Redundancy Problem Research Articles

Inductive State-Relabeling Adversarial Active Learning With Heuristic Clique Rescaling.

Active learning (AL) is to design label-efficient algorithms by labeling the most representative samples. It reduces annotation cost and attracts increasing attention from the community. However, previous AL methods suffer from the inadequacy of annotations and unreliable uncertainty estimation. Moreover, we find that they ignore the intra-diversity of selected samples, which leads to sampling redundancy. In view of these challenges, we propose an inductive state-relabeling adversarial AL model (ISRA) that consists of a unified representation generator, an inductive state-relabeling discriminator, and a heuristic clique rescaling module. The generator introduces contrastive learning to leverage unlabeled samples for self-supervised training, where the mutual information is utilized to improve the representation quality for AL selection. Then, we design an inductive uncertainty indicator to learn the state score from labeled data and relabel unlabeled data with different importance for better discrimination of instructive samples. To solve the problem of sampling redundancy, the heuristic clique rescaling module measures the intra-diversity of candidate samples and recurrently rescales them to select the most informative samples. The experiments conducted on eight datasets and two imbalanced scenarios show that our model outperforms the previous state-of-the-art AL methods. As an extension on the cross-modal AL task, we apply ISRA to the image captioning and it also achieves superior performance.

HMS-TENet: A hierarchical multi-scale topological enhanced network based on EEG and EOG for driver vigilance estimation

Driving vigilance estimation is an important task for traffic safety. Nowadays, electroencephalography (EEG) and electrooculography (EOG) have made some achievements in vigilance estimation, but there are still some challenges: 1) The traditional approachs with direct multimodal fusion may face the problems of information redundancy and data dimensionality mismatch; 2) Capture key discriminative features during multimodal fusion without losing specific patterns to each modality. In order to solve the above problems, this paper proposes a approach with fusion of EEG and EOG features in split bands, which not only preserves the information about brain activities in different bands of EEG, but also effectively integrates the relevant information of EOG. On this basis, we further propose a hierarchical multi-scale topological enhanced network (HMS-TENet). This network first introduces a pyramid pooling structure (PPS) to capture contextual relationships from different discriminative perspectives. And then we design a selective convolutional structure (SCS) for adaptive sense-field selection, which enables us to mine the desired discriminative information in small-size features. In addition, we design a topology self-aware attention to enhance the learning of representations of complex topological relationships among EEG channels. Finally, the output of the model can be selected for both regression and classification tasks, providing higher flexibility and adaptability. We demonstrate the robustness, generalizability, and utility of the proposed method based on intra-subject and cross-subject experiments on the SEED-VIG public dataset. Codes are available at https://github.com/tangmeng28/HMS-TENet.

An effective Key Frame Extraction technique based on Feature Fusion and Fuzzy-C means clustering with Artificial Hummingbird

Key frame extraction is very important in video summarization and content-based video analysis to address the problem of data redundancy in a video. Key frame extraction enables quick navigation and expert video arrangement in many applications. The visually impaired can benefit from the use of key frame extraction for rapid object recognition and tracking. Most key frame extraction techniques consider only a single visual feature instead of multiple features or full pictorial information of the video. This study proposes a key frame extraction method from a video that (i) first removes insignificant frames by pre-processing, (ii) second, four visual and structural feature differences among the consecutive frames are extracted and aggregated to identify informative frames, (iii) third, to cluster the obtained frames, a hybrid FCM-AHA method is proposed by combining Fuzzy C-means(FCM) with artificial hummingbird optimization algorithm (AHA) to circumvent the local minima trapping problem of FCM, and finally, from each cluster, the two frames having greatest Euclidean distance from all the other frames within a cluster is selected as key frames to remove redundant frames. Experimental results on the Open video and YouTube datasets show that the suggested method outperforms state-of-the-art methods both in terms of subjective qualitative analysis and objective quantitative evaluation, e.g., Precision, Recall, and F-score. Further, results are also taken on real video to demonstrate its applicability in real-life applications.

