Reviewerly/exHarmony huggingface dataset:
MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images

More than half of brain tumors are malignant tumors, so there is a need for fast and accurate segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) images. Traditional 2D brain tumor segmentation methods seriously ignore the spatial context features of brain tumor MRI images, so how to achieve accurate segmentation of brain tumor regions with multiple modalities is the main problem. The paper proposes a 3D convolutional neural network with 3D multi-branch attention - MBANet. First, the optimized shuffle unit is used to form the basic unit (BU) module of MBANet. In the BU module, the group convolution is used to perform convolution operation after the input channel is split, and the channel shuffle is used to scramble the convolutional channels after fusion. Then, MBANet uses a novel multi-branch 3D Shuffle Attention (SA) module as the attention layer in the encoder. The 3D SA module groups along the channel dimension and divides the feature maps into small features. For each small feature, the 3D SA module builds both channel attention and spatial attention while adopting the BU module. In addition, in order to recover the resolution of the upsampling semantic features better, a 3D SA module is also used in the skip connection of MBANet. Experiments on the BraTS 2018 and BraTS 2019 show that the dice of ET, WT and TC reach 80.18%, 89.80%, 85.47% and 78.21%, 89.79%, 83.04%, respectively. The excellent segmentation performance shows that MBANet is significantly improved compared with other state-of-the-art methods.
Real-time vehicle detection algorithm based on a lightweight You-Only-Look-Once (YOLOv5n-L) approach

A vehicle detection algorithm is of great significance for automatic driving technology. Current vehicle detection algorithms suffer from the complex structure, high configuration of hardware requirements, and the difficulty to apply to mobile terminal equipment. In order to solve these issues, this paper proposes an improved YOLOv5 algorithm, named YOLOv5n-L, for lightweight. First, a depthwise separable convolution and a C3Ghost module are used to replace several C3 modules to reduce the model parameters and improve the detection speed. Then a Squeeze-and-Excitation attention mechanism is integrated into backbone network to improve the accuracy of the algorithm and suppress the environmental interference. Finally, a bidirectional feature pyramid network is used for multi-scale feature fusion to enrich feature information and improve the feature extraction ability of the proposed algorithm. The experimental results demonstrate that compared with the original algorithm, the model weight is reduced by 40 % to only 2.3 M. The mean average precision ([email protected]) is increased by 1.7 %. The detection speed reaches 80 FPS, which could accurately detect vehicle targets in real-time.
The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.
Feedback RRT* algorithm for UAV path planning in a hostile environment

• We present a data-driven method FRRT* for UAV path planning in a hostile environment. • The RRT* based FRRT* consists of a risk network, feedback module and planning module. • FRRT* effectively couples the planning mechanism with a learning risk model. • FRRT* performs data-driven biased adjustments to improve planning efficiency. • The risk network and feedback module can be combined with other RRT based algorithms. Unmanned aerial vehicle (UAV) path planning in a hostile environment (UPPHE) considers the features and information of defense systems to plan a risk-constrained shortest path for a UAV from the initial position to target. When the characteristics of defense systems cannot be accurately expressed manually, it will be brutal to effectively or efficiently implement the classical planning methods because of the lack of risk information to constrain and guide planning. We present a data-driven approach for UPPHE, the feedback rapidly-exploring random tree star algorithm (FRRT*), with a data-driven risk network and feedback module. FRRT* can use information extracted from situation data to constrain the growth of the random tree and make biased adjustments to improve planning efficiency. Simulation experiments verify the effectiveness of the risk network in guiding planning, and the improved efficiency brought by the feedback module.
Past, present, and future of the application of machine learning in cryptocurrency research

Cryptocurrency has captured the interest of financial scholars and become a major research topic in blockchain. In cryptocurrency research, the use of machine learning algorithms is enabled by the presence of many types of data and abundant resources. However, there is currently no comprehensive review on cryptocurrencies using machine learning. Therefore, we collect papers on cryptocurrency-related using machine learning in the web of science database, and summarize these papers according to the algorithm, and draw the following conclusions: (1) The application of machine learning for cryptocurrencies research is increasing year over year; (2) Predicting cryptocurrency price trends and income fluctuations is the most relevant research topic; (3) The machine learning algorithm utilized in cryptocurrency research is not unique, and the practise of combining multiple machine learning approaches has emerged; (4) Concerns such as overfitting and interpretability still persist with machine learning methods. Finally, we suggest future research directions.
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion

Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifacts, denoising is largely studied both within the medical imaging community and beyond the community as a general subject. However, recent deep neural network-based approaches mostly rely on the minimum mean squared error (MMSE) estimates, which tend to produce a blurred output. Moreover, such models suffer when deployed in real-world situations: out-of-distribution data, and complex noise distributions that deviate from the usual parametric noise models. In this work, we propose a new denoising method based on score-based reverse diffusion sampling, which overcomes all the aforementioned drawbacks. Our network, trained only with coronal knee scans, excels even on out-of-distribution in vivo liver MRI data, contaminated with a complex mixture of noise. Even more, we propose a method to enhance the resolution of the denoised image with the same network. With extensive experiments, we show that our method establishes state-of-the-art performance while having desirable properties which prior MMSE denoisers did not have: flexibly choosing the extent of denoising, and quantifying uncertainty.
Time-varying dependence between Bitcoin and green financial assets: A comparison between pre- and post-COVID-19 periods

This paper studies the time-varying market linkages between Bitcoin and green assets before and during the COVID-19 pandemic through a TVP-VAR model with stochastic volatility. Both the roles of uncertainty and environmental attention related to cryptocurrency are considered when modeling market linkages, which underlying asymmetry is detected from three perspectives, i.e., bidirectionality of the impact direction, time points where the unit shock of the IRF analysis is imposed, and before and after the pandemic. We find that the investment sheltering role of Bitcoin for green assets is enhanced and expanded after the onset of the pandemic, while green assets in turn consistently act as an effective hedge for Bitcoin irrespective of the pandemic. Additional analyses confirm the robustness of our findings, which possess implications for not only hedging against green portfolios but also seeking green shelters.
Multi-level threshold segmentation framework for breast cancer images using enhanced differential evolution

Breast cancer has replaced lung cancer as the most prevalent malignancy threatening human health. Early breast screening can help improve treatment success and reduce the risk of death. The analysis and diagnosis of breast cancer real images by computer-aided technology is the key link to early diagnosis. High-quality medical segmentation images can improve the accuracy of lesion area detection. This study used a multi-level threshold image segmentation framework based on novel differential evolution, two-dimensional Kapur's entropy, and the two-dimensional histogram to improve the efficiency of subsequent image analysis and diagnosis. We proposed an enhanced differential evolution in the framework based on the roundup search, the elite lévy-mutation, and the decentralized foraging strategy to explore the optimal thresholds. In this study, the enhanced differential evolution was compared to state-of-the-art methods for benchmark function experiments and breast cancer image segmentation experiments. It is shown that the proposed threshold search method accelerates convergence and reduces the problem of premature convergence. Quantitative results demonstrate that the proposed method can achieve an average peak signal-to-noise ratio and feature similarity index of 21.231 and 0.951, respectively, at the 5-level threshold, which is better than other methods. As a result, the proposed multi-level threshold image segmentation model can provide quality samples for subsequent image analysis and classification.
An improved differential evolution by hybridizing with estimation-of-distribution algorithm

To fully exploit the strong exploitation of differential evolution (DE) and the strong exploration of the estimation-of-distribution algorithm (EDA), an improved differential evolution by hybridizing the estimation-of-distribution algorithm named IDE-EDA is proposed in the study. Firstly, a novel cooperative evolutionary framework is proposed to hybridize LSHADE-RSP, a state-of-the-art DE variant incorporating DE-based effective improvement strategies, with EDA. Secondly, the dominant individuals generated by LSHADE-RSP are used to establish the probability distribution model for EDA to enhance its exploitation in each generation, and a new control parameter is introduced to balance exploitation and exploration. Then, the use of greed strategy works via EDA to fully retain high-quality solutions to the next generation to improve the convergence speed. Finally, the greedy strategy is used to shrink the external archive when its size decreases due to the reduction of the population size. A comparison of IDE-EDA with cutting-edge DE-based and EDA-based variants, including AAVS-EDA, EB-LSHADE, ELSHADE-SPACMA, jSO, LSHADE-RSP, RWGEDA, HSES, and APGSK-IMODE, was implemented to verify its efficiency. The statistical test results on the IEEE CEC 2018 and IEEE CEC 2021 test suites demonstrate that IDE-EDA is an excellent hybrid algorithm. The MATLAB source code of IDE-EDA can be downloaded from https://github.com/Yintong-Li/IDE-EDA.
Non-fungible tokens (NFTs): A bibliometric and systematic review, current streams, developments, and directions for future research

