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Extracorporeal life support systems (ECLS) are life-sustaining measures for severe cardiovascular diseases, serving as bridging treatment either until cardiovascular function is restored or alternative treatment, such as heart transplantation or the implantation of permanent ventricular assist devices is performed. Given the insufficient evidence and frequent urgency of implantation without initial patient consent, the ethical challenges and psychological burden for patients, relatives and the interprofessional intensive care team are significant. As with any treatment, an appropriate therapeutic goal for ECLS treatment based on the indications and patient informed consent is mandatory. In order to integrate the necessary ethical considerations into everyday clinical practice, a structured algorithm for handling ECLS is proposed here, which takes ethical aspects into due account. [ABSTRACT FROM AUTHOR]

Over the years the number of research publications per year is growing exponentially. Finding research papers of quality from the massive literature of relevant articles is a challenging and time-consuming task. The approaches in the latest literature address citation recommendation by utilizing large bibliographic information and use machine learning and deep learning methods for the task. These techniques clearly require a large amount of training data as well as machines with high processing power. To overcome these issues, we propose a novel method by modifying the popular hyperlink induced topic search (HITS), a web page ranking algorithm, as citation recommendation using hyperlink induced topic search (CR-HITS) that works on a directed and weighted heterogeneous bibliographic network containing diverse types of nodes and edges. We define effective scoring schemes for nodes and edges based on basic bibliographic information like citations of papers, number of publications of an author, etc. Given a few seed papers, the citation recommendation algorithm CR-HITS is run on small neighborhoods of the seed papers and hence the time taken by the execution is very small to yield the final recommendations. To the best of our knowledge, HITS has been used for the first time for the citation recommendation problem. We perform extensive experimentation on DBLP (version-11) and ACM (version-9) datasets and compare the results with many baseline methods in terms of MAP, MRR, and recall@N measures. The performance of the proposed algorithms is superior with respect to the MAP metric and matches the second best for the other two metrics. Since the top two algorithms use deep learning methods and use much larger bibliographic information including abstracts of the papers, we claim that our approach utilizes very low resources, yet yields recommendations that are very close to the top recommendations. [ABSTRACT FROM AUTHOR]

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India has produced several drafts of data policies. In this work, they are referred to [1] JBNSCR 2018, [2] DPDPR 2018, [3] NSAI 2018, [4] RAITF 2018, [5] PDPB 2019, [6] PRAI 2021, [7] JPCR 2021, [8] IDAUP 2022, [9] IDABNUP 2022. All of them consider Artificial Intelligence (AI) a social problem solver at the societal level, let alone an incentive for economic growth. However, these policy drafts warn of the social disruptions caused by algorithms and encourage the careful use of computational technologies in various social contexts. Hence, the emerging data society and its implications in India's social contexts demand immense social science attention, which needs to be improved in the policy drafts, primarily because they are creations of industry stakeholders, technocrats, bureaucrats, and experts from tech schools. In the larger social milieu of digital infrastructure emerging, the fundamental question is whether India's national philosophy envisioned in the Indian constitution is reflected in the policy papers. The paper enquires whether the national data policy upholds the core values dispersed through the philosophy of the Indian constitution, which, among other things, is not confined only to inclusion, diversity, rights, liberty, justice and equality. By focusing on constitutional values, the paper seeks to offer a broader and more critical understanding of India's approach to AI policy by bringing together analyses of a wide array of policy documents available in the public realm. [ABSTRACT FROM AUTHOR]

Nowadays, the application of artificial intelligence (AI) algorithms and techniques is ubiquitous and transversal. Fields that take advantage of AI advances include sound and music processing. The advances in interdisciplinary research potentially yield new insights that may further advance the AI methods in this field. This special issue aims to report recent progress and spur new research lines in AI-driven sound and music processing, especially within interdisciplinary research scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative image encryption algorithm that leverages partial face recognition and DNA diffusion, building upon advancements in chaotic image encryption and face recognition technologies. The key is generated using the secure SHA-512 algorithm, while the chaotic sequence for the encryption algorithm is derived from the Chen system. This paper begins by employing facial recognition technology to identify and focus on specific facial regions within an image. Subsequently, DNA diffusion is applied to encrypt the recognized face segment via a diffusion-scrambling encryption process. Following this, the entire image undergoes diffusion utilizing the devised diffusion rules. Finally, a uniform scrambling operation based on Fisher-Yeats is performed on the entire image. The experimental results and safety analysis show that the information entropy can exceed 7.9973, the average correlation can be less than 0.0023, the pixel number change rate can reach 99.6052%, and the unified average change intensity can reach 33.4644%, all of which are very close to the ideal values. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method to improve loop closure detection within the domain of Simultaneous Localization And Mapping (SLAM) by integrating a Multi-Scale Attention and Semantic Guidance (MSA-SG) framework. In SLAM systems, accurate loop closure detection is essential for minimizing localization errors over time and ensuring the reliability of the constructed maps in robotics navigation through uncharted environments. Our proposed method utilizes EfficientNet-EA for robust feature extraction and introduces MSA-SG, a novel mechanism that synergizes multiscale attention with semantic guidance to focus on critical semantic features essential for loop closure detection. This approach ensures the prioritization of static environmental landmarks over transient and irrelevant objects, significantly enhancing the accuracy and efficiency of loop closure detection in complex and dynamic settings. Experimental validations on recognized datasets underscore the superiority of our approach, demonstrating marked improvements in precision, recall, and overall SLAM performance. This research highlights the significant benefits of leveraging semantic insights and attentional focus in advancing the capabilities of loop closure detection for SLAM applications. [ABSTRACT FROM AUTHOR]

