Reviewerly/exHarmony huggingface dataset:
An Adaptive Offset Activation Function for CNN Image Classification Tasks

The performance of the activation function in convolutional neural networks is directly related to the model’s image classification accuracy. The rectified linear unit (ReLU) activation function has been extensively used in image classification models but has significant shortcomings, including low classification accuracy. The performance of a series of parametric activation functions has made parameter addition a popular research avenue for improving the performance of activation functions in recent years, and excellent progress has been achieved. Existing parametric activation functions often focus on assigning a different slope to the negative part of the activation function and still involve the negative value alone in the activation function calculation, without considering the impact of linking the negative value to the positive value on the performance of the activation function. As a result, this work proposes a novel parametric right-shift activation function, the adaptive offset activation function (AOAF). By inserting an adaptive parameter (the mean value of the input feature tensor) and two custom ReLU parameters, the negative parameters previously driven to zero by ReLU can be turned into positive parameters with lower weight and participate in CNN feature extraction. We compared the performance of the suggested activation function to the performance of a selection of typical activation functions using four distinct public datasets. Compared with ReLU, the average classification accuracy of our proposed activation function improved by 3.82%, 0.6%, 1.02%, and 4.8% for the four datasets, respectively.
SHFL: K-Anonymity-Based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems

Dynamic and smart Internet of Things (IoT) infrastructures allow the development of smart healthcare systems, which are equipped with mobile health and embedded healthcare sensors to enable a broad range of healthcare applications. These IoT applications provide access to the clients’ health information. However, the rapid increase in the number of mobile devices and social networks has generated concerns regarding the secure sharing of a client’s location. In this regard, federated learning (FL) is an emerging paradigm of decentralized machine learning that guarantees the training of a shared global model without compromising the data privacy of the client. To this end, we propose a K-anonymity-based secure hierarchical federated learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates hierarchically with multiple directly and indirectly connected devices. In particular, the proposed SHFL formulates the hierarchical clusters of location-based services to achieve distributed FL. In addition, the proposed SHFL utilizes the K-anonymity method to hide the location of the cluster devices. Finally, we evaluated the performance of the proposed SHFL by configuring different hierarchical networks with multiple model architectures and datasets. The experiments validated that the proposed SHFL provides adequate generalization to enable network scalability of accurate healthcare systems without compromising the data and location privacy.
Mineral mapping in the Duolong porphyry and epithermal ore district, Tibet, using the Gaofen-5 satellite hyperspectral remote sensing data

Remote sensing technology is an exploration method that can directly map distribution of alteration minerals or zones associated with different types of mineral systems. Hyperspectral remote sensing can identify key pathfinder minerals and compositional variations within minerals. The Duolong porphyry-epithermal ore district, which has proven reserves of approximately 20 million tons of Cu and 420 tons of Au, is part of an emerging world-class metallogenic area in the western part of the Bangong Co-Nujiang metallogenic belt, Tibet, China. The Gaofen-5 hyperspectral (GF-HS) reflectance data were used to map the distribution patterns of hydrothermal alteration and to locate areas potentially containing mineral deposits in the Duolong ore district (DOD). Based on the mixture tuned matched filtering (MTMF) technique and the absorption feature wavelength positions of the minerals, an alteration map was generated using GF-HS data for mineral mapping in the DOD with sparse vegetation. The widely distributed advanced argillic alteration (alunite + pyrophyllite + kaolinite ± dickite), phyllic alteration (high content of Al-rich white mica), propylitic alteration (Mg-rich chlorite), and limited argillic alteration (kaolinite ± dickite ± white mica) were mapped in detail. The distribution patterns of alteration in known deposits were fairly consistent with the mineralogical information obtained in previous studies. In addition, we identified potential mineralization areas with characteristics similar to the Duobuza, Bolong and Naruo porphyry deposits.
Development of surveillance robot based on face recognition using Raspberry-PI and IOT

