Reviewerly/exHarmony huggingface dataset:
DenseNet Convolutional Neural Network for Breast Cancer Diagnosis

Breast cancer is a fatal disease, among which, its sub-type invasive (or infiltrating) ductal carcinomas (IDC) dominate the death cases of it.Detecting the features of such disease in an X-ray image by human eyes can be challenged, especially in cancer's early stage.Thus, this study is aimed at developing a system to assist doctors' diagnoses and help the patient to have a preliminary understanding of their own health conditions.More specifically, an IDC detection system based on the Convolutional Neural Network (CNN) is developed, where the DenseNet121 is applied here.In fact, DenseNet 169 and DenseNet 201 are also tested but their performances are not as good as DenseNet121 in this study.As is expected, the system can automatically judge whether the region in a breast histology image is IDC positive or not.This method achieves a high precision, 0.9725 validation accuracy, 0.97 test accuracy, 0.96 recall, 0.96 F1-score, and 0.965 AUC in the sub-dataset selected from Kaggle's Breast Histopathology images dataset.The time to predict 200 images is about 54 seconds and so the average prediction time for a single image is 2.7 s, which is fast enough for practical use.
SegTAD: Precise Temporal Action Detection via Semantic Segmentation

Temporal action detection (TAD) is an important yet challenging task in video analysis. Most existing works draw inspiration from image object detection and tend to reformulate it as a proposal generation - classification problem. However, there are two caveats with this paradigm. First, proposals are not equipped with annotated labels, which have to be empirically compiled, thus the information in the annotations is not necessarily precisely employed in the model training process. Second, there are large variations in the temporal scale of actions, and neglecting this fact may lead to deficient representation in the video features. To address these issues and precisely model TAD, we formulate the task in a novel perspective of semantic segmentation. Owing to the 1-dimensional property of TAD, we are able to convert the coarse-grained detection annotations to fine-grained semantic segmentation annotations for free. We take advantage of them to provide precise supervision so as to mitigate the impact induced by the imprecise proposal labels. We propose a unified framework SegTAD composed of a 1D semantic segmentation network (1D-SSN) and a proposal detection network (PDN). We evaluate SegTAD on two important large-scale datasets for action detection and it shows competitive performance on both datasets.
PA-Seg: Learning From Point Annotations for 3D Medical Image Segmentation Using Contextual Regularization and Cross Knowledge Distillation

The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire. In this paper, we propose to annotate a segmentation target with only seven points in 3D medical images, and design a two-stage weakly supervised learning framework PA-Seg. In the first stage, we employ geodesic distance transform to expand the seed points to provide more supervision signal. To further deal with unannotated image regions during training, we propose two contextual regularization strategies, i.e., multi-view Conditional Random Field (mCRF) loss and Variance Minimization (VM) loss, where the first one encourages pixels with similar features to have consistent labels, and the second one minimizes the intensity variance for the segmented foreground and background, respectively. In the second stage, we use predictions obtained by the model pre-trained in the first stage as pseudo labels. To overcome noises in the pseudo labels, we introduce a Self and Cross Monitoring (SCM) strategy, which combines self-training with Cross Knowledge Distillation (CKD) between a primary model and an auxiliary model that learn from soft labels generated by each other. Experiments on public datasets for Vestibular Schwannoma (VS) segmentation and Brain Tumor Segmentation (BraTS) demonstrated that our model trained in the first stage outperformed existing state-of-the-art weakly supervised approaches by a large margin, and after using SCM for additional training, the model’s performance was close to its fully supervised counterpart on the BraTS dataset.
Toward Adversarial Robustness in Unlabeled Target Domains