To address the problem of the low success rate caused by the lack of verification mechanism of hashed time-locked algorithm in processing blockchain cross-chain transactions, this paper proposes blockchain cross-chain protocol based on improved hashed time-locked contract. Firstly, in order to improve the success rate of cross-chain transactions, this paper introduces the virtual account verification mechanism to verify the legitimacy of the transaction before processing. Secondly, this paper also proposes the parallel processing strategy based on the virtual trading pool, which makes unqualified-transactions in virtual trading pool process in parallel, and avoids to retrieve the account information which is obtained by crossing multi-chains. Finally, the proposed algorithm is compared with hashed time-locked algorithm (HTLC) and time-locked algorithm under different chain number. The success rate of the proposed algorithm is increased to more than 90%. For large-scale transactions, compared with HTLC algorithm, time-locked algorithm, the throughput of the proposed algorithm is increased by 72 and 44 percentage points respectively. Under different transaction distribution, the proposed algorithm also outperforms the two algorithms in terms of transaction delay. The experimental results show that this algorithm can effectively improve the success rate of cross-chain transaction and shorten the delay of cross-chain transaction. [ABSTRACT FROM AUTHOR]

Underwater target detection technology is very important for Marine resources detection and underwater environment perception. The problems of low-quality underwater images, a large amount of calculation, and low accuracy of target detection models still need to be solved. Firstly, a new underwater image enhancement algorithm based on multi-scale fusion is proposed. The algorithm combines the improved white balance algorithm and the improved CLAHE (Contrast Limited Adaptive Histogram Equalization) and realizes the multi-scale fusion strategy by introducing different weight maps, aiming to improve the visual effect and quality of underwater images. Secondly, to further improve the accuracy and efficiency of underwater target detection, an improved YOLOv5s lightweight underwater target detection algorithm is proposed. The algorithm combines the Ghost convolution module, EMA(Efficient Multi-Scale Attention) mechanism, and CARAFE (Content-Aware ReAssembly of FEatures) up-sampling mode. The comparison on the URPC(Underwater Robot Perception Challenge) public dataset shows that the proposed algorithm has achieved significant improvement in terms of model size, parameter amount, calculation amount, and mAP@0.5. Thirdly, to verify the effectiveness of the image enhancement algorithm and underwater target detection algorithm proposed in this paper, the ROV(Remote Operated Vehicle) is used to conduct experiments in the experimental pool. By designing underwater target detection experiments before and after image enhancement, the results show that the enhanced image significantly improves the detection ability of underwater targets. Finally, underwater target detection software is developed to provide strong support for the application of underwater-related fields. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the AAPF* algorithm, an innovative approach that synergistically integrates the A-star search algorithm (A*) with the artificial potential field (APF) method. This algorithm is designed to enhance safety and ensure smoother global path planning for autonomous vehicles, particularly addressing vehicle cornering constraints. Initially, for augmenting the safety of autonomous vehicle, we implement an obstacle expansion strategy with a factor of 2 units, enhancing environmental adaptability. The study then delves into the classical A* algorithm, examining its core principles and characteristics, leading to the development of novel heuristic functions and search strategies that address the limitations inherent in the classic A* algorithm. Subsequently, we explore the APF algorithm, recognized for its excellence in obstacle avoidance in path planning. The paper culminates in the amalgamation of the APF's repulsive field concept with the improved A* algorithm, crafting a comprehensive global planning algorithm tailored for autonomous vehicle path planning schemes. Experiments conducted in a simulated environment model validate the AAPF* algorithm's efficacy in improving both the safety and smoothness, demonstrating its potential for real-world applications. [ABSTRACT FROM AUTHOR]

Archaeological illustration is a graphic recording technique that delineates the shape, structure, and ornamentation of cultural artifacts using lines, serving as vital material in archaeological work and scholarly research. Aiming at the problems of low line accuracy in the results of current mainstream image generation algorithms and interference caused by severe mural damage, this paper proposes a mural archaeological illustration generation algorithm based on multi-branch feature cross fusion (U2FGAN). The algorithm optimizes skip connections in U2Net through a channel attention mechanism, constructing a multi-branch generator consisting of a line extractor and an edge detector, which separately identify line features and edge information in artifact images before fusing them to generate accurate, high-resolution illustrations. Additionally, a multi-scale conditional discriminator is incorporated to guide the generator in outputting high-quality illustrations with clear details and intact structures. Experiments conducted on the Dunhuang mural illustration datasets demonstrate that compared to mainstream counterparts, U2FGAN reduced the Mean Absolute Error (MAE) by 10.8% to 26.2%, while also showing substantial improvements in Precision (by 9.8% to 32.3%), Fβ-Score (by 5.1% to 32%), and PSNR (by 0.4 to 2.2 dB). The experimental results show that the proposed method outperforms other mainstream algorithms in archaeological illustration generation. [ABSTRACT FROM AUTHOR]

