Reviewerly/exHarmony huggingface dataset:
Mammo-SAM: Adapting Foundation Segment Anything Model for Automatic Breast Mass Segmentation in Whole Mammograms

Automated breast mass segmentation from mammograms is crucial for assisting radiologists in timely and accurate breast cancer diagnosis. Segment Anything Model (SAM) has recently demonstrated remarkable success in natural image segmentation, suggesting its potential for enhancing artificial intelligence-based automated diagnostic systems. Unfortunately, we observe that the zero-shot performance of SAM in mass segmentation falls short of usability. Therefore, fine-tuning SAM for transfer learning is necessary. However, full-tuning is cost-intensive for foundation models, making it unacceptable in clinical practice. To tackle this problem, in this paper, we propose a parameter-efficient fine-tuning framework named Mammo-SAM, which significantly improves the performance of SAM on the challenging task of mass segmentation. Our key insight includes a tailored adapter to explore multi-scale features and a re-designed CNN-style decoder for precise segmentation. Extensive experiments on the public datasets CBIS-DDSM and INbreast demonstrate that our proposed Mammo-SAM surpasses existing mass segmentation methods and other tuning paradigms designed for SAM, achieving new state-of-the-art performance.
Recognition of mineralization-related anomaly patterns through an autoencoder neural network for mineral exploration targeting

In mineral potential mapping, supervised machine learning algorithms have shown great promise in delineating and prioritizing potential areas. However, since mineralization being a relatively rare geological event, most supervised machine learning-based models face substantial challenges in properly identifying prospective areas. Data sets with strongly imbalanced distributions of the target variable (deposits) and insufficient training data sets impose obstacles to these kinds of models which can significantly impact adversely on the performance of the models. Moreover, in some cases, negative training data sets as the non-deposit locations aren't really true negative data, which cause higher uncertainty in a mineral potential map. In this study, for handling these challenges the deep autoencoder neural network is adopted. The autoencoder can be trained to reconstruct geospatial data set in totally unsupervised manner and identify prospective areas based on the reconstruction error, where higher error corresponds with areas of higher mineral potential. In order to confirm the efficiency of the autoencoder algorithm in mineral potential modeling, the model was compared with a popular data-driven approach that assigned a weight to the evidence layer by using a concentration-area (C-A) fractal model and a prediction-area (P-A) plot, and combined them using a multi-class index overlay method. Receiver operating characteristic (ROC) curve, success-rate curve, and P-A plot were adopted to evaluate the predictive ability of Fe prospectivity models pertaining to the Esfordi district of Iran. Also, we use an area under the ROC curve (AUC) and partial AUC (pAUC) to quantitatively evaluate the overall and sensitivity performance of models, respectively.
Portrait age recognition method based on improved ResNet and deformable convolution

&lt;abstract&gt; &lt;p&gt;ResNet-based correlation models excel in age recognition algorithms, but specific age recognition research is currently limited and often plagued by substantial errors. We introduce an enhanced portrait age recognition algorithm based on ResNet, using CORAL (consistent rank logits) rank consistent ordered regression instead of traditional classification to predict precise ages. We further improve this approach by incorporating DCN (deformable convolution), resulting in the DCN-R model. DCN dynamically adjusts convolution kernels for diverse faces, improving accuracy and robustness. We tested DCN-R34 and DCN-R50 against the SOTA model, achieving better results with the same complexity. This reduces the computational load while maintaining or enhancing performance.&lt;/p&gt; &lt;/abstract&gt;
Enhancing Fashion Classification with Vision Transformer (ViT) and Developing Recommendation Fashion Systems Using DINOVA2

As e-commerce platforms grow, consumers increasingly purchase clothes online; however, they often need clarification on clothing choices. Consumers and stores interact through the clothing recommendation system. A recommendation system can help customers to find clothing that they are interested in and can improve turnover. This work has two main goals: enhancing fashion classification and developing a fashion recommendation system. The main objective of fashion classification is to apply a Vision Transformer (ViT) to enhance performance. ViT is a set of transformer blocks; each transformer block consists of two layers: a multi-head self-attention layer and a multilayer perceptron (MLP) layer. The hyperparameters of ViT are configured based on the fashion images dataset. CNN models have different layers, including multi-convolutional layers, multi-max pooling layers, multi-dropout layers, multi-fully connected layers, and batch normalization layers. Furthermore, ViT is compared with different models, i.e., deep CNN models, VGG16, DenseNet-121, Mobilenet, and ResNet50, using different evaluation methods and two fashion image datasets. The ViT model performs the best on the Fashion-MNIST dataset (accuracy = 95.25, precision = 95.20, recall = 95.25, F1-score = 95.20). ViT records the highest performance compared to other models in the fashion product dataset (accuracy = 98.53, precision = 98.42, recall = 98.53, F1-score = 98.46). A recommendation fashion system is developed using Learning Robust Visual Features without Supervision (DINOv2) and a nearest neighbor search that is built in the FAISS library to obtain the top five similarity results for specific images.
An oversampling method based on differential evolution and natural neighbors