The advancement in recent technologies such as artificial intelligence (AI), computer vision (CV) and internet of things (IoT) extends to various areas, especially surveillance systems that require real-time facial recognition processing to ensure safety. In fact, mobile robots are widely used in surveillance systems to perform dangerous tasks that humans cannot. In this context, we propose a prototype of a low-cost mobile surveillance robot based on Raspberry PI 4 which can be integrated into any industrial area. This intelligent robot checks about the presence of intruder using IoT and face recognition technology. The system is equipped with passive infrared (PIR) sensor and camera to record live streaming video and photos, which are transmitted to the room control via IoT. Thanks to facial recognition algorithms, the system can distinguish between company personnel and intruders. Haar Cascade Classifier and LBPH (Local Binary Pattern Histogram) algorithm are used in image processing to detect and identify individuals. When a stranger is detected, alert notifications and email with captured image are sent to the room control via IoT. A web interface was developed to remotely control the designed robot via a WiFi connection. This application allows monitoring of a wide area and can improve the robot's perception system. Performance evaluations will be conducted to demonstrate the effectiveness and robustness of the face recognition algorithms used in this framework.
Compositional Classification of Financial Statement Profiles: The Weighted Case

This article classifies petrol retail companies in Spain based on their financial ratios using the compositional data analysis (CoDA) methodology. This methodology solves the most common distributional problems encountered in the statistical analysis of financial ratios. The main purpose of this article is to show that with the CoDA methodology, accounting figures presenting low values can have a disproportional influence on classification. This problem can be attenuated by applying weighted CoDA, which is a novelty in the financial statement analysis field. The suggested weight of each accounting figure is proportional to its arithmetic mean. The results of Ward clustering show that after weighting, the contributions of the accounting figures to the total variance and to the clustering solution are more balanced, and the clusters are more interpretable. Four distinct financial profiles are identified and related to non-financial variables. Only one of the profiles represents companies in financial distress, with low turnover, low return on assets, high indebtedness, and low liquidity. Further developments include alternative weighting schemes.
Discursive Strategies of Blaming: The Language of Judgment and Political Protest Online

Modern politics is permeated by blame games-symbolic struggles over the blameworthiness or otherwise of various social actors. In this article, we develop a framework for identifying different strategies of blaming that protesters use on social media to criticize and delegitimize governments and political leaders. We draw on the systemic functional linguistic theory of Appraisal to distinguish between blame attributions based on negative judgments of the target's (1) capacity, such as references to their incompetence and policy failures; (2) veracity, questioning their truthfulness or honesty via references to deceitful character or dishonest acts and utterances; (3) propriety, questioning their moral standing by references to, for instance, corruption; and (4) tenacity, suggesting that the politicians are not dependable due to, for example, dithering. We add to this a further threefold distinction based on whether blaming is focused on the target's (1) bad character, (2) bad behavior, or (3) negative outcomes that the target either caused or did not prevent from happening. To illustrate the approach, we analyze a corpus of replies by Twitter users to tweets by British government ministers about two highly contentious issues, Covid-19 and Brexit, in 2020-2021. We suggest that the methodology outlined here could provide a useful avenue for systematically revealing and comparing a variety of realizations of blaming in large datasets of online conflict talk, thereby providing a more fine-grained understanding of the practices of protest and delegitimation in modern politics.
Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network

This study proposes a 3D attitude estimation algorithm using the RMPE algorithm coupled with a deep neural network that combines human pose estimation and action recognition, which provides a new idea for basketball auxiliary training. Compared with the traditional single-action recognition method, the present method makes the recognition accuracy better and the display effect more intuitive. The flipped classroom teaching mode based on this algorithm is applied to the college sports basketball optional course to explore the influence of this teaching mode on the classroom teaching effect. Compared with the evaluation index of action recognition, the experimental results of various action recognition methods and datasets are compared and analyzed, and it is verified that the method has a good recognition effect. The values of Topi and Top5 of the proposed method are 42.21% and 88.77%, respectively, which are 10.61% and 35.09% higher than those of the Kinetics-skeleton dataset. However, compared with the NTU RGM dataset, the recognition rate of Topi is significantly reduced. Compared with the traditional single-action recognition method, this method has better recognition accuracy and a more intuitive display effect. The fusion method of human posture estimation and motion recognition provides a new idea for basketball auxiliary training.
A Survey of Deep Learning for Electronic Health Records

Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to capture and extract valuable information from medical data. Many deep learning methods are conducted to handle various subtasks of EHR from the view of information extraction and representation learning. This survey designs a taxonomy to summarize and introduce the existing deep learning-based methods on EHR, which could be divided into four types (Information Extraction, Representation Learning, Medical Prediction and Privacy Protection). Furthermore, we summarize the most recognized EHR datasets, MIMIC, eICU, PCORnet, Open NHS, NCBI-disease and i2b2/n2c2 NLP Research Data Sets, and introduce the labeling scheme of these datasets. Furthermore, we provide an overview of deep learning models in various EHR applications. Finally, we conclude the challenges that EHR tasks face and identify avenues of future deep EHR research.
Deep Muti-Modal Generic Representation Auxiliary Learning Networks for End-to-End Radar Emitter Classification