As unmanned vehicle technology advances rapidly, obstacle recognition and target detection are crucial links, which directly affect the driving safety and efficiency of unmanned vehicles. In response to the inaccurate localization of small targets such as pedestrians in current object detection tasks and the problem of losing local features in the PointPillars, this paper proposes a three-dimensional object detection method based on improved PointPillars. Firstly, addressing the issue of lost spatial and local information in the PointPillars, the feature encoding part of the PointPillars is improved, and a new pillar feature enhancement extraction module, CSM-Module, is proposed. Channel encoding and spatial encoding are introduced in the new pillar feature enhancement extraction module, fully considering the spatial information and local detailed geometric information of each pillar, thereby enhancing the feature representation capability of each pillar. Secondly, based on the fusion of CSPDarknet and SENet, a new backbone network CSE-Net is designed in this paper, enabling the extraction of rich contextual semantic information and multi-scale global features, thereby enhancing the feature extraction capability. Our method achieves higher detection accuracy when validated on the KITTI dataset. Compared to the original network, the improved algorithm's average detection accuracy is increased by 3.42%, it shows that the method is reasonable and valuable. [ABSTRACT FROM AUTHOR]

Many sensitive applications require that data remain confidential and undisclosed, even for intrusion detection objectives. For this purpose, the detection of anomalies in encrypted data has become increasingly vital. Deep learning models are becoming good tools to detect anomalies in encrypted data without the need to pass through data decryption. This paper presents a systematic review focusing on the advancements made in deep learning models for intrusion detection over encrypted data with privacy preservation. This study aims to guide researchers on how to select the right tools to set up an intrusion detection system over encrypted data with privacy preservation. The study presented the context and challenges of intrusion detection on encrypted data and how machine learning-based solutions can circumvent these challenges. The paper looks at recently proposed solutions, examines metrics for assessing model performance, and evaluates frequently used reference datasets. Deep learning models are also evaluated with statistics on the most frequent models, datasets, and encryption tools. The performance metrics of the studied solutions are investigated as a function of the encryption tools, the deployed deep learning models, the privacy preservation tools, the deployed datasets, and the eventual additional tools and algorithms. Our recommendations help researchers evaluate their proposals for preserving privacy and detecting intrusions on encrypted data using deep learning techniques. [ABSTRACT FROM AUTHOR]

The paper presents a study of robot interaction algorithms. The study of algorithms that control the behavior of robots is relevant due to the development of robotic means and their wide application in various fields: in manufacturing, medicine, military. The paper considers the application of nanorobot swarms in medicine. The study is performed using simulation methods. The simulation system NetLogo is used as a toolkit. The Netlogo simulation system is agent-based system. In the first part of the article, experiments using reactive agents are performed. Next, we consider intelligent agents that can make decisions related to the optimization of the algorithms by which they act. A series of experiments were carried out with intelligent agents and the values of parameters that affect the time of destruction of "bad" cells were determined. [ABSTRACT FROM AUTHOR]

Manual term extraction is similar to literal meaning: A translator browses text, classifies words, and prepares for translation. Terminology, as a centralized carrier of expertise, creation, popularization, and disappearance, dynamically reflects the development and evolution of an industry. The automatic extraction of terminology is a key technology for creating a professional terminology database, and it is also a key topic in the field of natural language processing. The purpose of this paper is to study how to analyse a term extraction algorithm based on machine learning and a comprehensive feature strategy. Focusing on the problems of poor generality and single statistical features of current term extraction algorithms, this paper proposes an improved domain ontology term extraction algorithm based on a comprehensive feature strategy. Moreover, automatic term extraction experiments based on a word-based maximum entropy model and a conditional random field model based on machine learning are conducted in this paper. Its word-based conditional random field model outperforms the maximum entropy model. The experimental results show that the algorithm based on the comprehensive feature strategy improves the accuracy by 8.6% compared with the TF-IDF algorithm and the C-value term extraction algorithm. This algorithm can be used to effectively extract the terms in a text and has good generality. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments. [ABSTRACT FROM AUTHOR]

Conventional wireless sensors have difficulty solving the problem of energy limitation, especially in sensor networks in hard-to-reach extreme areas. In order to solve the problem that it is difficult to charge wireless sensors in the field using conventional energy sources, the energy harvesting wireless senor is designed to use renewable energy sources for power supply. Considering the uncertainty and unknown nature of renewable energy generation, and the need for effective energy management of the sensor. In this paper, an Node Power Control Optimization (NPCO) power allocation algorithm is proposed to adjust the power allocation problem of wireless sensor nodes within each time slot. In addition, to address the unknown and random nature of energy arrival, this paper proposes a CLSTM model based on deep learning to predict the energy arrival. The continuous autonomous energy management of wireless sensor nodes is achieved by combining the CLSTM prediction results using the NPCO algorithm. The algorithm is applicable to continuous states and is able to show good performance in the verification of real solar data. The algorithm achieves better performance in terms of long-term average net bit rate compared to the current DDPG algorithm, AC algorithm, and Lyapunov optimization algorithm. [ABSTRACT FROM AUTHOR]

The calculation of the spectral blueing operator in the traditional spectral blueing method has singularity, which leads to poor performance in post-stack seimic frequency expansion. To this end, a frequency spreading technique based on matching pursuit (MP) and spectral blueing is proposed. Through time–frequency analysis processing, it is shown that the seismic signal extracted by matching tracking method has good stability and higher resolution. The specific process of the method in this paper firstly uses the matching tracking method to accurately divide the post-stack seismic data into multiple frequency-division seismic bodies; then, in the process of calculating the spectral blueing ization operators for each frequency band, the weighting idea is used to calculate the weights of the optimized spectral blueing ization operators for each frequency band based on the differences in energy in different frequency bands; finally, the optimized spectral blueing operator is convolved with seismic reflection coefficients to obtain high-resolution seismic data. The actual test results of poststack seismic data have proven that the frequency raising method proposed in this paper is superior to the traditional spectral blueing ization algorithm, greatly improving the high-frequency component information of poststack seismic data. After frequency extension, there are more seismic events and higher resolution. Finally, the practicability and rationality of the seismic data after frequency extraction are verified by a series of operations such as attribute extraction, well seismic calibration and inversion. [ABSTRACT FROM AUTHOR]