As bibliometric analysis has become very popular in various academic fields, this paper reviews the literature on Non-Fungible Tokens (NFTs). NFTs are tradeable rights that can be used to acquire ownership of digital assets such as music, videos, and images (Dowling, 2022b). They became the first blockchain technology application to achieve clear public prominence in early 2021 and were widely used in blockchain research because of their significant contributions. This study reviews 150 documents on NFTs by using VOSviewer and identifies three major clusters: (1) blockchain, (2) cryptocurrency, and (3) digital art. In addition, this study provides an overview of how the literature on NFTs has developed over time, as well as a summary of the most cited authors, documents, countries, organizations, journal sources, and reference papers. Moreover, the results reveal that Finance Research Letters is the top journal that publishes documents on NFTs and the United States is the most active country in research on NFTs reflecting their important contributions to this research topic. The findings of this study present future research directions in the field of NFTs.
Future data center energy-conservation and emission-reduction technologies in the context of smart and low-carbon city construction

The energy consumption of data centers accounts for approximately 1% of that of the world, the average power usage effectiveness is in the range of 1.4–1.6, and the associated carbon emissions account for approximately 2–4% of the global carbon emissions. To reduce the energy consumption of data centers and promote smart, sustainable, and low-carbon city development, this study analyzes the energy conservation and emission-reduction technologies and potential decarbonization paths for data centers, compares the energy-saving situation of 20 typical data center cases, and highlights the impact of green data centers on the global carbon neutrality goal. The analysis reveals that data center energy consumption can be reduced by about 20–40% and 15–27% through IT equipment optimization and cooling technology improvements, respectively. Data center energy-saving strategies must consider differences in geographical location, natural resources, and economic bases. Therefore, this study examines the necessary steps for building zero-carbon data centers from the perspectives of public policy, technological innovation, and resource management. Specifically, the following aspects are explored: 1) accelerating the intelligent and unified management of data center resources; 2) building storage-computing integrated data centers that are compatible with heterogeneous resources and streamlined business models; 3) realizing large-scale and diversified use of clean energy in data centers.
InFEDge: A Blockchain-Based Incentive Mechanism in Hierarchical Federated Learning for End-Edge-Cloud Communications

Advances in communications and networking technologies are driving the computing paradigm toward the end-edge-cloud collaborative architecture to leverage ubiquitous data and resources. Opposite to centralized intelligence, Hierarchical Federated Learning (HFL) relieves overwhelmed communication overhead and enjoys the advantages of high bandwidth as well as abundant computing resources while retaining privacy-preserving benefits of Federated Learning (FL). It is difficult to balance system overhead and model performance in the HFL framework, while it could be solved by introducing an incentive mechanism. Although the incentive mechanism can alleviate the above anxiety by compensating relevant participants, some limitations (multi-dimensional properties, incomplete information and unreliable participants) will significantly degrade the performance and efficiency of the designed mechanism. To address the challenges caused by the above limitations, we propose InFEDge, a blockchain-based incentive mechanism in the HFL. The InFEDge considers 1) multi-dimensional individual properties to model system participants and proves the uniqueness of Nash equilibrium with the closed-form solution. Meanwhile, 2) we transform the problem under incomplete information into a contract game where we obtain the optimal solution. Moreover, 3) we also leverage the blockchain to provide economic incentives, prevent unreliable participants’ disturbance and further ensure data privacy by implementing the mechanism in the smart contract to offer a credible, faster, and transparent resource trading system. Experimental evaluations on a proof-of-concept testbed along with real traces demonstrate the superiority of our mechanism. Further, our method solves a real-world user allocation problem for future communications and networking.
Trends and gaps in photovoltaic power forecasting with machine learning

