Reviewerly/exHarmony huggingface dataset:
Deep learning generates synthetic cancer histology for explainability and education

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.
RCFusion: Fusing 4-D Radar and Camera With Bird’s-Eye View Features for 3-D Object Detection

Camera and millimeter-wave (MMW) radar fusion is essential for accurate and robust autonomous driving systems. With the advancement of radar technology, next-generation high-resolution automotive radar, i.e., 4D radar, has emerged. In addition to the target range, azimuth, and Doppler velocity measurements of traditional radar, 4D radar provides elevation measurement to create a denser “point cloud.” In this study, we propose a camera and 4D radar fusion network called RCFusion, which achieves multimodal feature fusion under a unified bird’s-eye view (BEV) space to accomplish 3D object detection tasks. In the camera stream, multi-scale feature maps are obtained by the image backbone and feature pyramid network; they are then converted into orthographic feature maps by an orthographic feature transform. Next, enhanced and fine-grained image BEV features are obtained via a designed shared attention encoder. Meanwhile, in the 4D radar stream, a newly designed component named Radar PillarNet efficiently encodes the radar features to generate radar pseudo-images, which are fed into the point cloud backbone to create radar BEV features. An interactive attention module is proposed for the fusion stage, which outputs a valid fusion of the two-modal BEV features. Finally, a generic detection head predicts the object classes and locations. The proposed RCFusion is validated on the TJ4DRadSet and View-of-Delft datasets. The experimental results and analysis show that the proposed method can effectively fuse camera and 4D radar features to achieve robust detection performance.
A path planning algorithm for mobile robot based on water flow potential field method and beetle antennae search algorithm

Aiming at the problems that beetle antennae search algorithm is difficult and easy to avoid obstacles when solving path planning problems, and falls into local optimization, which leads to low efficiency, a path planning algorithm of mobile robot combining the water flow potential field method and the beetle antennae search is proposed. Path planning divides the global path into segments by setting up segmented sites by using the beetle genetic operator. Local path planning between sites is classified according to the characteristics of obstacles, and the artificial potential field method is used to guide the search. The nature of water flow method is used to plan the obstacle avoidance route, optimize the obstacle avoidance process, and effectively avoid falling into local trap obstacles. Finally, the site coordinates are optimized by the beetle antennae search algorithm to improve the path quality and prevent the path from falling into local optimum. The simulation results show that the algorithm can effectively avoid obstacles in various path planning environments, has the characteristics of short time consumption, good optimization effect, and is not easy to fall into local optimization, and is an efficient algorithm for solving the path planning problem of mobile robots.
Robust Unscented Kalman Filter-Based Decentralized Multisensor Information Fusion for INS/GNSS/CNS Integration in Hypersonic Vehicle Navigation

INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integration is a favourable navigation mode for hypersonic vehicles. However, since the measurements from GNSS and CNS are easily interfered during highly dynamic maneuvers, this integration is very difficult to achieve optimal navigation solution with existing information fusion techniques. This paper proposes a new method of decentralized multi-sensor information fusion based on robust UKF (unscented Kalman filter) for INS/GNSS/CNS integration to solve the above issue. Firstly, a fault detection-based robust UKF is established for local state estimation, in which a hypothesis test is constructed via the Mahalanobis distance of innovation to detect abnormal measurements in GNSS and CNS; and subsequently, a scalar factor is determined and further introduced into the innovation covariance to decrease the Kalman gain to improve the UKF robustness against abnormal measurements. Secondly, the traditional multi-sensor optimal data fusion technique is extended to nonlinear systems by use of unscented transformation in the framework of minimum variance estimation to fuse the local state estimations from INS/GNSS and INS/CNS subsystems. The proposed information fusion method can achieve the globally optimal fusion estimation results against abnormal measurements for hypersonic vehicle navigation with INS/GNSS/CNS integration. Semi-physical simulations and comparison analysis have validated the superior performance of the proposed method.
DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks

Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL) models have been commonly employed in load forecasting problems supported by adaptation mechanisms to cope with the changing pattern of consumption by customers, known as concept drift. A drift magnitude threshold should be defined to design change detection methods to identify drifts. While the drift magnitude in load forecasting problems can vary significantly over time, existing literature often assumes a fixed drift magnitude threshold, which should be dynamically adjusted rather than fixed during system evolution. To address this gap, in this paper, we propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models without requiring a drift threshold setting. We integrate several strategies into the framework based on active and passive adaptation approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze the proposed framework and deploy it in a real-world problem through a cloud-based environment. Efficiency is evaluated in terms of the prediction performance of each approach and computational cost. The experiments show performance improvements on multiple evaluation metrics achieved by our framework compared to baseline methods from the literature. Finally, we present a trade-off analysis between prediction performance and computational costs.
Addressing the class-imbalance and class-overlap problems by a metaheuristic-based under-sampling approach