The Distributed power flow controller can be considered as advance power flow controller which is combination of series and shunt compensator without DC link. In this paper Space vector Pulse width Modulation employs in Distributed power flow Controller for Harmonics Reduction and power quality improvement. A new technique SVPWM adopted which reduces the harmonics and the same time improves power quality and increase transient stability. In this paper a new technique of Multi objective Grey wolf algorithm used to optimize the controller parameter of DPFC. For the design of PI controller GWO algorithm can be used for better optimistic performance of DPFC. As a result of that power quality and voltage profile improves drastically and the same time harmonics also reduced remarkable level of 4.62% of Voltage THD and 1.8% of current THD. The simulation result and hardware model Prove the feasibility of proposed Configuration. [ABSTRACT FROM AUTHOR]

Human pose estimation is an important task in computer vision, which can provide key point detection of human body and obtain bone information. At present, human pose estimation is mainly utilized for detection of large targets, and there is no solution for detection of small targets. This paper proposes a multi-channel spatial information feature based human pose (MCSF-Pose) estimation algorithm to address the issue of medium and small targets inaccurate detection of human key points in scenarios involving occlusion and multiple poses. The MCSF-Pose network is a bottom-up regression network. Firstly, an UP-Focus module is designed to expand the feature information while reducing parameter computation during the up-sampling process. Then, the channel segmentation strategy is adopted to cut the features, and the feature information of multiple dimensions is retained through different convolutional groups, which reduces the parameter lightweight network model and makes up for the loss of the feature information associated with the depth of the network. Finally, the three-layer PANet structure is designed to reduce the complexity of the model. With the aid of the structure, it also to improve the detection accuracy and anti-interference ability of human key points. The experimental results indicate that the proposed algorithm outperforms YOLO-Pose and other human pose estimation algorithms on COCO2017 and MPII human pose datasets. [ABSTRACT FROM AUTHOR]

Cloud model similarity algorithm is an important part of cloud modelling theory. Most of the existing cloud model similarity algorithms suffer from poor discriminability, poor classification, unstable results, and low time efficiency. In this paper, a new similarity algorithm is proposed that considers the triangular cloud model distance and shape. First, according to the D T distance formula, a new exponential closeness measure is defined, with which the distance similarity of cloud models is characterized. Then, the shape similarity is calculated according to the variance of the cloud model cloud drops. Finally, the two similarities are synthesized to define a similarity algorithm for determining the distance from the D T distance formula and shape based on the triangular cloud model (DDTSTCM). In this paper, discriminability, stability, efficiency and theoretical interpretability are taken as the evaluation indices. Equipment security system capability evaluation experiment, cloud model differentiation simulation experiment and time series classification accuracy experiment are set up to verify the effectiveness of the algorithm in terms of the four above aspects. The experimental results show that DDTSTCM has good differentiation and excellent classification effects. In the classification experiment for the time series, the average classification accuracy of DDTSTCM reaches 91.78%, which is at least 2.78% higher than those of the other seven commonly used algorithms. The CPU running efficiency of DDTSTCM is also extremely high, and the average CPU running time of group training is always on the order of milliseconds, which effectively reduces the time cost. Finally, a case study is conducted to analyse a risk assessment problem for China's island microgrid industry, and the evaluation results based on DDTSTCM are in line with human cognition and have good value for engineering applications. [ABSTRACT FROM AUTHOR]

In response to the challenges of accurately identifying event triggers and elements in long texts related to aquaculture, existing models struggle to differentiate between elements and triggers, as well as effectively recognize complete entity texts. To tackle this issue, this study proposes an algorithm for extracting fish disease events based on pseudo triggers and augmented event element data. The method starts by constructing pseudo samples using the original dataset. Two types of noise datasets are then generated: a trigger noise dataset constructed based on fish disease triggers and an entity noise dataset with varying levels of entity noise constructed based on fish disease entities. Next, three parallel neural networks are deployed to extract sample features from these datasets. The fish disease event extraction for the source dataset employs multi-label classification. For the trigger noise dataset, the sample features are activated using the sigmoid function, and the MRSE loss is utilized for optimization of this branch. For the entity noise dataset, the sample features are activated using the Relu function, and the XOR loss is used for optimization. Finally, the losses from the three branches are combined with weighted summation to obtain the fusion loss. The experimental results on the fish disease dataset used in this paper show that the proposed algorithm achieves an average accuracy of 78.71%, 78.95%, and 79.43% on F1, recall, and precision, respectively, which is a maximum improvement of 11.201%, 11.849%, and 12.421% in accuracy with respect to the baseline model on F1, recall, and precision, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the received signal strength index (RSSI) in wireless positioning system, an improved weighted KNN fingerprint positioning algorithm is proposed in this paper. The algorithm pre-processes fingerprint data in offline stage that including eliminating outliers and Kalman filtering first, in order to improve the accuracy of data acquisition. Secondly, the fingerprint data is partitioned by using RSSI to attenuate obstacles such as walls. Then, points with significant RSSI differences in each region are selected as regional feature points, and the distance between RSSI of test points and feature points in each region is calculated respectively to determine the region in which the test points are located. Geometric method is used to analyse and define the correlation degree, and KNN is re-weighted to achieve accurate positioning in the region. Finally, experiments were carried out in the indoor environment to complete the establishment of the fingerprint database. Compared with the existing NN, KNN and WKNN, the experimental analysis results show that the accumulated error and average error are better than the traditional algorithm with the increase of measurement points, which has reference value for the complex environment positioning technology. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive study of the simultaneous localization and mapping (SLAM) algorithm for robot localization and navigation in unknown environments. The SLAM algorithm is a widely used approach for building a map of an environment and estimating the robot's position within it, which is especially useful in dynamic and unstructured environments. The paper discusses various SLAM techniques, including the Kalman filter (KF) and GraphSLAM algorithms, and their use in probabilistic estimation of the robot's position and orientation. The paper also explores different path-planning techniques that can be used with the map created by the SLAM algorithm to generate collision-free paths for the robot to navigate toward its goal. The paper also discusses recent advances in deep learning-based SLAM algorithms and their applications in indoor navigation with ORB and RGB-D cameras. The research concludes that SLAM-based robot localization and navigation algorithms are a promising approach for robots navigating in unstructured environments and present various opportunities for future research. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

