Reviewerly/exHarmony huggingface dataset:
Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems

With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments.
Influence of virtual reality on visual parameters: immersive versus non-immersive mode

Abstract Background To investigate the differences in refraction, accommodative factors, visual parameters, and subjective symptoms after using two types of virtual reality (VR) content with different depths of perception. Methods Twenty-three volunteers, who played VR games in two modes (immersive and non-immersive) for 30 min, were enrolled. Visual parameters were examined before and after using VR. Accommodative factors were measured using static and dynamic methods. Subjective symptoms were assessed using a questionnaire. Differences according to VR content were compared, and correlations between each visual parameter were analyzed. Results There were no changes in refraction or accommodative factors after use of the VR. However, there was a significant increase in the near point of accommodation (NPA), the near point of convergence (NPC), and subjective symptom scores after using the immersive mode. Correlation analysis revealed a positive correlation between baseline values of near exophoria and mean accommodative lag of the dominant eye, and also revealed a negative correlation between NPA and mean accommodative lag in the non-dominant eye. Conclusions The use of VR for 30 min increased NPA and NPC, especially after the immersive mode was used. In addition, higher exophoria and smaller NPA is associated with increased accommodative lag after using VR.
Optimal transport mapping via input convex neural networks

In this paper, we present a novel and principled approach to learn the optimal transport between two distributions, from samples. Guided by the optimal transport theory, we learn the optimal Kantorovich potential which induces the optimal transport map. This involves learning two convex functions, by solving a novel minimax optimization. Building upon recent advances in the field of input convex neural networks, we propose a new framework where the gradient of one convex function represents the optimal transport mapping. Numerical experiments confirm that we learn the optimal transport mapping. This approach ensures that the transport mapping we find is optimal independent of how we initialize the neural networks. Further, target distributions from a discontinuous support can be easily captured, as gradient of a convex function naturally models a {\em discontinuous} transport mapping.
Joint Causal Inference from Multiple Contexts

The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. We introduce Joint Causal Inference (JCI), a novel approach to causal discovery from multiple data sets from different contexts that elegantly unifies both approaches. JCI is a causal modeling framework rather than a specific algorithm, and it can be implemented using any causal discovery algorithm that can take into account certain background knowledge. JCI can deal with different types of interventions (e.g., perfect, imperfect, stochastic, etc.) in a unified fashion, and does not require knowledge of intervention targets or types in case of interventional data. We explain how several well-known causal discovery algorithms can be seen as addressing special cases of the JCI framework, and we also propose novel implementations that extend existing causal discovery methods for purely observational data to the JCI setting. We evaluate different JCI implementations on synthetic data and on flow cytometry protein expression data and conclude that JCI implementations can considerably outperform state-of-the-art causal discovery algorithms.
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph

Multi-hop reasoning has been widely studied in recent years to seek an effective and interpretable method for knowledge graph (KG) completion.Most previous reasoning methods are designed for dense KGs with enough paths between entities, but cannot work well on those sparse KGs that only contain sparse paths for reasoning.On the one hand, sparse KGs contain less information, which makes it difficult for the model to choose correct paths.On the other hand, the lack of evidential paths to target entities also makes the reasoning process difficult.To solve these problems, we propose a multi-hop reasoning model named DacKGR over sparse KGs, by applying novel dynamic anticipation and completion strategies: (1) The anticipation strategy utilizes the latent prediction of embeddingbased models to make our model perform more potential path search over sparse KGs.(2) Based on the anticipation information, the completion strategy dynamically adds edges as additional actions during the path search, which further alleviates the sparseness problem of KGs.The experimental results on five datasets sampled from Freebase, NELL and Wikidata show that our method outperforms state-of-the-art baselines.
Medical analytics for healthcare intelligence – Recent advances and future directions

Recent advances in information technology have facilitated the massive collection of big data in numerous areas, including the healthcare sector. Healthcare data exists in various forms that can be briefly grouped into two categories. First, there is clinical data that is directly related to patients and medical conditions. This data includes, but is not limited to, demographic data, patient history, lab test results, physical examinations, diagnostic analysis and medical notes. The proliferation of wearable devices also enables the collection of clinical data periodically through monitoring systems via wireless technology, which supports the real-time tracking of patient care and timely adjustment of treatment plans. A second category of healthcare data originates from the business side of healthcare, such as operational and equipment costs, and logistic and administrative data which may be utilised for the optimisation of operational dynamics to support effective healthcare services and generally enhance utility in medical practice.
Delegated Proof of Reputation Consensus Mechanism for Blockchain-Enabled Distributed Carbon Emission Trading System

Most carbon Emission Trading Systems (ETS) rely on a centralized system to manage the transactional tasks, and are vulnerable to security threats. This article proposes a Blockchain-enabled Distributed ETS (BD-ETS) to improve the security and efficiency of the system. The BD-ETS transforms the centralized Carbon Emissions Permit (CEP) trading mode to a distributed trading system in which the trading mode is based on a smart contract performed in Hyperledger Fabric. In a smart contract, every transaction considers both the offer price and reputation value of the emitting enterprises. The voting power of the emitting enterprise is determined by its reputation value, which stems from their contributions to carbon emission reduction. To achieve consistency of every node in the CEP transactions, we propose a Delegated Proof of Reputation (DPoR) consensus mechanism. Compared to the enhanced Delegated Proof of Stake, the DPoR decreases the attack intention of malicious enterprises and performs better in finding malicious miners faster, thus improving the security of the BD-ETS. A case study and numerical simulations are developed to illustrate how the CEP trading functions, and to validate the DPoR mechanism.
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale

Traffic signs are essential map features for smart cities and navigation. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a new traffic sign dataset of 105K street-level images around the world covering 400 manually annotated traffic sign classes in diverse scenes, wide range of geographical locations, and varying weather and lighting conditions. The dataset includes 52K fully annotated images. Additionally, we show how to augment the dataset with 53K semi-supervised, partially annotated images. This is the largest and the most diverse traffic sign dataset consisting of images from all over the world with fine-grained annotations of traffic sign classes. We run extensive experiments to establish strong baselines for both detection and classification tasks. In addition, we verify that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research ( www.mapillary.com/dataset/trafficsign ) .
Secure Crowdsensing in 5G Internet of Vehicles: When Deep Reinforcement Learning Meets Blockchain

Vehicular crowdsensing has been widely leveraged in the intelligent transportation systems to collect real-time traffic data. However, the data security and privacy preservation issues have not been well investigated for vehicular crowdsensing. This article proposes a blockchain-enabled vehicular crowdsensing system to protect user privacy and data safety in 5G Internet of Vehicles (IoV). In order to maximize the security and minimize the latency of blockchain, a deep reinforcement learning (DRL)-enabled algorithm is proposed to select proper active miners and transactions. Then, a two-sided matching-based algorithm is put forwarded to allocate the nonorthogonal multiple access subchannels to minimize the maximum uploading delay of all users to guarantee the freshness of messages. Extensive experimental results demonstrate the effectiveness of our system. Finally, system analysis demonstrates that our system can protect user privacy, guarantee data security as well as integrity, and defend against common attacks. With the development of information communication techniques, vehicular crowdsensing is widely leveraged in the Intelligent Transportation Systems (ITSs) to collect real-time traffic information (e.g., traffic accidents and jams). Recently, vehicles have begun to be regarded as the consumer electronics product, due to the advancements in the development of autonomous vehicles, and users in vehicular crowdsensing focus more and more on their privacy protection and data security. In order to guarantee the connectivity and communication of users participating in the vehicular crowdsensing, intelligent management of network traffic and offering services to vehicles has become a vital need, especially for the highly dynamic topology and untrusty communication environment in 5G Internet of Vehicles (IoV).1 However, the vehicular crowdsensing-based data collection and transmission in 5G IoVs face the following major challenges:
Evaluation of College English Teaching Quality Based on Grey Clustering Analysis

Currently, there are two main problems with the evaluation of college English teaching quality: the lack of a complete evaluation system, and the limited quantification of evaluation indices. To solve the problems, this paper sums up the evaluation contents of college English teaching quality, and identifies the factors affecting the teaching quality of college English. On this basis, an improved evaluation system was established for the assessment of college English teaching quality, which is theoretically innovative. Next, grey clustering analysis and entropy weight method were combined into a robust evaluation model for college English teaching quality. This research provides a new and applicable solution to quantification of college English teaching quality.
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?

Although neural models have achieved impressive results on several NLP benchmarks, little is understood about the mechanisms they use to perform language tasks.Thus, much recent attention has been devoted to analyzing the sentence representations learned by neural encoders, through the lens of 'probing' tasks.However, to what extent was the information encoded in sentence representations, as discovered through a probe, actually used by the model to perform its task?In this work, we examine this probing paradigm through a case study in Natural Language Inference, showing that models can learn to encode linguistic properties even if they are not needed for the task on which the model was trained.We further identify that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself, highlighting the importance of careful controls when designing probing experiments.Finally, through a set of controlled synthetic tasks, we demonstrate models can encode these properties considerably above chance-level even when distributed in the data as random noise, calling into question the interpretation of absolute claims on probing tasks. 1
use of modern technologies by foreign language teachers: developing digital skills

In recent years, the question of using new information technologies in the education system has been increasingly raised. These are not only new technical means but also new forms and methods of teaching, a new approach to the learning process. The purpose of this study consisted in developing a program for teaching future foreign language teachers using digital technologies. In this study, a descriptive and analytical methods were used. Analysis of specialised literature on research topics as well as of training programs for future teachers of a foreign language was conducted. A system of classes for future teachers of a foreign language for a semester in the subject “English” and a number of recommendations for creating a course and conducting classes using digital technologies has been developed. A pilot study that showed the effectiveness of these techniques was conducted. It was found that thanks to the authors’ approach the students can complete the tasks assigned to them more quickly and learn how to correctly apply new tools in their work.
MultiNet: A deep neural network approach for detecting breast cancer through multi-scale feature fusion

