Reviewerly/exHarmony huggingface dataset:
Ecological vulnerability assessment of a China's representative mining city based on hyperspectral remote sensing

Mining cities are clusters of communities that specialized in mining and extractive industries. The extensive mining activities in these cities have stimulated widespread and substantial ecological stresses to the surrounding environment, that significantly jeopardize the health condition of vegetation and human. Given the recent recognition of remote sensing in monitoring large-scale environmental change, we incorporated Ziyuan #1-02D, a recently released hyperspectral remote sensing data, into the ecological vulnerability assessment framework, using Panzhihua city as a case study, which is recognized as one of the most representative mining cities in China. The multi-spectral imaging data was widely applied in previous research. However, with the wide bands, multi-spectral imaging data cannot depict detailed characteristics of spectral targeted. As a result, we introduce indexes from the hyperspectral imaging to ecological vulnerability assessment proposed in this study, which can depict and monitor the growth and restoration of vegetation more accurately. We used the optimum index factor method to select bands from the satellite-based Ziyuan #1-02D data for quantifying vegetation indexes and red edge. Besides, we obtained inventory data, land-use, soil type, and typography of Panzhihua city to reconstruct its ecological vulnerability index (EVI) for 2020 and 2021. Comparing to the multi-spectral data, the ecological vulnerability results from hyperspectral imaging performed better in precision and concentration in EVI values, reaching the conclusions more directly. Specifically, the mining area and the relevant hazard types and impact areas were delineated through intensive fieldwork. Results suggested that the east and west districts, and north of Renhe district suffer great ecological stress, in which we observed intensive coal and metal-related mining industry. The central region, which occupies vanadium titanomagnetite mines, also shows substantial ecological issues, while the other mining industries, such as granite ore did not significantly influence the local environment. Although the newly released satellite-based data only have two-year periods, we still observed improving ecological conditions, with the southeast and west regions showing much lower ecological vulnerability values. The spatial autocorrelation analysis suggested that the high-high clustering region of EVI is located in the east, west, and Renhe districts, primarily due to the mining industries of variations scales. We also found that the clustering of low ecological vulnerable regions, mostly surrounds the vanadium titanomagnetite excavation industry, thanks to the local restoration projects.
MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis

One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.
Underwater object detection algorithm based on attention mechanism and cross-stage partial fast spatial pyramidal pooling

For the routine target detection algorithm in the underwater complex environment to obtain the image of the existence of blurred images, complex background and other phenomena, leading to difficulties in model feature extraction, target miss detection and other problems. Meanwhile, an improved YOLOv7 model is proposed in order to improve the accuracy and real-time performance of the underwater target detection model. The improved model is based on the single-stage target detection model YOLOv7, incorporating the CBAM attention mechanism in the model, so that the feature information of the detection target is weighted and enhanced in the spatial dimension and the channel dimension, capturing the local relevance of feature information, making the model more focused on target feature information, improved detection accuracy, and using the SPPFCSPC module, reducing the computational effort of the model while keeping the model perceptual field unchanged, improved inference speed of the model. After a large number of comparison experiments and ablation experiments, it is proved that our proposed ACFP-YOLO algorithm model has higher detection accuracy compared with Efficientdet, Faster-RCNN, SSD, YOLOv3, YOLOv4, YOLOv5 models and the latest YOLOv7 model, and is more accurate for target detection tasks in complex underwater environments advantages.
Developing an AI-Based Learning System for L2 Learners’ Authentic and Ubiquitous Learning in English Language

Motivated by the rapid development and application of artificial intelligence (AI) technologies in education and the needs of language learners during the COVID-19 pandemic, an AI-enabled English language learning (AIELL) system featuring authentic and ubiquitous learning for the acquisition of vocabulary and grammar in English as a second language (L2) was developed. The aim of this study was to present the developmental process and methods used to design, develop, evaluate, and validate the AIELL system and to distil key design features for English learning in authentic contexts. There were 20 participants in the tests, with three interviewees in the study. Mixed research methods were employed to analyse the data, including a demonstration test, a usability test, and an interview. The quantitative and qualitative data collected and analysed affirmed the validity and usability of the design and helped identify areas for further improvements to the desired features. This study informs the integration of AI into facilitating language teaching and learning guided by the mobile learning principle.
The benefits of blockchain for digital certificates: A multiple case study analysis

