Reviewerly/exHarmony huggingface dataset:
Full-coverage confidence function path planning algorithm for mobile robots

We propose a full-coverage path planning algorithm for mobile robots based on a raster confidence function. The algorithm aims to address the problem of guiding a mobile robot to traverse all reachable points in the working area while ensuring automatic obstacle avoidance. To achieve this, we assign a raster map based on environmental information and utilize different function values to represent obstacles, covered raster, and uncovered raster areas. This algorithm provides an effective solution for achieving full coverage in path planning for mobile robots, ensuring efficient exploration of the working area while avoiding obstacles; second, different directional confidence functions are introduced to determine whether the robot is caught in a dead zone, and the raster function values are adjusted. Finally, the robot generates the coverage path using the raster confidence function values. This algorithm not only enables the mobile robot to achieve complete coverage of the working area but also allows it to efficiently escape from dead zones and minimize path repetition. In the simulation experiments, it is demonstrated that the algorithm mentioned in this paper has higher coverage efficiency by comparing it with the bio-inspired neural network algorithm.
Velocity control of Electromagnetic pellet injection based on genetic algorithm in J-TEXT

Electromagnetic pellet injection (EMPI) is a new disruption mitigation system on J-TEXT Tokamak. In the experimental research, there is a great demand for the control accuracy of the injection velocity of the impurity pellet. Therefore, considering the special demand for the armature velocity control of the electromagnetic railgun, a genetic algorithm (GA) based power supply timing optimization method is proposed, which makes the armature move uniformly and linearly through controlling the discharge current waveform smoothly, and makes the pellet reach the preset velocity within the controllable orbit length. In this paper, three strategies of elite retention, mutation and tournament selection crossover are used to generate new population. The results show that not only the current waveform is stable, but also the armature moves in a straight line with almost uniform acceleration. The results show that the optimization method is a practical and effective method to determine the timing of electromagnetic railgun pulse power supply.
Enhancing Robustness in Multi-Agent Actor-Critic with Double Actors

In recent years, multi-agent reinforcement learning (MARL) has shown a strong ability to solve complex system tasks. How to solve the overfitting problem of MARL algorithm and how to improve the robustness of MARL algorithm are important issues that need to be considered when the algorithm is turned to practical application. In this paper, we discuss the application potential of two-actor networks in MARL algorithms. First, we design double actors based on MADDPG algorithm, to improve the robustness of learning strategies, which is called DA-MADDPG algorithm. In the DA-MADDPG, the central-decentralized mismatch caused by two actor networks updating with the same Q value is further discussed. A gradient updating optimization method for DA-MADDPG by KL divergence and clipped function is proposed respectively. Experimental results in MPE environment show that DA-MADDPG and DA-MADDPG bycclipped function can achieve better results than MADDPG algorithm.
Feature Classification of Motor Imagery EEG Based on Deep Learning Networks

The Brain Computer Interface (BCI) technology refers to not rely on normal brain nerve and musclar tissue, but on computer or other equipment to build a new information transmission circuit between the brain and the external environment, it can directly realize the information exchange between brain and external environment, which is a new way for human to communicate with the outside world. Motor imagery brain-computer interfaces are increasingly being used in practical application scenarios where the degree of accuracy in classification is crucial. However, due to the small amount of data available for MI EEG, overfitting can easily occur when using deep learning network models for classification training, resulting in low recognition rates. To overcome this problem, we have proposed, attention mechanismc convolutional neural network (AMCNN) which is a novel optimized convolution neural network model. A 4-Layer convolutional neural network is built as the classifier and convolutional kernels of different sizes are applied. The performance obtained by the proposed approach is evaluated by Accuracy, Precision, Recall and Kappa value. The accuracy on dataset 2a from competition IV(BCIIV-2a) reaches 86%, and the best Precision, Kappa and Recall on dataset 2a from competition IV is greater than many of other methods.
On robust output regulation of four strings*

Based on our recent work [1], this paper focuses on the potential relationship between two ways to design observer based control, namely based on either three exosystem observers or two exosystem observers. Both can introduce a 3-copy internal model structure. We further explain the potential relationship between the above observer systems. The tracking errors can be converged to zero and the system states of the closed-loop system are bounded. There are two simulation examples to show the effectiveness of the observer based robust controls.
A Multi-Distance Feature Dissimilarity-Guided Encoder-Decoder Network for Polyp Segmentation