Fruit recognition and location are the premises of robot automatic picking. YOLOv3 has been used to detect different fruits in complex environment. However, for the object with definite features, the complex network structure will increase the computing time and may cause overfitting. Therefore, this paper has carried out a lightweight design for the YOLOv3. This paper proposed an improved T-Net to detect tomato images. Firstly, the T-Net reduces the residual network layers. This paper changed the number of cycles in each group of the residual unit to 1, 2, 2, 1, and 1. Second, two feature layers with different scales are selected according to the features of tomatoes. Meanwhile, the convolutional layer at the neck has been reduced by two layers. Finally, the location and approximate diameter of the ripe tomato are obtained by combining the node information of the Intel D435i camera and T-Net in the Robot Operation System. T-Net obtains mean average precision (mAP) of 99.2%, F1-score of 98.9%, precision of 99.0%, and recall of 98.8% at a detection rate of 104.2 FPS. The proposed T-Net has outperformed the YOLOv3 with 0.4%, 0.1%, and 0.2% increase in precision, mAP, and F1-score. The detection speed of T-Net is 1.8 times faster than YOLOv3. The mean errors of the center coordinates and diameter of the tomato are 8.5 mm and 2.5 mm, respectively. This model provides a method for efficient real-time detection and location of tomatoes. [ABSTRACT FROM AUTHOR]

This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]

The popular block diagonal representation subspace clustering approach shows high effectiveness in dividing a high-dimensional data space into the corresponding subspaces. However, existing subspace clustering algorithms have some weaknesses in achieving high clustering performance. This paper presents a multi-geometric block diagonal representation subspace clustering with low-rank kernel (MBDR-LRK) method that includes two major improvements. First, as visual data often exists on a Riemannian manifold not captured by Euclidean geometry, we harness the multi-order data complementarity to develop a multi-geometric block diagonal representation (MBDR) subspace clustering. Secondly, the proposed MBDR-LRK approach ensures the low-rankness in the mapped space, by adapting the kernel matrix to a pre-defined one rather than relying on a fixed kernel as in traditional methods. The paper also presents details on the monotonic decrease of the objective function and the boundedness and convergence of the affinity matrix, and the experimental results prove the convergence of the proposed method. Based on the MATLAB development environment, the proposed MBDR-LRK algorithm outperforms other related algorithms and obtained an accuracy of 88.70% on the ORL (40 classes), 89.39% on the Extended Yale B (38 classes), 50.22% on the AR (100 classes) and 75.47% on the COIL (50 classes) datasets. [ABSTRACT FROM AUTHOR]

Language models and other recent machine learning paradigms blur the distinction between generative and discriminative tasks, in a continuum that is regulated by the degree of pre- and post-supervision that is required from users, as well as the tolerated level of error. In few-shot inference, we need to find a trade-off between the number and cost of the solved examples that have to be supplied, those that have to be inspected (some of them accurate but others needing correction) and those that are wrong but pass undetected. In this paper, we define a new Supply-Inspect Cost Framework, associated graphical representations and comprehensive metrics that consider all these elements. To optimise few-shot inference under specific operating conditions, we introduce novel algorithms that go beyond the concept of rejection rules in both static and dynamic contexts. We illustrate the effectiveness of all these elements for a transformative domain, data wrangling, for which language models can have a huge impact if we are able to properly regulate the reliability-usability trade-off, as we do in this paper. [ABSTRACT FROM AUTHOR]

In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5–69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length. [ABSTRACT FROM AUTHOR]