The problem of imbalanced class distribution in real-world datasets severely impairs the performance of classification algorithms. The learning task becomes more complicated and challenging when there is also the class-overlap problem in imbalanced data. This research tackles these problems by presenting an under-sampling approach based on a metaheuristic method in which the under-sampling problem is mapped into an optimization problem. The proposed approach aims to select an optimal subset of the majority samples to handle the imbalanced and the class-overlap problems simultaneously while avoiding the excessive elimination of majority samples, especially in overlapped regions. The quality of the generated solutions is evaluated by a classifier and optimized in an evolutionary process. Unlike most existing under-sampling methods, the majority samples are not removed only from the overlapped regions; the classifier performance determines the desired regions for eliminating the majority samples. Extensive experiments conducted on 66 synthetic and 24 real-world datasets with different imbalance ratios and overlapping degrees and two large high-dimensional datasets show a significant performance improvement from the proposed method compared to the competitors.
AMSUnet: A neural network using atrous multi-scale convolution for medical image segmentation

In recent years, Unet and its variants have gained astounding success in the realm of medical image processing. However, some Unet variant networks enhance their performance while increasing the number of parameters tremendously. For lightweight and performance enhancement jointly considerations, inspired by SegNeXt, we develop a medical image segmentation network model using atrous multi-scale (AMS) convolution, named AMSUnet. In particular, we construct a convolutional attention block AMS using atrous and multi-scale convolution, and redesign the downsampling encoder based on this block, called AMSE. To enhance feature fusion, we design a residual attention mechanism module (i.e., RSC) and apply it to the skip connection. Compared with existing models, our model only needs 2.62 M parameters to achieve the purpose of lightweight. According to experimental results on various datasets, the segmentation performance of the designed model is superior for small, medium, and large-scale targets. Code will be available at https://github.com/llluochen/AMSUnet.
A New 3-Bit Hiding Covert Channel Algorithm for Public Data and Medical Data Security Using Format-Based Text Steganography

The primary concern of every individual and organization is the security of sensitive information generated via authorized activities; nonetheless, illicit data drawing and extraction by attackers is prevalent, which may be mitigated by covert approaches. Although cypher techniques give excellent protection, they raise suspicion in the eyes of adversaries, resulting in both passive and active assaults on the information sent. Steganography, on the other hand, helps to reduce third-party suspicion. This method conceals sensitive information on cover data and transports it to the targets without skepticism. However, the issue depends entirely on the effectiveness of the embedding method; it must also satisfy other data concealing features such as embedding capacity. As payload grows, so does skepticism. This article handled this issue to lessen suspicion while maintaining embedding capacity. The article proposes a format-based text concealing algorithm, a traditional way for dealing with embedding capacity and invisibility. The authors compared our results to those of other similar current methods. They discovered that theirs are pretty decent—the present study offered both standard public communication security and medical data protection.
Open- and Closed-Loop Neural Network Verification Using Polynomial Zonotopes

We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU, sigmoid, or hyperbolic tangent activation functions. In particular, we abstract the input-output relation of each neuron by a polynomial approximation, which is evaluated in a set-based manner using polynomial zonotopes. While our approach can also can be beneficial for open-loop neural network verification, our main application is reachability analysis of neural network controlled systems, where polynomial zonotopes are able to capture the non-convexity caused by the neural network as well as the system dynamics. This results in a superior performance compared to other methods, as we demonstrate on various benchmarks.
Optimizing the parameters of hybrid active power filters through a comprehensive and dynamic multi-swarm gravitational search algorithm

This paper introduces a dynamic comprehensive multi-swarm gravitational search algorithm with non-uniform mutation (cdGSA-2m) for optimizing hybrid active power filter (HAPF) parameters. The cdGSA-2m algorithm divides the population into two heterogeneous sub-populations with different learning mechanisms. The first sub-population focuses on exploitation with comprehensive learning, while the second sub-population emphasizes exploration with a dynamic multi-swarm strategy (DMS) with non-uniform mutation. Additionally, a non-linear adaptive parameter balances the search capabilities of the DMS sub-population. These mechanisms effectively control the exploration, exploitation, and diversity of the population. To analyse the effectiveness of implemented mechanisms in the cdGSA-2m, diversity and exploration–exploitation are discussed at length with a few experiments. An independent structural study of cdGSA-2m is also carried out to understand the effects of comprehensive learning, dynamic multi-swarm strategy, and non-uniform mutation mechanisms in the algorithm. The proposed cdGSA-2m is tested on IEEE-CEC 2017 benchmark problems, and the results of these problems are compared with twenty-two state-of-the-art algorithms in terms of mean error values and standard deviation of the fitness values. Based on the results, the algorithms are ranked against each other, along with statistical validation of the results. The proposed cdGSA-2 m also solves two case studies of the HAPF model and compares the results with existing state-of-the-art algorithms for the HAPF model. Experimental results demonstrate that the cdGSA-2m algorithm has better accuracy, convergence rate, search capability, and stability. The overall ranking of the algorithms suggests that cdGSA-2 m has outstanding potential to deal with difficult optimization problems.
A nomadic multi-agent based privacy metrics for e-health care: a deep learning approach