Most colorectal cancers originate from adenomatous polyps, which start as single asymptomatic polyps and develop into malignant tumors. In clinical practice, colonoscopy is an extremely effective method for detecting polyps, and it provides important visual information for the accurate identification and removal of polyps. However, it is highly challenging to achieve accurate segmentation of various polyps due to the complex and variable size, shape, color, number, and growth background of polyps at different periods. To address these dilemmas, we propose a multi-distance feature dissimilarity-guided encoder-decoder network for automatic polyp segmentation, mainly consisting of the Multi-Distance Differential Module (MDDM) and the Hybrid Loss Module (HLM). The former mainly utilizes the multilayer feature subtraction (MLFS) operations to extract the difference information between short-distance adjacent layer features and short-distance and long-distance cross-layer features. Given this, the pyramid-inspired MDDM obtains discriminative features continuously between adjacent/cross layers, enhancing complementary features between different layers. The latter supervises the feature maps extracted at each network level to achieve finer predictions. Experiments on four challenge datasets confirm that the proposed model outperforms most state-of-the-art methods in six evaluation metrics while yielding reasonably accurate segmentation results.
ShapeRef: A Representation Method of Industrial Abnormal Time-Series Waveform Based on Shape Reference

Time-series waveform data widely exist in various industrial fields, such as equipment monitoring and fault diagnosis. The current time series representation methods have limitations when dealing with industrial abnormal time-series waveforms, such as limited applicability, semantic ambiguity, and time distortion. This work proposes a novel shape reference-based representation method for industrial abnormal time-series waveform (ShapeRef), which takes the shape of the standard waveform as a reference to represent the anomaly deviation. Specifically, ShapeRef first establishes a time-series shape reference frame, then proposes the minimum shape difference-based mapping method to describe the mapping process of coordinates, and finally reduces multi-intersection points in the mapping process to achieve uniform mapping of the abnormal time-series waveform. Experimental results show that ShapeRef can effectively represent abnormal time-series waveforms and outperforms several baseline methods in the clustering task of a real industrial equipment waveform dataset. This work enhances the accuracy and reliability of industrial equipment monitoring and fault diagnosis, which could have significant practical implications.
Random Pairwise Competition Based Ant Selection for Pheromone Updating in Ant Colony Optimization

Ant Colony Optimization (ACO) has shown very promising performance in solving Traveling Salesman Problem (TSP). However, most existing ACO algorithms utilize either the absolutely best ants or all ants to update the pheromone matrix. This leads to either serious diversity loss or slow convergence. To alleviate these predicaments, this paper designs a random pairwise competition based ant selection for pheromone updating. Specifically, a number of ants are randomly selected from the ant colony and then are randomly paired together. Subsequently the better one in each pair is selected to update the pheromone matrix. In this way, a good balance between search diversity and search convergence is potentially maintained. Integrating this selection strategy along with a local search scheme into the ACO framework, a new ACO algorithm called random pairwise competition based ACO (RPCACO) is developed. Experiments conducted on 8 TSP instances from the TSPLIB benchmark set demonstrate that RPCACO is more effective and efficient than the five classical ACO algorithms in solving TSP.
RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding

Data augmentation is a widely used technique in machine learning to improve model performance. However, existing data augmentation techniques in natural language understanding (NLU) may not fully capture the complexity of natural language variations, and they can be challenging to apply to large datasets. This paper proposes the Random Position Noise (RPN) algorithm, a novel data augmentation technique that operates at the word vector level. RPN modifies the word embeddings of the original text by introducing noise based on the existing values of selected word vectors, allowing for more fine-grained modifications and better capturing natural language variations. Unlike traditional data augmentation methods, RPN does not require gradients in the computational graph during virtual sample updates, making it simpler to apply to large datasets. Experimental results demonstrate that RPN consistently outperforms existing data augmentation techniques across various NLU tasks, including sentiment analysis, natural language inference, and paraphrase detection. Moreover, RPN performs well in low-resource settings and is applicable to any model featuring a word embeddings layer. The proposed RPN algorithm is a promising approach for enhancing NLU performance and addressing the challenges associated with traditional data augmentation techniques in large-scale NLU tasks. Our experimental results demonstrated that the RPN algorithm achieved state-of-the-art performance in all seven NLU tasks, thereby highlighting its effectiveness and potential for real-world NLU applications.
Harnessing Convolutional Neural Networks for Histopathological Breast Cancer Classification.