In the past several years, various adversarial training (AT) approaches have been invented to robustify deep learning model against adversarial attacks. However, mainstream AT methods assume the training and testing data are drawn from the same distribution and the training data are annotated. When the two assumptions are violated, existing AT methods fail because either they cannot pass knowledge learnt from a source domain to an unlabeled target domain or they are confused by the adversarial samples in that unlabeled space. In this paper, we first point out this new and challenging problem— adversarial training in unlabeled target domain. We then propose a novel framework named Unsupervised Cross-domain Adversarial Training (UCAT) to address this problem. UCAT effectively leverages the knowledge of the labeled source domain to prevent the adversarial samples from misleading the training process, under the guidance of automatically selected high quality pseudo labels of the unannotated target domain data together with the discriminative and robust anchor representations of the source domain data. The experiments on four public benchmarks show that models trained with UCAT can achieve both high accuracy and strong robustness. The effectiveness of the proposed components is demonstrated through a large set of ablation studies. The source code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/DIAL-RPI/UCAT</uri> .
A hybrid chimp optimization algorithm and generalized normal distribution algorithm with opposition-based learning strategy for solving data clustering problems

This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest Error Rate (ER), accelerating the convergence speed, and finding the optimal cluster centers.
A new sentiment analysis method to detect and Analyse sentiments of Covid-19 moroccan tweets using a recommender approach

Since the beginning of the covid-19 crisis, people from all over the world have used social media platforms to publish their opinions, sentiments, and ideas about the coronavirus epidemic and their news. Due to the nature of social networks, users share an immense amount of data every day in a freeway, which gives them the possibility to express opinions and sentiments about the coronavirus pandemic regardless of the time and the place. Moreover, The rapid number of exponential cases globally has become the apprehension of panic, fear, and anxiety among people. In this paper, we propose a new sentiment analysis approach to detect sentiments in Moroccan tweets related to covid-19 from March to October 2020. The proposed model is a recommender approach using the advantages of recommendation systems for classifying each tweet into three classes: positive, negative, or neutral. Experimental results show that our method gives good accuracy(86%) and outperforms the well-known machine learning algorithms. We find also that the sentiments of users changed from period to period, and that the evolution of the epidemiological situation in morocco affects the sentiments of users.
Student and Language Teacher Perceptions of Using a WeChat-Based MALL Program during the COVID-19 Pandemic at a Chinese University

The outbreak of COVID-19 has impacted conventional educational practice in universities worldwide. Chinese universities are no exception. WeChat, a social application widely used in China, has been considered a viable tool for language education. However, the perspectives of Chinese university students and English language teachers in terms of using WeChat for English vocabulary learning and teaching during the pandemic remain unclear. The aim of the present study was twofold: First, it explored Chinese university students’ and language teachers’ opinions of adopting a self-developed WeChat-assisted lexical-learning program (the WALL program) during COVID-19. Second, it gathered their evaluations of the WALL program. To achieve the aim, two sets of semi-structured interviews were used to gather qualitative data about five students’ and three English language teachers’ perceptions at a university in northern China. The results first revealed that the eight participants showed overwhelming opinions in support of adopting the program for vocabulary learning and teaching during the pandemic. In addition, it received mostly positive evaluations. However, the program had two main drawbacks: distracting learning environments and uncertain learning effects. The present study then made recommendations for future WeChat-based language learning and teaching programs. The findings are expected to provide pedagogical insights for tertiary educational institutions, practitioners, and students in the chosen context in order to deal with the future design and implementation of sound MALL-based approaches.
Causal relationships between cryptocurrencies: the effects of sampling interval and sample size

Abstract For this paper, the relationship between seventeen popular cryptocurrencies was analyzed by multivariate Granger causality tests and simple linear regression, using data spanning the period 1 September 2020 to 8 December 2021. The novelty of this work is that it studies the effects of sampling interval and sample size in cryptocurrency markets, which can yield significantly different results. Minute-by-minute, hourly and daily data were collected to examine the Granger causality relationship between cryptocurrencies. It was found that all the currencies demonstrated a significant causality relationship when high frequency (such as minute-by-minute) data was used, in contrast to hourly and daily data. The bigger the sample size, the higher the probability of rejecting the null hypothesis. Hence, the null hypothesis for the Granger causality test can be rejected for minute-by-minute time series data because of too large a sample size. Granger causality test results for hourly and daily data indicated that Bitcoin, Ethereum Classic, and Neo were leading indicators among the cryptocurrencies included in the research. In addition, according to simple linear regression analysis, the short term marginal effect of Bitcoin plays an important role by creating significant impacts on other cryptocurrencies.
Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment

Visual (image, video) quality assessments can be modelled by visual features in different domains, e.g., spatial, frequency, and temporal domains. Perceptual mechanism in the human visual system (HVS) play a crucial role in the generation of quality perception. This paper proposes a general framework for no-reference visual quality assessment using efficient windowed transformer architectures. A lightweight module for multi-stage channel attention is integrated into the Swin (shifted window) Transformer. Such module can represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. Meanwhile, representative features for image quality perception in the spatial and frequency domains can also be derived from the IQA model, which are then fed into another windowed transformer architecture for video quality assessment (VQA). The VQA model efficiently reuses attention information across local windows to tackle the issue of expensive time and memory complexities of original transformer. Experimental results on both large-scale IQA and VQA databases demonstrate that the proposed quality assessment models outperform other state-of-the-art models by large margins.
On the voluntariness of public health apps: a European case study on digital contact tracing

As evidenced during the COVID-19 pandemic, there is a growing reliance on smartphone apps such as digital contact tracing apps and vaccination passports to respond to and mitigate public health threats. In light of the European Commission's guidance, Member States typically offer such apps on a voluntary, ‘opt-in’ basis. In this paper, we question the extent to which the individual choice to use these apps – and similar future technologies – is indeed a voluntary one. By explicating ethical and legal considerations governing the choice situations surrounding the use of smartphone apps, specifically those related to the negative consequences that declining the use of these apps may have (e.g. loss of opportunities, social exclusion, stigma), we argue that the projected downsides of refusal may in effect limit the liberty to decline for certain subpopulations. To mitigate these concerns, we recommend three categories of approaches that may be employed by governments to safeguard voluntariness.
Discrete Cosine Transformed Images Are Easy to Recognize in Vision Transformers

Deep learning models for image classification with adequate parameters show excellent classification performance because they can effectively extract the features of input images. On the other hand, there is a limit to the abilities of deep learning models to interpret images using only spatial information because an image is a signal with great spatial redundancy. Therefore, in this study, the discrete cosine transform was applied to an input image in units of an N×N block size to allow the deep learning model to employ both frequency and spatial information. The proposed method was implemented and verified by selecting a vision transformer using a 16×16 nonoverlapping patch as a baseline and training various datasets of Cifar-10, Cifar-100, and Tiny-ImageNet from the very beginning without pre-trained weights. The experimental results showed that the top-1 accuracy is improved by approximately 3-5% for every dataset with little increase in computational cost.
Weakly Supervised Concept Map Generation Through Task-Guided Graph Translation

Recent years have witnessed the rapid development of concept map generation techniques due to their advantages in providing well-structured summarization of knowledge from free texts. Traditional unsupervised methods do not generate task-oriented concept maps, whereas deep generative models require large amounts of training data. In this work, we present GT-D2G (Graph Translation-based Document To Graph), an automatic concept map generation framework that leverages generalized NLP pipelines to derive semantic-rich initial graphs, and translates them into more concise structures under the weak supervision of downstream task labels. The concept maps generated by GT-D2G can provide interpretable summarization of structured knowledge for the input texts, which are demonstrated through human evaluation and case studies on three real-world corpora. Further experiments on the downstream task of document classification show that GT-D2G beats other concept map generation methods. Moreover, we specifically validate the labeling efficiency of GT-D2G in the label-efficient learning setting and the flexibility of generated graph sizes in controlled hyper-parameter studies.
Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport

Sediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore, validity of sediment transport models is linked to the range of data used for the model development. Existing design models were established on the limited data ranges. Thus, the present study aimed to utilize all experimental data available in the literature, including recently published datasets that covered an extensive range of hydraulic properties. Extreme learning machine (ELM) algorithm and generalized regularized extreme learning machine (GRELM) were implemented for the modeling, and then, particle swarm optimization (PSO) and gradient-based optimizer (GBO) were utilized for the hybridization of ELM and GRELM. GRELM-PSO and GRELM-GBO findings were compared to the standalone ELM, GRELM, and existing regression models to determine their accurate computations. The analysis of the models demonstrated the robustness of the models that incorporate channel parameter. The poor results of some existing regression models seem to be linked to the disregarding of the channel parameter. Statistical analysis of the model outcomes illustrated the outperformance of GRELM-GBO in contrast to the ELM, GRELM, GRELM-PSO, and regression models, although GRELM-GBO performed slightly better when compared to the GRELM-PSO counterpart. It was found that the mean accuracy of GRELM-GBO was 18.5% better when compared to the best regression model. The promising findings of the current study not only may encourage the use of recommended algorithms for channel design in practice but also may further the application of novel ELM-based methods in alternative environmental problems.
Quantum Key Distribution with a Continuous-Wave-Pumped Spontaneous-Parametric-Down-Conversion Heralded Single-Photon Source

Quantum key distribution (QKD) protocols utilize single photons for key exchanging. Heralded single-photon sources (HSPSs) with spontaneous parametric down-conversion (SPDC) is a practical and effective way of producing single photons of high fidelity, bringing extra performance especially at long distances. A pulsed laser is usually used as the pump for HSPS QKD, but it has disadvantages in operational stability and practicality compared to a cw one. On the other hand, a precise model describing the performance of HSPS with a cw pump is still missing. It is inaccurate and unsafe to apply the key rate model of the pulse-pumped HSPS QKD directly to the cw one, and it also overlooks the timing jitter and dead time of the single-photon detectors. In this paper, we study the cw case in detail, including practical imperfections of the experimental instruments. Our model shows that a cw-pumped setup can reach a much longer transmission distance (31 % longer) than the fastest pulsed HSPS QKD system at present, together with other advantages such as higher operational stability and better spectral characteristics.
Quantum enhanced radio detection and ranging with solid spins

Abstract The accurate radio frequency (RF) ranging and localizing of objects has benefited the researches including autonomous driving, the Internet of Things, and manufacturing. Quantum receivers have been proposed to detect the radio signal with ability that can outperform conventional measurement. As one of the most promising candidates, solid spin shows superior robustness, high spatial resolution and miniaturization. However, challenges arise from the moderate response to a high frequency RF signal. Here, by exploiting the coherent interaction between quantum sensor and RF field, we demonstrate quantum enhanced radio detection and ranging. The RF magnetic sensitivity is improved by three orders to 21 $${{{{{{{\rm{pT}}}}}}}}/\sqrt{{{{{{{{\rm{Hz}}}}}}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>pT</mml:mi> <mml:mo>/</mml:mo> <mml:msqrt> <mml:mrow> <mml:mi>Hz</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> , based on nanoscale quantum sensing and RF focusing. Further enhancing the response of spins to the target’s position through multi-photon excitation, a ranging accuracy of 16 μ m is realized with a GHz RF signal. The results pave the way for exploring quantum enhanced radar and communications with solid spins.
Blood quality evaluation <i>via</i> on-chip classification of cell morphology using a deep learning algorithm

The quality of red blood cells (RBCs) in stored blood has a direct impact on the recovery of patients treated by blood transfusion, which directly reflects the quality of blood. The traditional means for blood quality evaluation involve the use of reagents and multi-step and time-consuming operations. Here, a low-cost, multi-classification, label-free and high-precision method is developed, which combines microfluidic technology and a deep learning algorithm together to recognize and classify RBCs based on morphology. The microfluidic channel is designed to effectively and controllably solve the problem of cell overlap, which has a severe negative impact on the identification of cells. The object detection model in the deep learning algorithm is optimized and used to recognize multiple RBCs simultaneously in the whole field of view, so as to classify them into six morphological subcategories and count the numbers in each subgroup. The mean average precision of the developed object detection model reaches 89.24%. The blood quality can be evaluated by calculating the morphology index (MI) according to the numbers of cells in subgroups. The validation of the method is verified by evaluating three blood samples stored for 7 days, 21 days and 42 days, which have MIs of 84.53%, 73.33% and 24.34%, respectively, indicating good agreement with the actual blood quality. This method has the merits of cell identification in a wide channel, no need for single cell alignment as the image cytometry does and it is not only applicable to the quality evaluation of RBCs, but can also be used for general cell identifications with different morphologies.