In recent years, there has been a surge in the use of deep learning systems for e-healthcare applications. While these systems can provide significant benefits regarding improved diagnosis and treatment, they also pose substantial privacy risks to patients' sensitive data. Privacy is a crucial issue in e-healthcare, and it is essential to keep patient information secure. A new approach based on multi-agent-based privacy metrics for e-healthcare deep learning systems has been proposed to address this issue. This approach uses a combination of deep learning and multi-agent systems to provide a more robust and secure method for e-healthcare applications. The multi-agent system is designed to monitor and control the access to patients' data by different agents in the system. Each agent is assigned a specific role and has specific data access permissions. The system employs a set of privacy metrics to a substantial privacy level of the data accessed by each agent. These metrics include confidentiality, integrity, and availability, evaluated in real-time and used to identify potential privacy violations. In addition to the multi-agent system, the deep learning component is also integrated into the system to improve the accuracy of diagnoses and treatment plans. The deep learning model is trained on a large dataset of medical records and can accurately predict the diagnosis and treatment plan based on the patient's symptoms and medical history. The multi-agent-based privacy metrics for the e-healthcare deep learning system approach have several advantages. It provides a more secure system for e-healthcare applications by ensuring only authorized agents can access patients' data. Privacy metrics enable the system to identify potential privacy violations in real-time, thereby reducing the risk of data breaches. Finally, integrating deep learning improves the accuracy of diagnoses and treatment plans, leading to better patient outcomes.
Comparative analysis of colloid-mechanical microenvironments on the efficient purification of phosphogypsum

The storage of phosphogypsum could bring huge burden to the environment, while the traditional method of washing and purifying phosphogypsum produced a large amount of waste water and colossal energy consumption. The novel colloidal-mechanical method as a new treatment technology of purifying phosphogypsum was benefit for the large-scale comprehensive utilization of phosphogypsum, but its purification effect and influencing factors needed to be further studied. Surfactants like Span-80, TX-100 and Tween-80 were one of major factors for colloidal-mechanical purification method about whiteness improvement and impurities removal of phosphogypsum. It created colloidal-mechanical microenvironments during the purification process of phosphogypsum, and helped to realize the best removal effects and purification technology of phosphogypsum. After characterization, it showed that the whiteness of phosphogypsum increased under the action of micelle and mechanical force, and the removal effects of soluble phosphorus and fluorine impurities were noticeable. The whiteness value of purified PAS could reach 51.9, which was 57.8% higher than natural PG. The phosphorus content in the slurry solution of PAS was 274.649 ppm, which was 208% higher than PG. The fluorine content in PAS was 0.289 wt%, which was 24.3% lower than PG. The purification effect of phosphogypsum was the best through the micro-environment created by Span-80 and mechanical force. This study provided a green design direction for the critical purification and its further utilization of phosphogypsum. This work provided a clear idea for solving the current purification dilemma of phosphogypsum with an outstanding application prospect.
Social engineering attack types and prevention techniques- A survey

The term "Cyber Security" is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim’s social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article.
Blockchain, sustainability and clean energy transition

The digital economy's neo-industrialization self-accelerates resource consumption and wide automatization inevitably envisage a technological leap. The article contributes conceptually and empirically to a systemic vision of blockchain to sort out climate change challenges and clean energy transition and simultaneously increase the productivity and efficiency of good practices. This vision covers the popularization of ecological initiatives, waste reduction, organization of sustainable investments, control over responsibilities on both fighting and forecasting climate change and clean energy transition. By embracing the notion of blockchain as a problem-solving tool for climate change and clean energy transition, the paper draws and investigates the experiences of the 36 digitally developed and 25 digitally developing economies. It also examines the effectiveness of alternative practices in Industry 4.0. The paper's findings represent a systematic vision of implementing blockchain initiatives to solve climate change and clean energy transition. An energy-efficient model with a blockchain opens up massive opportunities for ecological monitoring, supports energy transition and ameliorates economic sustainability. Since the blockchain potential is not fully unlocked, a model expanding the use of blockchain in education to train green personnel and in science to support climate innovations is proposed.
ResNet and its application to medical image processing: Research progress and challenges

Deep learning, a novel approach and subset of machine learning, has drawn a growing amount of attention from computer vision researchers in recent years. This method has drawn a lot of interest because of its extraordinary ability to interpret medical pictures, especially when combined with residual neural networks, which have helped to progress the field.In this paper, the following research is carried out on the residual network. First, the research status of ResNet in the medical field is introduced. The fundamental idea behind the residual neural network is then explained, along with the residual unit, its many structures, and the network architecture. Second, four aspects of the widespread use of residual neural networks in medical image processing are discussed: lung tumor, diagnosis of skin diseases, diagnosis of breast diseases, and diagnosis of diseases of the brain. Finally, the main issues and ResNet's future development in the area of processing medical images are discussed.In the area of medical graph processing, residual neural networks have made strides and have had success in the clinical auxiliary diagnosis of serious illnesses such as lung tumors, breast cancer, skin conditions, and cardiovascular and cerebrovascular diseases.We thoroughly sorted out the most recent developments in residual neural network research and their use in medical image processing, which serves as a crucial point of reference for this field of study. It offers a helpful reference for further promoting the application and research of the ResNet model in the field of medical image processing by summarising the application status and issues of the ResNet model in the field of medical image processing and putting forwards some future development directions.