Certificates in either hard or soft copies are common documents in our day-to-day activities. However, they are vulnerable to be tampered and inefficiencies exist in the current verification systems. Blockchain technology is considered a feasible solution to protect certificates from being forged and simplify the current verification systems. As an attempt to understand the potential of blockchain for digital certificates in a holistic view, this research aims to conduct a deep analysis of the benefits of blockchain for digital certificates using a multiple case study approach. A benefits analysis model is developed for mapping the benefits of an information system comprehensively and systematically covering benefits in technical, individual, organisational and societal dimensions. Although contextual variations exist, some common benefits are identified such as reduced costs of verification, improved decision making and planning, attracting new customers and supporting business growth. Lastly, future research opportunities in this research field are identified.
A new ensemble learning approach to detect malaria from microscopic red blood cell images

Malaria is a life-threatening parasitic disease spread by infected female Anopheles mosquitoes. After analyzing it, microscopists detect this disease from the sample of microscopic red blood cell images. A professional microscopist is required to conduct the detection process, such an analysis may be time-consuming and provide low-quality results for large-scale diagnoses. This paper develops an ensemble learning-based deep learning model to identify malaria parasites from red blood cell images. VGG16(Retrained), VGG19(Retrained), and DenseNet201(Retrained) are three models that are used in developing the adaptive weighted average ensemble models. To reduce the dispersion of predictions, a max voting ensemble technique is then applied in combination with adaptive weighted average ensemble models. A variety of image processing techniques are utilized including the data augmentation technique to increase the number of data and solve the overfitting problem of the model. Some other approaches of custom CNN, Transfer Learning, and CNN-Machine Learning (ML) classifier techniques are also implemented for comparing their performance with the ensemble learning model. The proposed ensemble learning model provides the best performance among all with an accuracy of 97.92% to classify parasitized and uninfected cells. Therefore, the deep learning model has the potential to diagnose malaria more accurately and automatically.
Data augmentation techniques in natural language processing

Data Augmentation (DA) methods – a family of techniques designed for synthetic generation of training data – have shown remarkable results in various Deep Learning and Machine Learning tasks. Despite its widespread and successful adoption within the computer vision community, DA techniques designed for natural language processing (NLP) tasks have exhibited much slower advances and limited success in achieving performance gains. As a consequence, with the exception of applications of back-translation to machine translation tasks, these techniques have not been as thoroughly explored by the wider NLP community. Recent research on the subject still lacks a proper practical understanding of the relationship between the various existing DA methods. The connection between DA methods and several important aspects of its outputs, such as lexical diversity and semantic fidelity, is also still poorly understood. In this work, we perform a comprehensive study of NLP DA techniques, comparing their relative performance under different settings. We analyze the quality of the synthetic data generated, evaluate its performance gains and compare all of these aspects to previous existing DA procedures.
Automated deidentification of radiology reports combining transformer and “hide in plain sight” rule-based methods

To develop an automated deidentification pipeline for radiology reports that detect protected health information (PHI) entities and replaces them with realistic surrogates "hiding in plain sight."In this retrospective study, 999 chest X-ray and CT reports collected between November 2019 and November 2020 were annotated for PHI at the token level and combined with 3001 X-rays and 2193 medical notes previously labeled, forming a large multi-institutional and cross-domain dataset of 6193 documents. Two radiology test sets, from a known and a new institution, as well as i2b2 2006 and 2014 test sets, served as an evaluation set to estimate model performance and to compare it with previously released deidentification tools. Several PHI detection models were developed based on different training datasets, fine-tuning approaches and data augmentation techniques, and a synthetic PHI generation algorithm. These models were compared using metrics such as precision, recall and F1 score, as well as paired samples Wilcoxon tests.Our best PHI detection model achieves 97.9 F1 score on radiology reports from a known institution, 99.6 from a new institution, 99.5 on i2b2 2006, and 98.9 on i2b2 2014. On reports from a known institution, it achieves 99.1 recall of detecting the core of each PHI span.Our model outperforms all deidentifiers it was compared to on all test sets as well as human labelers on i2b2 2014 data. It enables accurate and automatic deidentification of radiology reports.A transformer-based deidentification pipeline can achieve state-of-the-art performance for deidentifying radiology reports and other medical documents.
Supervised Hebbian learning