Recent advancements in Convolutional Neural Networks (CNNs) have significantly supported the field of breast cancer discovery using medical imaging. An improvised DenseNet architecture for the classification of histo-pathological breast cancer images is explored in this work. Leveraging the effectiveness of DenseNet in capturing intricate patterns through dense connectivity, our improvised architecture aims achieve high accuracy and efficiency of classification. The model integrates novel features such as optimized bottleneck layers and attention mechanisms, contributing to improved feature extraction and classification capabilities. The improvised DenseNet produced a accuracy of 93.39% on the breakhis dataset. A summary of key findings and future research directions, emphasizing the need of custom CNN models in breast cancer detection is provided.
Optimization Algorithms with Machine Learning to Improve Security of Internet of Things

The IOT network traffic classification is the approach which helps to analyse IOT network traffic. The network traffic analysis can to various network activities. The network traffic analysis process has various steps which include data input, pre-processing, feature extraction, classification and performance analysis. The various machine learning algorithms is proposed in the previous years but those algorithms are unable to achieve high accuracy. The algorithms which are already proposed is unable to extract features from the dataset. To propose algorithm which can extract features from the dataset and achieve high accuracy for the network traffic classification is the motivation this research work. To achieve high accuracy hybrid optimization algorithm is proposed in this paper which is the combination of genetic and PSO algorithm. The hybrid optimization algorithm extract features and later it will be classified using Random Forest. The proposed model is implemented in python and results is achieved in terms of accuracy, precision, recall.
The Taylor's Theorem and Its Application

Taylor's theorem is an essential concept in calculus. By constructing polynomials, Taylor's formula can simplify calculations by approximating complex functions so that a variety of functions can be analyzed in detail. This paper explores the form and proof of Taylor's theorem based on Peano and Lagrange remainder forms as well as the Polynomial interpolation. Then, examples of the application of Taylor’s theorem in mathematics fields, such as limit calculation and high-order derivatives computation, are discussed. Additionally, the applications of Taylor’s theorem in other related subjects are examined. Based on the chronological investigation, the Taylor theorem is widely used in physics, engineering, and other fields as a computational tool. The Taylor theorem has gradually been used in machine learning because of the rapid development of computer science in recent years. The purpose of this paper is to summarize the different forms and proofs of Taylor's formula and discuss the development of its application over time.
Application of Virtual reality for mindfulness practices

Virtual reality is an immersive technology that has a lot of applications in the modern world. Currently, there are many applications for virtual reality technology but most of that lies in the field of entertainment. Having a technology that can immerse the user in a hand-crafted environment can have a lot of positive impacts in many other fields. The purpose of this essay is to explore the application of this technology in the field of mindfulness and other alternatives. Furthermore, exploring the current proven effectiveness of this technology in the current stress epidemic where many people can benefit from widespread use. Lastly, looking at the software side of things and seeing how designing a compelling environment in the virtual world can contribute to the experience of mindfulness practices. In conclusion, this essay will cover the reasons why virtual reality can be used for mindfulness practices in the world today and what the pros and cons of this technology are.
Multi-scale Parallax Attention for Stereo Image Super-Resolution

Stereo image super-resolution (SR) has achieved great progress in recent years. However, the existing methods are unable to obtain rich cross-view information at a low computational cost. In addition, these methods treat each pixel equally when fusing the cross-view information with the intra-view information, resulting in non-robustness of the fused information. In this work, we propose a multi-scale parallax attention stereo super-resolution network (MPASSRnet) to address these problems. Firstly, we design a multi-scale parallax attention module (MSPAM), which computes the similarity between stereo images on multiple scale images based on the introduction of pixel-to-patch matching, thus acquiring multi-scale cross-view information at a low computational cost. Second, we propose a pixel attention fusion module (PAFM), which introduces spatial attention (SA) by calculating the correlation between cross-view information and intra-view information, and combines channel attention (CA) to make the fused information more robust. Finally, extensive experiments show that our method achieves state-of-the-art performance on the benchmark datasets.
Investigation of Using Large-Scale Swarm Optimizers to Optimize Sub-Problems in Cooperative Co-Evolution

Cooperative co-evolutionary algorithms (CCEAs) have witnessed giant success in solving large-scale optimization problems (LSOPs). However, most existing CCEAs use low-dimensional EAs to optimize the decomposed sub-problems. Such utilization of low-dimensional EAs may limit the effectiveness of CCEAs because some of the decomposed sub-problems may still be high-dimensional. Since there exist many non-decomposition based large-scale EAs, it is interesting to investigate the optimization effectiveness of CCEAs by using these non-decomposition based large-scale EAs to solve the decomposed sub-problems. To this end, this paper incorporates two state-of-the-art large-scale swarm optimizers into CCEAs with five state-of-the-art decomposition strategies to solve LSOPs. Experiments conducted on the CEC'2010 and CEC'2013 LSOP benchmark sets have shown that the two large-scale swarm optimizers help CCEAs with the five decomposition strategies achieve much better performance than the most widely used low-dimensional EA.
SCU: A Hardware Accelerator for Smart Contract Execution