In the field of autonomous mobile robots, sampling-based motion planning methods have demonstrated their efficiency in complex environments. Although the Rapidly-exploring Random Tree (RRT) algorithm and its variants have achieved significant success in known static environment, it is still challenging in achieving optimal motion planning in unknown dynamic environments. To address this issue, this paper proposes a novel motion planning algorithm Bi-HS-RRT X , which facilitates asymptotically optimal real-time planning in continuously changing unknown environments. The algorithm swiftly determines an initial feasible path by employing the bidirectional search. When dynamic obstacles render the planned path infeasible, the bidirectional search is reactivated promptly to reconstruct the search tree in a local area, thereby significantly reducing the search planning time. Additionally, this paper adopts a hybrid heuristic sampling strategy to optimize the planned path quality and search efficiency. The convergence of the proposed algorithm is accelerated by merging local biased sampling with nominal path and global heuristic sampling in hyper-ellipsoid region. To verify the effectiveness and efficiency of the proposed algorithm in unknown dynamic environments, numerous comparative experiments with existing algorithms were conducted. The experimental results indicate that the proposed planning algorithm has significant advantages in planned path length and planning time. [ABSTRACT FROM AUTHOR]

Some particular shaped datasets, such as manifold datasets, have restrictions on density peak clustering (DPC) performance. The main reason of variations in sample densities between clusters of data and uneven densities is not taken into consideration by the DPC algorithm, which could result in the wrong clustering center selection. Additionally, the use of single assignment method is leads to the domino effect of assignment errors. To address these problems, this paper creates a new, improved density peaks clustering method use the similarity assignment strategy with K nearest Neighbors (IDPC-SKNN). Firstly, a new method for defining local density is proposed. Local density is comprehensively consider in the proportion of the average density inside the region, which realize the precise location of low-density clusters. Then, using the samples' K-nearest neighbors information, a new similarity allocation method is proposed. Allocation strategy successfully address assignment cascading mistakes and improves algorithms robustness. Finally, based on four evaluation indicators, our algorithm outperforms all the comparative clustering algorithm, according to experiments conducted on synthetic, real world and the Olivetti Faces datasets. [ABSTRACT FROM AUTHOR]

Conveyor belt deviation is a commmon and severe type of fault in belt conveyor systems, often resulting in significant economic losses and potential environment pollution. Traditional detection methods have obvious limitations in fault localization precision and analysis accuracy, unable to meet the demands of efficient and real-time fault detection in complex industrial scenarios. To address these issues, this paper proposes an improved detection algorithm based on YOLOv8, aiming to achieve efficient and accurate detection during the operation of the belt. Firstly, an Enhanced Squeeze-and-Excitation (ESE) module is incorporated into C2f to boost feature extraction for rollers and belts. Secondly, the construction of the BiFPN_DoubleAttention module in the neck network enhances bidirectional feature fusion and attention mechanism, thereby improving multi-scale object localization accuracy under complex environments. Then, a Multi-Head Self-Attention (MHSA) mechanism is introduced in the head network, better capturing positional features of small roller targets and belt areas in various environments, thus enhancing detection performance. Finally, extensive experiments are conducted on a self-built dataset, achieving an accuracy of 98.1%, mAP0.5 of 99.0%, and a detection speed of 46 frames per second (FPS). This method effectively handles variations and disturbances in the conveyor belt transportation environment, meeting real-time diagnostic needs for belt misalignment in the industry. [ABSTRACT FROM AUTHOR]

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The Grey Wolf Optimizer (GWO) is one of the more successful swarm-based intelligent algorithms in recent years, but the shortcomings of the Grey Wolf Optimizer are revealed as the problems handled become progressively more complex. For this purpose, this paper presents a new variant of GWO and names its Hybrid Contact List Subpopulation Mixed Evolution Grey Wolf Optimizer (CSELGWO). In the paper first introduces the Contact List Mechanism (CLM) to obtain high quality local optimal information in the search space. This is followed by the Hybrid Contact List Subpopulation Generation (HCSG) mechanism, which utilizes the information in the Contact List to assist in the updating of the Subpopulation and interacts with the main population through Subpopulation Mixed Evolution (SME) to interact with the main population, thus significantly improving population diversity and convergence accuracy. In addition, the proposed Levy Flight with archives and Activation Mechanism (LFAA) can moving away from local optimality by reasonable judgment. We evaluated it using 66 test functions and showed excellent convergence speed, stability and accuracy. Additionally, when compared with the top-performing algorithm from the CEC2020 Real World Competition, CSELGWO demonstrates effective solutions to real-world problems. Finally, we compared LSHADE_cnEpSin with LSHADE_SPACMA. Although CSELGWO does not outperform these LSHADE variants in terms of convergence accuracy and standard deviation obtained, it shows excellent performance on certain types of functions, indicating excellent potential. [ABSTRACT FROM AUTHOR]

This paper introduces an algorithm to construct a bidirectional causal graph using an augmented graph. The algorithm decomposes the augmented graph, significantly reducing the size of the variable set required for conditional independence testing. Simultaneously, it preserves the fundamental structure of the augmented graph after decomposition, saving time and cost in constructing a global skeleton graph. Through experiments on discrete and continuous datasets, the algorithm demonstrates a clear advantage in runtime compared to traditional methods. In large-scale sparse networks, the training time is only about one-tenth of traditional methods. Additionally, the algorithm shows improvement in terms of construction error. Since the input to the algorithm is an augmented graph, this paper also discusses the impact on construction error when using both real and generated augmented graphs as input. Furthermore, the concept of markov blanket is extended to multivariate regression chain graphs, providing a method for rapidly constructing augmented graphs given certain prior knowledge. [ABSTRACT FROM AUTHOR]

