, we provide a comprehensive review of automatic GNN model building frameworks to summarize the status of the field to facilitate future progress. We categorize the components of automatic GNN model building frameworks into three dimensions according to the challenges of building them. After reviewing the representative works for each dimension, we discuss promising future research directions in this rapidly growing field.
DCAN: Improving Temporal Action Detection via Dual Context Aggregation

Temporal action detection aims to locate the boundaries of action in the video. The current method based on boundary matching enumerates and calculates all possible boundary matchings to generate proposals. However, these methods neglect the long-range context aggregation in boundary prediction. At the same time, due to the similar semantics of adjacent matchings, local semantic aggregation of densely-generated matchings cannot improve semantic richness and discrimination. In this paper, we propose the end-to-end proposal generation method named Dual Context Aggregation Network (DCAN) to aggregate context on two levels, namely, boundary level and proposal level, for generating high-quality action proposals, thereby improving the performance of temporal action detection. Specifically, we design the Multi-Path Temporal Context Aggregation (MTCA) to achieve smooth context aggregation on boundary level and precise evaluation of boundaries. For matching evaluation, Coarse-to-fine Matching (CFM) is designed to aggregate context on the proposal level and refine the matching map from coarse to fine. We conduct extensive experiments on ActivityNet v1.3 and THUMOS-14. DCAN obtains an average mAP of 35.39% on ActivityNet v1.3 and reaches mAP 54.14% at IoU@0.5 on THUMOS-14, which demonstrates DCAN can generate high-quality proposals and achieve state-of-the-art performance. We release the code at https://github.com/cg1177/DCAN.
Autonomy, Motivation, and Digital Pedagogy Are Key Factors in the Perceptions of Spanish Higher-Education Students toward Online Learning during the COVID-19 Pandemic

This paper proposes a development model of the adaptation capacity of students to digital transformation in university teaching through three constructs: motivations, digital pedagogy, and student autonomy. For this study, an ad hoc scale was created to record the adaptation capacity of students to digital transformation. The sample was 483 students from the University of Seville (Spain), to whom an online survey was administered during the development of online teaching in the period of November 2020 using the Google Forms platform. The findings of this study showed that university student motivation acquired a greater threshold than autonomy, whose threshold in turn, was greater than that of digital pedagogy in the ability to adapt to online teaching and that the capacity of adaptation to the online modality is explained by the perception that university students have of the usefulness, products, and learning outcomes, among others. In conclusion, the lack of adequate and enabled study spaces is key to developing the online model. We consider all these aspects as prospective research objectives.
A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography

Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0-2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.
Motion Guided Region Message Passing for Video Captioning

Video captioning is an important vision task and has been intensively studied in the computer vision community. Existing methods that utilize the fine-grained spatial information have achieved significant improvements, however, they either rely on costly external object detectors or do not sufficiently model the spatial/temporal relations. In this paper, we aim at designing a spatial information extraction and aggregation method for video captioning without the need of external object detectors. For this purpose, we propose a Recurrent Region Attention module to better extract diverse spatial features, and by employing Motion-Guided Cross-frame Message Passing, our model is aware of the temporal structure and able to establish high-order relations among the diverse regions across frames. They jointly encourage information communication and produce compact and powerful video representations. Furthermore, an Adjusted Temporal Graph Decoder is proposed to flexibly update video features and model high-order temporal relations during decoding. Experimental results on three benchmark datasets: MSVD, MSR-VTT, and VATEX demonstrate that our proposed method can outperform state-of-the-art methods.
Blockchain Enabled Cyber Security: A Comprehensive Survey

This paper identifies research paths for blockchain in cyber security that need further research: Internet of Things (IoT) security has indeed been promoted as a critical industrial necessity, even though almost all publications on blockchain cyber security in literature pointed out that the security of IoT systems can be revived if it is backed by blockchain. Yet, few things are now known and talked about the decision-making and practicality of adopting the technology, or how it may be used realistically to correct existing IoT security risks and threats, providing scope for imagination and the construction of future vectors in this particular area. As a result, future research must establish measurable recommendations and methods that can assist in filling the gaps in the existing literature. There is also the potential to develop solutions for IoT devices with limited resources (operating on the periphery of the network) that are based on blockchain technology.
A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure

Abstract Background Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units. Methods Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients’ clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation. Results The performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820–0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805–0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk. Conclusion Using readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.
Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science

Abstract Given the growing use of Artificial intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system. A common misconception is that the environmental sciences are immune to such unintended consequences when AI is being used, as most data come from observations, and AI algorithms are based on mathematical formulas, which are often seen as objective. In this article, we argue the opposite can be the case. Using specific examples, we demonstrate many ways in which the use of AI can introduce similar consequences in the environmental sciences. This article will stimulate discussion and research efforts in this direction. As a community, we should avoid repeating any foreseeable mistakes made in other domains through the introduction of AI. In fact, with proper precautions, AI can be a great tool to help reduce climate and environmental injustice. We primarily focus on weather and climate examples but the conclusions apply broadly across the environmental sciences.
Improving Children's Cognitive Intelligence Through Literacy Management

Cognitive intelligence has a vital role in children's growth and development to solve remembering, logical thinking, and symbolic thinking activities. This study aim to explore how the implementation of in improving early childhood cognitive intelligence in RA Tania, Paiton, Probolinggo?. This research uses a qualitative case study approach. Researchers conducted observations and interviews with principals, teachers, students, and guardians of students. The data analysis technique is carried out in stages, including data presentation, data reduction, and concluding. The results showed that literacy management in improving early childhood cognitive intelligence was carried out through; identification of needs, implementation of literacy management, evaluation, and a tiered plan. This study implies that literacy management can increase interest in reading and writing and an understanding of simple problem-solving in early childhood