In neural network's Literature, Hebbian learning traditionally refers to the procedure by which the Hopfield model and its generalizations store archetypes (i.e., definite patterns that are experienced just once to form the synaptic matrix). However, the term "Learning" in Machine Learning refers to the ability of the machine to extract features from the supplied dataset (e.g., made of blurred examples of these archetypes), in order to make its own representation of the unavailable archetypes. Here, given a sample of examples, we define a supervised learning protocol by which the Hopfield network can infer the archetypes, and we detect the correct control parameters (including size and quality of the dataset) to depict a phase diagram for the system performance. We also prove that, for structureless datasets, the Hopfield model equipped with this supervised learning rule is equivalent to a restricted Boltzmann machine and this suggests an optimal and interpretable training routine. Finally, this approach is generalized to structured datasets: we highlight a quasi-ultrametric organization (reminiscent of replica-symmetry-breaking) in the analyzed datasets and, consequently, we introduce an additional "replica hidden layer" for its (partial) disentanglement, which is shown to improve MNIST classification from 75% to 95%, and to offer a new perspective on deep architectures.
Re-evaluating portfolio diversification and design using cryptocurrencies: Are decentralized cryptocurrencies enough?

This paper investigates the portfolio diversification potential of a pool of cryptocurrencies classified based on their degree of leadership. We employ the mean-variance and the higher-order moments optimization approaches to evaluate the diversification potential of centralized and decentralized cryptocurrencies across multiple frameworks. While theoretical implications of the mean-variance and the higher-order moments optimization approaches are similar, our results suggest that the latter provides a more precise portfolio allocation strategy because it considers investor risk-aversion for each moment. Furthermore, we find that extending the pool of cryptocurrencies achieves marginal diversification benefits due to considerable co-movements among the cryptocurrencies. Moreover, we find that decentralized cryptocurrencies offer greater diversification potential than centralized cryptocurrencies, although centralized cryptocurrencies carry some diversification potential during alt-seasons. In order of their weights, Bitcoin, Chainlink, and Ethereum (all decentralized) offer the highest contribution to portfolio diversification across most portfolio frameworks, while Ethereum offers greater diversification benefits during the alt-seasons.
SMDetector: Small mitotic detector in histopathology images using faster R-CNN with dilated convolutions in backbone model

Breast cancer is one of the most common cancer types among women, and it is a deadly disease caused by the uncontrolled proliferation of cells. Pathologists face a challenging issue of mitotic cell identification and counting during manual detection and identification of cancer. Artificial intelligence can help the medical professional with early, quick, and accurate diagnosis of breast cancer. Consequently, the survival rate will be improved and mortality rate will be decreased. Different deep learning techniques are used in computational pathology for cancer diagnosis. In this study, the SMDetector is proposed which is a deep learning model for detecting small objects such as mitotic and non-mitotic nuclei. This model employs dilated layers in the backbone to prevent small objects from disappearing in the deep layers. The purpose of the dilated layers in this model is to reduce the size gap between the image and the objects it contains. Region proposal network is optimized to accurately identify small objects. The proposed model yielded overall average precision (AP) of 50.31% and average recall (AR) of 55.90% that outperforms the existing standard object detection models on ICPR 2014 (Mitos-Atypia-14) dataset. To best of our knowledge the proposed model is state-of-the-art model for precision and recall of mitotic object detection on ICPR 2014 (Mitos-Atypia-14) dataset. The proposed model has achieved average precision for mitotic nuclei 68.49%, average recall for mitotic nuclei 59.86% and f-measure 63.88%.
A Storage Sustainability Mechanism With Heterogeneous Miners in Blockchain