Weeding is an important part of agricultural production. With the development of science and technology, automated weeding is regarded as the future development direction, and how to accurately and efficiently detect plants in the field is one of the key points. Corn seedlings and weeds are similar in color, shape and other characteristics, which brings serious challenges to plant detection. In this paper, we propose an improved model based on YOLOv7-tiny, called DC-YOLO. To improve the extraction of key features in the model, we propose Dual Coordinate Attention model (DCA). In addition, we introduce the Content-Aware ReAssembly of FEatures (CARAFE) operator to represent the up-sampling process as a learnable feature reorganization, which enriches the feature information of the sampled images. Finally, we decoupled the detection head to minimize conflicts between features from different tasks. The results show that applying the proposed method to corn and weed datasets, the detection accuracy of the model reaches 95.7% mean Average Precision (mAP@0.5), the computational effort of the model is 13.083 Giga Floating-point Operations (GFLOPs), and the parameter size is 5.223 Millon (M), which is better than the rest of the mainstream light-weight target detection model. [ABSTRACT FROM AUTHOR]

Digital technology has become crucial for the protection of cultural relics, enabling their acquisition, preservation, exhibition, and dissemination in a non-contact manner.At present, the three-dimensional(3D) visualization technology of cultural relics focuses on the research of non-true 3D visualization, which cannot provide continuous viewing angle and takes a long time to render. This paper focuses on the acquisition and reproduction of digital information related to cultural relics, and proposes to achieve the true 3D display of cultural relics from a continuous perspective based on integrated imaging(InIm). Building up on traditional InIm, an Element image array (EIA) generation algorithm based on local depth template matching is designed by using spatial geometry relation. Experimental results show that the algorithm not only enables naked-eye 3D visualization of cultural relics, but also the generation speed is more than twice that of the compared 3D display data source generation algorithm. [ABSTRACT FROM AUTHOR]

Presently, multi-label classification algorithms are mainly based on positive and negative logical labels, which have achieved good results. However, logical labeling inevitably leads to the label misclassification problem. In addition, missing labels are common in multi-label datasets. Recovering missing labels and constructing soft labels that reflect the mapping relationship between instances and labels is a difficult task. Most existing algorithms can only solve one of these problems. Based on this, this paper proposes a soft-label recover based label-specific features learning (SLR-LSF) to solve the above problems simultaneously. Firstly, the information entropy is used to calculate the confidence matrix between labels, and the membership degree of soft labels is obtained by combining the label density information. Secondly, the membership degree and confidence matrix are combined to construct soft labels, and this process not only solves the problem of missing labels but also obtains soft labels with richer semantic information. Finally, in the process of learning specific label features for soft labels. The local smoothness of the labels learned through stream regularization is complemented by the global label correlation, thus improving the classification performance of the algorithm. To demonstrate the effectiveness of the proposed algorithm, we conduct comprehensive experiments on several datasets. [ABSTRACT FROM AUTHOR]

Zonal calibration algorithm is the most widely used method to extend the measurement range of five-hole probes. However, large measurement error will be aroused near the boundary between two neighboring zones and this is acknowledged as the inner boundary measurement problem of zonal calibration algorithm. To tackle this problem, a two-dimensional uniform flow model is developed in this paper to describe the relationship between pressure from holes and flow angles. Based on this model, a method to adjust the boundary between two neighboring zones automatically with respect to inlet flow conditions is developed. With this novel method, the data extrapolation of zonal calibration algorithm at measurement stations near the boundary between two neighboring zones is avoided, and the corresponding large measurement error is eliminated. According to the experimental data, maximum measurement error of total pressure and flow angle can reach 7.5% and 3.2°, and will be reduced to 0.89% and 0.12° by the novel method. Resultantly, the inner boundary measurement problem of zonal calibration algorithm is solved. Influences of several key parameters on the measurement accuracy of the novel method are investigated too, and criteria to adjust the boundary between two neighboring zones are given. Conclusions of this paper can be used to further improve the accuracy of five-hole probes in measuring large angle flows. [ABSTRACT FROM AUTHOR]

The quality of communication between any two users in multi-hop wireless sensor networks directly depends upon the path selection among the available paths between end-users. The issue of selecting the optimized path from source to a destination becomes the necessary criteria for effective communication between end-users. The art of work mainly focuses on the selection of the path which has the best available bandwidth however they do not consider other network parameters such as distance, energy, the intensity of traffic which plays a critical role in routing. In this paper, an algorithm is presented for the selection of optimized route from source to destination by considering different network parameters along with the bandwidth and rank is given to all the available routes from source to destinations per their weights. The paper is validated using network simulator. [ABSTRACT FROM AUTHOR]

Aiming at the electric vehicle charging pile control system has the characteristics of multi-parameter, strong coupling and non-linearity, and the existing traditional PID control and fuzzy PID control methods have the problems of slow charging speed, poor control performance and anti-interference ability, as well as seriously affecting the service life of the battery, this paper designs a kind of improved particle swarm algorithm to optimize the PID controller of the charging control system for electric vehicle charging piles, and utilizes the improved particle swarm algorithm to Adaptive and precise adjustment of proportional, integral and differential parameters, so that the system quickly reaches stability, so as to improve the accuracy of the system control output current or voltage. Simulation results show that the optimized system response speed of the improved particle swarm algorithm is improved by 3.077 s, the overshooting amount is reduced by 1.01%, and there is no oscillation, which has strong adaptability and anti-interference ability, and can significantly improve the control accuracy and charging efficiency of the charging pile control system. [ABSTRACT FROM AUTHOR]