Radar data mining is the key module for signal analysis, where patterns hidden inside of signals are gradually available in the learning process and its superiority is significant for enhancing the security of the radar emitter classification (REC) system. Owing to the disadvantage that radio frequency fingerprinting (RFF) caused by the imperfection of emitter’s hardware is difficult to forge, current deep-learning REC methods based on deep-learning techniques, e.g., convolutional neural network (CNN) and long short term memory (LSTM) are difficult to capture the stable RFF features. In this paper, an online and non-cooperative multi-modal generic representation auxiliary learning REC model, namely muti-modal generic representation auxiliary learning networks (MGRALN), is put forward. Multi-modal means that multi-domain transformations are unified to a generic representation. After this, the representation is employed to facilitate mining the implicit information inside of the signals and to perform the better model robustness, which is achieved by using the available generic genenation to guide the network training and learning. Online means the learning process of REC is only once and the REC is end-to-end. Non-cooperative denotes no demodulation techniques are used before the REC task. Experimental results on the measured civil aviation radar data demonstrate that the proposed method enables one to achieve superior performance.
CMFCUNet: cascaded multi-scale feature calibration UNet for pancreas segmentation

Segmenting the pancreas from abdominal CT scans is challenging since it often takes up a relatively small region. Researchers suggested leveraging coarse-to-fine approaches to cope with this challenge. However, the coarse-scaled segmentation and the fine-scaled segmentation are either trained separately utilizing the coordinates located by the coarse-scaled segmentation mask to crop the fine-scaled segmentation input, or trained jointly utilizing the coarse-scaled segmentation mask to enhance the fine-scaled segmentation input. We argued that these two solutions are complementary to some extent and can promote each other to improve the performance of pancreas segmentation. In addition, the backbone in the coarse-scaled segmentation and fine-scaled segmentation is mostly based on UNet or UNet-like networks, where the multi-scale features transmitted from the encoder to the decoder have not been explored for vertical calibration before. In this paper, we propose a cascaded multi-scale feature calibration UNet (CMFCUNet) for pancreas segmentation where the multi-scale features in the backbone of each scaled segmentation are calibrated vertically in a pixel-wise fashion. Besides, the coarse-scaled segmentation and the fine-scaled segmentation are connected by leveraging a designed dual enhancement module (DEM). Experiments are first conducted on the public NIH pancreas dataset. First, when leveraging CMFCUNet, our method increased by over 3% on the Jaccard index (JI) and nearly 1% on dice similarity coefficient (DSC) which surpasses all existing pancreas segmentation approaches. In addition, our experiments demonstrate that CMFCUNet improved the coarse-to-fine segmentation framework and outperformed the mainstream coarse-to-fine pancreas segmentation approaches. Furthermore, we also conducted ablation studies to analyze the effectiveness of the backbone (MFCUNet) and the DEM. In addition to the experiments on the NIH dataset, we also experimentally demonstrate the excellent generalization of our method on the MSD pancreas dataset.
Improving GAN Learning Dynamics for Thyroid Nodule Segmentation

Thyroid nodules are lesions requiring diagnosis and follow-up. Tools for detecting and segmenting nodules can help physicians with this diagnosis. Besides immediate diagnosis, automated tools can also enable tracking of the probability of malignancy over time. This paper demonstrates a new algorithm for segmenting thyroid nodules in ultrasound images. The algorithm combines traditional supervised semantic segmentation with unsupervised learning using GANs. The hybrid approach has the potential to upgrade the semantic segmentation model's performance, but GANs have the well-known problems of unstable learning and mode collapse. To stabilize the training of the GAN model, we introduce the concept of closed-loop control of the gain on the loss output of the discriminator. We find gain control leads to smoother generator training and avoids the mode collapse that typically occurs when the discriminator learns too quickly relative to the generator. We also find that the combination of the supervised and unsupervised learning styles encourages both low-level accuracy and high-level consistency. As a test of the concept of controlled hybrid supervised and unsupervised semantic segmentation, we introduce a new model named the StableSeg GAN. The model uses DeeplabV3+ as the generator, Resnet18 as the discriminator, and uses PID control to stabilize the GAN learning process. The performance of the new model in terms of IoU is better than DeeplabV3+, with mean IoU of 81.26% on a challenging test set. The results of our thyroid nodule segmentation experiments show that StableSeg GANs have flexibility to segment nodules more accurately than either comparable supervised segmentation models or uncontrolled GANs.
Symmetry-based regularization in deep breast cancer screening