In current blockchain systems, the transaction fee is often not enough to cover the storage cost, jeopardizing blockchain sustainability in the long run. Such a storage sustainability issue is partially due to miners’ heterogeneous storage costs and users’ low-intensity fee competition. Motivated by these two observations, we propose a Fee and Transaction Expiration Time (FTET) mechanism to alleviate this issue. Specifically, we model the blockchain operation as a three-stage game. In Stage I, the system designer proposes the storage sustainability mechanism. In Stage II, each user decides whether to propose transactions and the corresponding transaction fees. In Stage III, each miner decides which transactions to include in the block. Although the analysis of the heterogeneous miner interaction is technically challenging, we fully solve it in closed-form motivated by how miners select transactions in practice. The equilibrium analysis reveals that high-storage-cost miners admit transactions with fees above a time-increasing threshold. Under the optimal FTET mechanism, the blockchain system can achieve the storage sustainability without any social welfare loss, comparing with the maximum achievable social welfare without the storage sustainability constraint. Moreover, the optimal FTET mechanism achieves a higher social welfare than the fee mechanism in current practice by selectively rejecting some transactions suffering high delays. Finally, we implement a blockchain prototype to compare the performance of the optimal FTET mechanism with the mining round time adjustment (MRTA) mechanism. The optimal FTET mechanism achieves higher social welfare (94.5% on average) and better storage sustainability. We find that more pending transactions may lead to lower transaction fees.
Conflict-based search with D* lite algorithm for robot path planning in unknown dynamic environments

This study proposes a locally observable robot pathfinding algorithm, conflict-based search with D* lite (CBS-D*), to realize automatic and effective pathfinding in mixed environments with dynamic obstacles. This algorithm takes Manhattan distance as the heuristic function and extends the incremental search range from 1order to 3order neighbors. It presents a prejudgment mechanism of collision avoidance and investigates a wait and circuity strategy to promote pathfinding performance. Compared with the D* lite, the experimental results demonstrate that CBS-D* achieves a higher success rate and obstacle avoidance number, and a lower time step. By this collision avoidance mechanism, CBS-D* gives all successes in pathfinding in various dynamic environments, while D* lite may result in some failures. Specifically, CBS-D* has around 31% in the average success rate of pathfinding improved to D* lite in a 32 × 32 map. Furthermore, CBS-D* gives a superiority of self-adaptability and intelligence in unknown dynamic environments.
Research on cloud data encryption algorithm based on bidirectional activation neural network

Recently, it has been found that cloud storage still has security risks, and research on the security and privacy of user data and information is still in the early stage. This paper studies the security risks of cloud data, and designs an image encryption scheme based on neural networks. First, the existing neural network model is improved to obtain a new bidirectional activation (BA) neural network, to establish a many-to-one mapping relationship between the key and the chaotic initial value, to hide the original key of the cloud encryption system, and to improve the security and randomness of the key system. Then, a medical image encryption scheme based on dynamic index scrambling and the M-semitensor product diffusion is proposed. Dynamic index scrambling is more flexible than the traditional approach, and its security and efficiency are improved. The diffusion algorithm adopts the semi tensor product operation, and one of the product matrices is composed of a unitary matrix after Schur decomposition of a plaintext image to effectively resist a selective plaintext attack. Performance analysis shows that the encryption algorithm has high security.
MGL: Mutual Graph Learning for Camouflaged Object Detection

Camouflaged object detection, which aims to detect/segment the object(s) that blend in with their surrounding, remains challenging for deep models due to the intrinsic similarities between foreground objects and background surroundings. Ideally, an effective model should be capable of finding valuable clues from the given scene and integrating them into a joint learning framework to co-enhance the representation. Inspired by this observation, we propose a novel Mutual Graph Learning (MGL) model by shifting the conventional perspective of mutual learning from regular grids to graph domain. Specifically, an image is decoupled by MGL into two task-specific feature maps - one for finding the rough location of the target and the other for capturing its accurate boundary details. Then, the mutual benefits can be fully exploited by reasoning their high-order relations through graphs recurrently. It should be noted that our method is different from most mutual learning models that model all between-task interactions with the use of a shared function. To increase information interactions, MGL is built with typed functions for dealing with different complementary relations. To overcome the accuracy loss caused by interpolation to higher resolution and the computational redundancy resulting from recurrent learning, the S-MGL is equipped with a multi-source attention contextual recovery module, called R-MGL_v2, which uses the pixel feature information iteratively. Experiments on challenging datasets, including CHAMELEON, CAMO, COD10K, and NC4K demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods. The code can be found at https://github.com/fanyang587/MGL.