The share of solar energy in the electricity mix increases year after year. Knowing the production of photovoltaic (PV) power at each instant of time is crucial for its integration into the grid. However, due to meteorological phenomena, PV power output can be uncertain and continuously varying, which complicates yield prediction. In recent years, machine learning (ML) techniques have entered the world of PV power forecasting to help increase the accuracy of predictions. Researchers have seen great potential in this approach, creating a vast literature on the topic. This paper intends to identify the most popular approaches and the gaps in this discipline. To do so, a representative part of the literature consisting of 100 publications is classified based on different aspects such as ML family, location of PV systems, number of systems considered, features, etc. Via this classification, the main trends and gaps can be highlighted while offering advice to researchers interested in the topic.
Designing solar power generation output forecasting methods using time series algorithms

The present photovoltaic (PV) power generation systems are globally facing the irregularity problem in the distribution of PV generation. In particular, the exact PV power forecasting is critical for grid-connected photovoltaic (PV) systems under unwanted changes in environmental circumstances. The grid energy management, grid operation and scheduling are important factors to forecast the PV power output. Time series analysis is one of the most important aspects of PV output prediction, especially in places (in South Korea) where past solar radiation data or other weather parameters have not been recorded. In this paper, a variety of time-series methods including deep-learning algorithm and machine learning algorithms was used to predict the PV power generation output for quick respond to equipment and panel defects. For designing AI models, the input data were characterized by dividing seasons and choosing the multiple parameters from seasons. In this study, the photovoltaic power generation data was collected from Ansan city, South Korea during January 2017 to June 2021 and the weather data was collected from Suwon city, South Korea during January 2017 to June 2021. In this work, approx. 40,000 hours of operation data from 1.5 MW grid-connected PV system in South Korea was used. PV power generation forecasting was carried out on an hourly basis to test efficacy of various models. Among all models (Holt-Winters, Multivariate Linear Regression, ARIMA, SARIMA, ARIMAX, SARIMAX), LSTM model presented the lowest error rate as compared to other models for quick PV power generation forecasting.
Sentimental Analysis Using Supervised Learning Algorithms

The humongous growth of online-grounded operations, similar to forums and blogs, led to commentary and reviews related to diurnal conditioning. Sentimental analysis is the process of collection of data and analyzing one’s studies, ideas, also extremities about colorful motifs, products, motifs, and services. One’s ideas can be useful to companies, governmental organizations, and individualities by gathering data and practicing vision-grounded opinions. Still, emotional analysis and the process of assessment are at stake numerous challenges. These challenges produce walls to directly interpret feelings and determine applicable emotional diversity. Emotional judges and excerpt practical information from the textbook using natural language processing as well as digging the textbook. This composition discusses a complete idea of how to negotiate this work and the use of Sentimental analysis. Also, it is responsible for evaluating, comparing, and investigating the styles used to understand both good and bad. Eventually, Sentimental analysis challenges are explored to define unborn directions.
Splicing ViT Features for Semantic Appearance Transfer

We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are “painted” with the visual appearance of their semantically related objects in a target appearance image. Our method works by training a generator given only a single structure/appearance image pair as input. To integrate semantic information into our framework—a pivotal component in tackling this task-our key idea is to leverage a pre-trained and fixed Vision Transformer (ViT) model which serves as an external semantic prior. Specifically, we derive novel representations of structure and appearance extracted from deep ViT features, untwisting them from the learned self-attention modules. We then establish an objective function that splices the desired structure and appearance representations, interweaving them together in the space of ViT features. Our framework, which we term “Splice”, does not involve adversarial training, nor does it require any additional input information such as semantic segmentation or correspondences, and can generate high resolution results, e.g., work in HD. We demonstrate high quality results on a variety of in-the-wild image pairs, under significant variations in the number of objects, their pose and appearance.