The classification problem of imbalanced data is a research focus in machine learning. An effective method for solving the class-imbalance problem is to generate synthetic samples for minority class data. Popular methods for synthesize samples include SMOTE, variants of SMOTE, and oversampling methods applying metaheuristic algorithms. Recently, an oversampling method DEBOHID based on differential evolutionary algorithm has been proposed. It uses nearest neighbors of the minority class to synthesize samples for balancing the data set. However, samples synthesized by nearest neighbors are easily interfered by noisy samples. Therefore, we proposed an oversampling method based on natural neighbors, called NaN-DEBOHID. In NaN-DEBOHID, minority samples are divided into dense samples and outliers according to the number of natural neighbors. Samples of different types are processed by different methods. The main advantages of NaN-DEBOHID are concluded as follows: (a) it finds samples that better represent the minority class distribution by applying natural neighbors; (b) it creates synthetic samples that showed better consistency with the surrounding samples by the DEBOHID method; (c) it removes noise samples of the minority class to enhance the classification performance. For experiments, Support Vector Machine (SVM) and k-Nearest Neighbor (kNN) are used as classifiers. The results indicated that NaN-DEBOHID performed competitively in terms of Accuracy, F-measure and AUC.
An Adaptive Neural Network Model for Clinical Face Mask Detection

Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This area is now very prominent due to the coronavirus disease pandemic and the post-pandemic phases where wearing of clinical face mask is imminent. Wearing a protective face mask in public and a clinical face mask in a hospital environment slows the spread of the virus and any other respiratory-related contagious diseases, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people’s faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed technique utilized the Faster R-CNN model on the Inception V3 backbone to reduce system complexity and dataset requirements. The model was trained and validated with very few datasets and evaluation results show an overall accuracy of 96% regardless of skin tone.
Large-Scale Rooftop Solar Photovoltaic Power Production Potential Assessment: A Case Study for Tehran Metropolitan Area, Iran

The exponential growth of population and industries has brought about an increase in energy consumption, causing severe climatic and environmental problems. Therefore, the move towards green renewable energy is being ever more intensified. This study aims at estimating the rooftop solar power production for Tehran, the capital city of Iran, using a Geospatial Information System (GIS) to assess the big data of city building parcels. Tehran is faced with severe air pollution due to its excessive fossil fuel usage, and its electricity demand is increasing. As a result, this paper attempts to provide the quantified solar power potential of city roof tops for policymakers and authorities in order to facilitate decision-making in relation to integrating renewable energies into the power production infrastructure. The results shows that approximately 3000 GWh (more than 14% of the total electric energy consumption) of solar power can be produced by the rooftop PV installations in Tehran. The potential nominal power of rooftop PV installations is estimated to be more than 2000 MW, which is four times the current installed PV capacity of the whole country. The findings of the study suggest that there is great potential hidden on the rooftops of the city, which can be utilized to assist the power systems of the city in the longer run for a more sustainable future.
Desensitization to hate speech: Examination using heart rate measurement

Exposure to hate speech (HS) leads to desensitization of listeners. Yet, most evidence of this process has been obtained using self-report measures. In this paper, we examined desensitization to HS using an unobtrusive, psychophysiological measure. In an experimental electrocardiogram study (N = 56), we observed heart rate (HR) deceleration after reading comments that contained HS. This suggested a substantive psychophysiological reaction of participants to hateful comments. However, such HR deceleration was not observed among participants preexposed to HS. People exposed to hateful comments thus appeared to show different HR responses to HS compared to people who were not previously exposed to such comments. Consequently, not only does frequent exposure to HS influence an individual's beliefs as observed in earlier studies, but it also impacts psychophysiological reactions to derogatory language.
Lightweight fully connected network-based fast CU size decision for video-based point cloud compression