Breast cancer diagnosis from biopsy tissue images conducted manually by pathologists is costly, time-consuming, and disagreements among specialists. Nowadays, the advancement of the Computer-Aided Diagnosis (CAD) system allows pathologists to identify breast cancer more reliably and quickly.For this reason, interest in CAD-based deep learning models has been increased significantly. In this study, we propose a “MultiNet” framework based on the transfer learning concept to classify different breast cancer types using two publicly available datasets that include 7909 and 400 microscopic breast images, respectively. The proposed “MultiNet” framework is designed to provide fast and accurate diagnostics for breast cancer with binary classification (benign and malignant) and multi-class classification (benign, in situ, invasive, and normal). In the proposed framework, features from microscopy images are extracted using three well-known pre-trained models, including DenseNet-201, NasNetMobile, and VGG16. The extracted features are then fed into the concatenate layer, making a robust hybrid model. The proposed framework yields an overall classification accuracy of 99% in classifying two classes. It also achieves 98% classification accuracy in classifying four classes. Such promising results will provide the opportunity to use “MultiNet” framework as a diagnostic model in clinics and health care.
SeQUeNCe: a customizable discrete-event simulator of quantum networks

Recent advances in quantum information science enabled the development of quantum communication network prototypes and created an opportunity to study full-stack quantum network architectures. This work develops SeQUeNCe, a comprehensive, customizable quantum network simulator. Our simulator consists of five modules: hardware models, entanglement management protocols, resource management, network management, and application. This framework is suitable for simulation of quantum network prototypes that capture the breadth of current and future hardware technologies and protocols. We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories. The simulation capabilities are illustrated in three use cases. We show the dependence of quantum network throughput on several key hardware parameters and study the impact of classical control message latency. We also investigate quantum memory usage efficiency in routers and demonstrate that redistributing memory according to anticipated load increases network capacity by 69.1% and throughput by 6.8%. We design SeQUeNCe to enable comparisons of alternative quantum network technologies, experiment planning, and validation and to aid with new protocol design. We are releasing SeQUeNCe as an open source tool and aim to generate community interest in extending it.
Structure-Preserving Image Super-Resolution

Structures matter in single image super-resolution (SISR). Benefiting from generative adversarial networks (GANs), recent studies have promoted the development of SISR by recovering photo-realistic images. However, there are still undesired structural distortions in the recovered images. In this paper, we propose a structure-preserving super-resolution (SPSR) method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details. First, we propose SPSR with gradient guidance (SPSR-G) by exploiting gradient maps of images to guide the recovery in two aspects. On the one hand, we restore high-resolution gradient maps by a gradient branch to provide additional structure priors for the SR process. On the other hand, we propose a gradient loss to impose a second-order restriction on the super-resolved images, which helps generative networks concentrate more on geometric structures. Second, since the gradient maps are handcrafted and may only be able to capture limited aspects of structural information, we further extend SPSR-G by introducing a learnable neural structure extractor (NSE) to unearth richer local structures and provide stronger supervision for SR. We propose two self-supervised structure learning methods, contrastive prediction and solving jigsaw puzzles, to train the NSEs. Our methods are model-agnostic, which can be potentially used for off-the-shelf SR networks. Experimental results on five benchmark datasets show that the proposed methods outperform state-of-the-art perceptual-driven SR methods under LPIPS, PSNR, and SSIM metrics. Visual results demonstrate the superiority of our methods in restoring structures while generating natural SR images. Code is available at https://github.com/Maclory/SPSR.
When medical images meet generative adversarial network: recent development and research opportunities

Abstract Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed well in computer vision. Medical image analysis is an important application of deep learning, which is expected to greatly reduce the workload of doctors, contributing to more sustainable health systems. However, most current AI methods for medical image analysis are based on supervised learning, which requires a lot of annotated data. The number of medical images available is usually small and the acquisition of medical image annotations is an expensive process. Generative adversarial network (GAN), an unsupervised method that has become very popular in recent years, can simulate the distribution of real data and reconstruct approximate real data. GAN opens some exciting new ways for medical image generation, expanding the number of medical images available for deep learning methods. Generated data can solve the problem of insufficient data or imbalanced data categories. Adversarial training is another contribution of GAN to medical imaging that has been applied to many tasks, such as classification, segmentation, or detection. This paper investigates the research status of GAN in medical images and analyzes several GAN methods commonly applied in this area. The study addresses GAN application for both medical image synthesis and adversarial learning for other medical image tasks. The open challenges and future research directions are also discussed.
Want To Reduce Labeling Cost? GPT-3 Can Help

Data annotation is a time-consuming and labor-intensive process for many NLP tasks.Although there exist various methods to produce pseudo data labels, they are often taskspecific and require a decent amount of labeled data to start with.Recently, the immense language model GPT-3 with 175 billion parameters has achieved tremendous improvement across many few-shot learning tasks.In this paper, we explore ways to leverage GPT-3 as a low-cost data labeler to train other models.We find that, to make the downstream model achieve the same performance on a variety of NLU and NLG tasks, it costs 50% to 96% less to use labels from GPT-3 than using labels from humans.Furthermore, we propose a novel framework of combining pseudo labels from GPT-3 with human labels, which leads to even better performance with limited labeling budget.These results present a cost-effective data labeling methodology that is generalizable to many practical applications.