Smart contracts and the blockchain have recently been widely used in many application fields. Current smart contracts are executed on general-purpose CPUs and still have a large room to improve performance. In this paper, we first analyze the most popular public blockchain platform Ethereum and characterize smart contracts running on its ecosystem. After identifying its performance limitations, we propose a heterogeneous processor Smart Contract Unit (SCU), which is a hardware-based accelerator in place of the current EVM design. With our proposed novel RISC-style SCU ISA and heterogeneous architecture, SCU can leverage instruction-level parallelism and transaction-level parallelism during smart contract processing and boost its execution performance. Furthermore, SCU can be configured and adapted to different workloads in order to remove bottlenecks. We implement and evaluate the proposed SCU design on a Xilinx FPGA platform. Our design achieves a significant speedup compared to the software implementation on an Intel CPU and runs a few times faster than state-of-the-art design.
A Blockchain Based Trustworthy Space in Cross-Domain Synergy

With the increasing demand for data sharing and value transmission in the digital era, the development of new generation information technologies are expected to break down system barriers and achieve efficient collaborations among different organizations. Cross-domain synergy has drawn much academic and industry attentions since it can form effective connections among various production factors, organizations, and affairs. However, due to the lack of mutual trust mechanisms, it’s hindered by the consideration of responsibility, risk and interest. In this paper, we study the trustworthy challenges in the shared 5G scenario systematically, i.e., system trustworthy, data trustworthy and behavior trustworthy, and propose a notion of Trustworthy Space. Furthermore, we design three independent trust zones for Trustworthy Space, which can ensure the integrity of multi-party data, autonomous interaction of systems, and verifiability of behavior. A typical application of 5G network co-construction and sharing shows that our proposed trusted infrastructure has achieved significant results.
A Community-based Strategy for Blockchain Sharding: Enabling More Budget-friendly Transactions

The promise of blockchain networks is occasionally overshadowed by challenges related to scalability and the escalation of gas fees. A significant portion of these gas fees arises from processing invalid or zero-value transactions, which imposes unnecessary costs and diminishes the technology’s intrinsic value. Sharding, which segments a blockchain into smaller, efficient shards, offers a solution, but determining the most effective partitioning is non-trivial. This paper unveils an innovative, budget-friendly approach to sharding by leveraging community detection algorithms. By clustering tightly-knit transactional communities, our method encourages more intrashard transactions, thus minimizing the higher fees associated with cross-shard transactions. As our experiments demonstrate, the result is a significant 50% reduction in transaction fees. Adopting community detection in the sharding process enhances blockchain scalability and creates a more budget-friendly ecosystem, ensuring users derive maximum value from every transaction.
An Implementation of Word Prediction for Gaze Swipe Text Entry

Gaze swipe, one of the text entry methods that do not require physical interaction with a device, involves sequentially tracing the characters of the desired word on a displayed keyboard using the user's gaze. This approach holds the promise of enabling quick word entry. However, previous gaze swipe method presents an issue where the time required for typing increases with longer words, consequently burdening the user. As a solution, there is a demand for an input word prediction method that presents the desired word for typing before the user completes tracing all the characters with their gaze. We have proposed a new gaze swipe typing method incorporating word prediction functionality. We will exhibit an application that implements the proposed method, so that users can experience that the method can be used in actual input and that the proposed method is useful in gaze-based text entry.
A Supervised Learning-Based Min/Max Voltage Estimation Model for All Nodes in Low-Voltage Networks

Voltage regulation is performed primarily to control low-voltage networks, which can be challenging for large networks. This study presents the development of a voltage estimation technique for low-voltage networks based on supervised learning. The proposed technology utilizes a feeder remote terminal unit (FRTU) and advanced metering infrastructure (AMI) measurement data to estimate the voltage range of a low-voltage system by deploying smart metering in a distribution network A multi-layer perceptron technique is implemented to perform near real-time voltage estimation using measured data from part of FRTU and AMI. The voltage range estimation model is simulated based on radial networks of the Korea Electric Power Corporation network system. The test results indicate that the proposed model can accurately estimate the maximum and minimum voltage range in near real-time tests and in photovoltaic integration scenarios with low error rates across various cases.