Breast cancer is the most common and lethal form of cancer in women. Recent efforts have focused on developing accurate neural network-based computer-aided diagnosis systems for screening to help anticipate this disease. The ultimate goal is to reduce mortality and improve quality of life after treatment. Due to the difficulty in collecting and annotating data in this domain, data scarcity is - and will continue to be - a limiting factor. In this work, we present a unified view of different regularization methods that incorporate domain-known symmetries in the model. Three general strategies were followed: (i) data augmentation, (ii) invariance promotion in the loss function, and (iii) the use of equivariant architectures. Each of these strategies encodes different priors on the functions learned by the model and can be readily introduced in most settings. Empirically we show that the proposed symmetry-based regularization procedures improve generalization to unseen examples. This advantage is verified in different scenarios, datasets and model architectures. We hope that both the principle of symmetry-based regularization and the concrete methods presented can guide development towards more data-efficient methods for breast cancer screening as well as other medical imaging domains.
Multi-Aspect co-Attentional Collaborative Filtering for extreme multi-label text classification

The extreme multi-label text classification (XMTC) keeps attracting researchers' attention due to its wide application. Recent studies have been trying to enhance text representation or reduce the number of labels to optimize the lack of information in a text or the sparsity of the possibility vector. In the social recommendation field, a similar problem has already been defined and studied extensively, the methods from which can be adopted in XMTC to identify matching relations in large datasets accurately. Thus, we proposed a general architecture enhanced by a pre-defined global nearest label neighbor group for XMTC, which reformulates the learning task to an interaction function between document and label by a multi-layer perceptron. Further, with co-attention mechanism and neural collaborative filtering, our architecture learns informative label representation enhanced by document-specific label group vector and label-specific text feature vector, which builds an effective interaction function to get a matching score. Through extensive experiments comparing various models and ablation studies, results show that our proposed architecture outperforms most existing methods for XMTC and significantly improves the performance of elemental document encoders.
Linear discriminant analysis with generalized kernel constraint for robust image classification

Linear discriminant analysis (LDA) as a classical supervised dimensionality reduction method has shown powerful capability in various image classification tasks. The purpose of LDA seeks an optimal linear transformation that maps the original data to a low-dimensional space. Inspired by the fact that the kernel trick can capture the nonlinear similarity of features, we propose a novel generalized distance constraint dubbed intra-class and inter-class kernel constraint (IIKC). The proposed IIKC explicitly models the category kernel distance and focuses on helping the original LDA capture more discriminant features in order to further improve the separability and magnitude difference between nearby data points. Our proposed method with IIKC aims to achieve maximum category separability by minimizing the intra-class kernel distances as well as maximizing the inter-class kernel distance, simultaneously. Extensive experimental results on six publicly available benchmark databases illustrate that the LDA-based methods embedded with the proposed IIKC significantly improve the discrimination ability and achieve a better classification performance than the original and state-of-the-art LDA algorithms.
Ensemble learning framework for image retrieval via deep hash ranking

Deep hashing combines feature extraction or representation with hash coding jointly, which can significantly improve the speed of large-scale image retrieval. However, we notice that compared with traditional retrieval methods, due to the reduction of dimension and information loss, the retrieval performance of binaryhash coding has declined to a certain extent. Most hash retrieval algorithms focus on the semantic similarity between image pairs, and ignore the ranking information between the returned samples. The returned samples should not only match the retrieved samples, but also rank the correct samples in front of the returned list. In addition, the performance difference of the deep model used in deep hash retrieval will also limit the efficiency of retrieval. To address such problem, we proposed an ensemble deep neural model robust framework for image retrieval, which can learn compact hash codes containing rich semantic information through hash constraints. The ensemble strategy is introduced, and the weighted voting is applied to integrate the ranking list. Comprehensive experiments on three benchmark datasets show that the proposed method achieves very competitive results. Codes are available at https://github.com/lidonggen-123/Ensemble_Deephash_Image_Retrieval.