Identifying discriminative patterns, known as shapelets, within time series is a critical step in many time series classification tasks. A major limitation of shapelet learning is that often hindered by their unsupervised methods, treating shapelet learning as an unsupervised subsequence clustering process and discovery based on pre-defined metric, which performed sequentially. This sequential procedure presents challenges, as it fails to establish a direct connection between shapelets and samples, and lacks the capacity to explicitly incorporate label information. In this paper, we proposed a novel shapelet learning algorithm called Discriminative Shapelet Learning via Temporal Clustering and Matrix Factorization (DSLMF). DSLMF introduced a joint framework that combines matrix factorization and coherent temporal clustering to discovery salient and coherent feature subsets. To further enhance discriminability and prevent arbitrary shapelet shapes, DSLMF integrates a label-specific shapelet regularization as a guiding mechanism enabling the learning of shapelets optimized for higher classification performance. The proposed algorithm has shown to be effective for capturing the temporal cluster structure and interpretability of shapelet-based method. The results of experiments showcased in this paper highlight DSLMF's effectiveness in capturing temporal cluster structures and learning meaningful shapelets, ultimately leading to promising performance on benchmark datasets. [ABSTRACT FROM AUTHOR]

In a seminal paper in 2013, Witt showed that the (1+1) Evolutionary Algorithm with standard bit mutation needs time (1 + o (1)) n ln n / p 1 to find the optimum of any linear function, as long as the probability p 1 to flip exactly one bit is Θ (1) . In this paper we investigate how this result generalizes if standard bit mutation is replaced by an arbitrary unbiased mutation operator. This situation is notably different, since the stochastic domination argument used for the lower bound by Witt no longer holds. In particular, starting closer to the optimum is not necessarily an advantage, and OneMax is no longer the easiest function for arbitrary starting positions. Nevertheless, we show that Witt's result carries over if p 1 is not too small, with different constraints for upper and lower bounds, and if the number of flipped bits has bounded expectation χ . Notably, this includes some of the heavy-tail mutation operators used in fast genetic algorithms, but not all of them. We also give examples showing that algorithms with unbounded χ have qualitatively different trajectories close to the optimum. [ABSTRACT FROM AUTHOR]

This paper investigates a simple case of the state-agent-environment model of artificial intelligence theory. The general framework of AI in this part provides search and planning and is based on the development of modelling mathematical formalisms with a consequent validation-check, and pathfinding stages. The application problem and the use case considered in this paper belongs to the domain of personalized medicine. The problem is known as the dynamic treatment regime (DTR), and we demonstrate that in addition to traditional AI techniques for such problems, a simpler graph theoretical development can be incorporated that solves the validation and pathfinding problems with less efforts and complexities. The results obtained are achieved by using graph connectivity checking algorithms, algorithms of pathfinding in rooted trees, and checking additional graph components to be a one dicycle cactus graph; and provide a means of consecutive restructuring the whole graph for the connectivity property that preserves the validated DTR policy. [ABSTRACT FROM AUTHOR]

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. [ABSTRACT FROM AUTHOR]

The increasing ubiquity of machine learning (ML) motivates research on algorithms to "explain" models and their predictions—so-called Explainable Artificial Intelligence (XAI). Despite many publications and discussions, the goals and capabilities of such algorithms are far from being well understood. We argue that this is because of a problematic reasoning scheme in the literature: Such algorithms are said to complement machine learning models with desired capabilities, such as interpretability or explainability. These capabilities are in turn assumed to contribute to a goal, such as trust in a system. But most capabilities lack precise definitions and their relationship to such goals is far from obvious. The result is a reasoning scheme that obfuscates research results and leaves an important question unanswered: What can one expect from XAI algorithms? In this paper, we clarify the modest capabilities of these algorithms from a concrete perspective: that of their users. We show that current algorithms can only answer user questions that can be traced back to the question: "How can one represent an ML model as a simple function that uses interpreted attributes?". Answering this core question can be trivial, difficult or even impossible, depending on the application. The result of the paper is the identification of two key challenges for XAI research: the approximation and the translation of ML models. [ABSTRACT FROM AUTHOR]

Flocking cohesion is critical for maintaining a group's aggregation and integrity. Designing a potential function to maintain flocking cohesion unaffected by social distance is challenging due to the uncertainty of real-world conditions and environments that cause changes in agents' social distance. Previous flocking research based on potential functions has primarily focused on agents' same social distance and the attraction–repulsion of the potential function, ignoring another property affecting flocking cohesion: well depth, as well as the effect of changes in agents' social distance on well depth. This paper investigates the effect of potential function well depths and agent's social distances on the multi-agent flocking cohesion. Through the analysis, proofs, and classification of these potential functions, we have found that the potential function well depth is proportional to the flocking cohesion. Moreover, we observe that the potential function well depth varies with the agents' social distance changes. Therefore, we design a segmentation potential function and combine it with the flocking control algorithm in this paper. It enhances flocking cohesion significantly and has good robustness to ensure the flocking cohesion is unaffected by variations in the agents' social distance. Meanwhile, it reduces the time required for flocking formation. Subsequently, the Lyapunov theorem and the LaSalle invariance principle prove the stability and convergence of the proposed control algorithm. Finally, this paper adopts two subgroups with different potential function well depths and social distances to encounter for simulation verification. The corresponding simulation results demonstrate and verify the effectiveness of the flocking control algorithm. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [ABSTRACT FROM AUTHOR]