Video-based point cloud compression (V-PCC) utilizes high efficiency video coding (HEVC) to compress geometry and attribute videos generated from dynamic point cloud projection. However, the HEVC exhaustive coding unit (CU) size decision process is complex and hinders the real-time application of V-PCC. To reduce the coding complexity of V-PCC, this paper proposes a method that combined hand-crafted features and lightweight neural network to accurately predict the best CU partition in advance. First, we extract hand-crafted features, including direct features (DFs) and indirect feature (IF), as mixed features. DFs are simple and require no additional calculation, while IF is obtained indirectly by transforming the global and local distortions of the CU extracted before size decision determination. Second, we propose a lightweight fully connected network (LFCN) as the backbone network, two feature types are used as inputs to the LFCN to predict whether the CU should be split into sub-CUs, and the LFCN can be fully integrated into the encoder with only about 1.58KB of additional parameters. Experimental results show that the proposed method reduces coding complexity by an average of 51.2% while Luma’s BD-TotalRate only increases by 0.1% on average under the All Intra (AI) configuration.
Mineral Resources of the Russian Nuclear Industry and Isolation of Radioactive Waste

Abstract This article considers tasks needed to ensure sustainable development of the Russian nuclear industry, which are aimed for solving the problems of the initial and final stages of the nuclear fuel cycle, from reproduction of the uranium mineral-resource base to handling of liquid high-level waste of spent nuclear fuel reprocessing. The trends in the development of the mineral-resource complex are analyzed in connection with the projected growth of electricity generation at nuclear power plants. New approaches to the allocation and substantiation of prospecting areas based on the models of mineral-forming systems of uranium deposits using remote-sensing data, GIS modeling, and neural-network technologies are proposed on the example of the territory of southeastern Transbaikal within the framework of current prognostic and mineragenic studies. A detailed analysis of trends in solving the problems of the back end stage of the nuclear fuel cycle is carried out. The use of phosphate glass ceramics with monazite is proposed for isolation and subsequent disposal of the REE–actinide fraction of high-level waste.
Blockchain and Machine Learning Empowered Internet of Things Applications: Current Issues, Challenges and Future Research Opportunities

Two advanced technologies that have the potential to revolutionise the Internet of Things (IoT) ecosystem are blockchain and machine learning, as they address a wide range of issues, including data security, interoperability, and scalability. However, there are a number of barriers to integrating these technologies, such as issues with trust, reliability, and governance. This research study summarises the current state of the BML-IoT applications and identifies the challenges and issues encountered in developing these applications. It also discusses possible directions for future research on BML-IoT applications for smart cities, energy management, smart homes, smart agriculture, smart education, and smart healthcare. Overall, this study contributes to a better understanding of the endless possibilities of BML-IoT applications and the research challenges that need to be addressed to fully realise their potential.
A Metaheuristic based Clustering Approach for Breast Cancer Identification for Earlier Diagnosis

Breast cancer is the most common type of cancer among women and is an imminent danger to their life. China now has the highest global mortality rate for breast cancer, and this rate is rising. Research teams and organizations are working round-the-clock to create the optimal diagnostic procedure and course of action because of the significant harm that breast cancer does to the well-being and health of an individual. Because of developments in computer technology and learning algorithms, artificially intelligent algorithms have been given the ability to replace human behavior and judgement in some fields. In order to identify breast cancer using the conventional way, medical personnel must constantly evaluate patient data. In this case, the algorithm is used to quickly deliver clinicians an incredibly plausible reference outcome, which is very important to increase the accuracy of the diagnosis and minimize the burden on medical professionals. In order to improve the precision of cancer recognition, this research establishes and puts into practice a method based on the particle swarm optimization (PSO) algorithm to continuously change the most important variables of the spatially constrained adaptively regularized kernel based fuzzy c means (ScARKFCM) clustering. The outcomes of the developed PSO-ScARKFCM algorithm were validated using the MIAS dataset. The findings assist doctors in obtaining a quicker diagnosis of the condition by detecting and extracting the tumor from the unprocessed medical images.
DEMRL: Dynamic estimation meta reinforcement learning for path following on unseen unmanned surface vehicle

Reinforcement learning has been widely used for unmanned surface vehicle (USV) control tasks. However, the requirement of numerous training samples limits its transferability to new USVs. In this article, we propose a dynamic estimation meta reinforcement learning (DEMRL) approach that enables few-shot learning for the path following control policy. We first present a dynamic estimation method to learn a latent dynamic context feature. The learned context contains the hidden information of USV dynamics with only a few estimation samples. We then propose a meta reinforcement learning based training framework to learn the generalizable path following control policy. After that, given the prior knowledge from dynamic context, the well-trained policy can easily adapt to the target USV during the rapid adaptation process. This proposed method represents the initial effort in tackling the few-shot learning challenge associated with training reinforcement learning based USV path-following policies. Extensive experiments demonstrate that the proposed method can achieve promising path following performance for unseen USV with very few training data and training volume.
A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models. With gigapixel WSIs exhibiting diverse histological appearances, obtaining clinically prognostic and explainable features remains challenging. Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival.The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset was used in this investigation. A deep convolutional survival model (DeepConvSurv) extracted histopathological information from the image patches of nine different tissue types, including tumors, lymphocytes, stroma, and mucus. The tissue map of the WSIs was segmented using image processing techniques that involved localizing and quantifying the tissue region. Six survival models with the concordance index (C-index) were used as the evaluation metrics.We extracted 128 histopathological features from four histological types and five tissue area features from WSIs to predict colorectal cancer survival. Our method performed better in six distinct survival models than the Whole Slide Histopathological Images Survival Analysis framework (WSISA), which adaptively sampled patches using K-means from WSIs. The best performance using histopathological features was 0.679 using LASSO-Cox. Compared to histopathological features alone, tissue area features increased the C-index by 2.5%. Based on histopathological features and tissue area features, our approach achieved performance of 0.704 with RIDGE-Cox.A deep learning-based algorithm combining histopathological features with tissue area proved clinically relevant and effective for predicting cancer survival.
How digital transformation affects the cost of equity capital: the role of information disclosure quality and stock liquidity

Abstract To achieve high-quality development, a critical question needs to be addressed: can enterprise digital transformation effectively alleviate enterprise financing problems by reducing the cost of equity capital? This study uses the text mining to analyze the financial statement from Chinese companies listed on Shanghai and Shenzhen A-share markets between 2011 and 2021, in order to define the digital transformation. Further, this study empirically tests the influencing mechanisms of digital transformation on the cost of equity capital, considering the perspectives of information disclosure quality and stock liquidity. The conclusions are as follows: (i) enterprise digital transformation can significantly reduce the cost of equity capital, and under the control of other variables, the cost of equity financing will be reduced by 8.8% for every 1% increase in digital transformation; (ii) digital transformation not only leads to a decrease in the cost of equity capital through the two independent mediation channels, improving information disclosure and accelerating stock liquidity, but also reduces the cost of equity capital through the chain mediation channel from information disclosure quality to stock liquidity; (iii) heterogeneity analysis finds that the effect of digital information on the cost of equity capital is more pronounced in larger, non-state-owned, non-high-tech and eastern enterprises. This study enriches the research of the influence of digital transformation on external financing, and plays an important role in the promotion of enterprise digital transformation and the formulation of targeted policies.
Optimal scheduling in probabilistic imaginary-time evolution on a quantum computer

Ground-state preparation is an important task in quantum computation. The probabilistic imaginary-time evolution (PITE) method is a promising candidate for preparing the ground state of the Hamiltonian, which comprises a single ancilla qubit and forward- and backward-controlled real-time evolution operators. The ground state preparation is a challenging task even in the quantum computation, classified as complexity-class quantum Merlin-Arthur. However, optimal parameters for PITE could potentially enhance the computational efficiency to a certain degree. In this paper, we analyze the computational costs of the PITE method for both linear and exponential scheduling of the imaginary-time step size for reducing the computational cost. First, we analytically discuss an error defined as the closeness between the states acted on by exact and approximate imaginary-time evolution operators. The optimal imaginary-time step size and rate of change of imaginary time are also discussed. Subsequently, the analytical discussion is validated using numerical simulations for a one-dimensional Heisenberg chain. From the results, we find that linear scheduling works well in the case of unknown eigenvalues of the Hamiltonian. For a wide range of eigenstates, the linear scheduling returns smaller errors on average. However, the linearity of the scheduling causes problems for some specific energy regions of eigenstates. To avoid these problems, incorporating a certain level of nonlinearity into the scheduling, such as by inclusion of an exponential character, is preferable for reducing the computational costs of the PITE method. The findings of this paper can make a significant contribute to the field of ground-state preparation of many-body Hamiltonians on quantum computers.