Enhanced local multi-windows attention network for lightweight image super-resolution

Since the global self-attention mechanism can capture long-distance dependencies well, Transformer-based methods have achieved remarkable performance in many vision tasks, including single-image super-resolution (SISR). However, there are strong local self-similarities in images, if the global self-attention mechanism is still used for image processing, it may lead to excessive use of computing resources on parts of the image with weak correlation. Especially in the high-resolution large-size image, the global self-attention will lead to a large number of redundant calculations. To solve this problem, we propose the Enhanced Local Multi-windows Attention Network (ELMA), which contains two main designs. First, different from the traditional self-attention based on square window partition, we propose a Multi-windows Self-Attention (M-WSA) which uses a new window partitioning mechanism to obtain different types of local long-distance dependencies. Compared with original self-attention mechanisms commonly used in other SR networks, M-WSA reduces computational complexity and achieves superior performance through analysis and experiments. Secondly, we propose a Spatial Gated Network (SGN) as a feed-forward network, which can effectively overcome the problem of intermediate channel redundancy in traditional MLP, thereby improving the parameter utilization and computational efficiency of the network. Meanwhile, SGN introduces spatial information into the feed-forward network that traditional MLP cannot obtain. It can better understand and use the spatial structure information in the image, and enhances the network performance and generalization ability. Extensive experiments show that ELMA achieves competitive performance compared to state-of-the-art methods while maintaining fewer parameters and computational costs.

Brief History of the Uncontrolled Manifold Hypothesis and Its Role in Motor Control

BACKGROUND: The apparent problem of motor redundancy was replaced by the principle of abundance and turned into a theoretical framework and associated toolbox for exploration of performance-stabilizing synergies. AIM and METHOD: We review briefly the development of the main methods within the UCM framework and some of the main findings, both basic and clinical. The UCM framework is naturally merged with the theory of hierarchical movement control with spatial referent coordinates. RESULTS: The UCM framework has established itself as a productive framework for the analysis of movement control, in particular as related to stability of salient performance variables. It led to the discovery of novel phenomena such as trade-offs within hierarchical systems, anticipatory synergy adjustments, synergies within systems of different complexity from single muscles to the whole body. It has also led to promising results offering sensitive biomarkers to various neurological disorders. Recent experiments suggest the existence of three main levels of organization of performance-stabilizing synergies tentatively associated with cortical, subcortical, and spinal circuitry. CONCLUSION: Currently, this approach is in its adulthood. Further progress may be expected in focusing on spaces of neural control variables, developing the method for analysis across species, and expanding the range and depth of clinical studies.

Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system

Abstract Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

DCMR: Degradation compensation and multi-dimensional reconstruction based pre-processing for video coding

The rapid growth of video data poses a serious challenge to the limited bandwidth. Video coding pre-processing technology can remove coding noise without changing the architecture of the codec. Therefore, it can improve the coding efficiency while ensuring a high degree of compatibility with existing codec. However, the existing pre-processing methods have the problem of feature redundancy, and lack an effective mechanism to recover high-frequency details. In view of these problems, we propose a Degradation Compensation and Multi-dimensional Reconstruction (DCMR) pre-processing method for video coding to improve compression efficiency. Firstly, we develop a degradation compensation model, which aims at filtering the coding noise in the original video and relieving the frame quality degradation caused by transmission. Secondly, we construct a lightweight multi-dimensional feature reconstruction network, which combines residual learning and feature distillation. It aims to enhance and refine the key features related to coding from both spatial and channel dimensions while suppressing irrelevant features. In addition, we design a weighted guided image filter detail enhancement convolution module, which is specifically used to recover the high-frequency details lost in the denoising process. Finally, we introduce an adaptive discrete cosine transform loss to balance coding efficiency and quality. Experimental results demonstrate that compared with the original codec H.266/VVC, the proposed DCMR can achieve BD-rate (VMAF) and BD-rate (MOS) gains by 21.62% and 12.99% respectively on VVC, UVG, and MCL-JCV datasets.

Research on the Coupled Representation of Model and Data Integration Guided by Metadata Sets

Abstract. Aiming at the problems of islands, redundancy and sharing difficulties of spatial and temporal business models and data in geospatial and spatial business systems, this paper proposes an integrated and coupled expression method of models and data guided by metadata sets, based on the research on core metadata of information resources and component-based encapsulation. First of all, this method classifies the model and data features, and constructs the metadata description and expression framework dominated by feature meta-components; then, establishes metadata sets through the expansion of the metadata description and expression framework; then, constructs the correlation between the model and the data through the use of the established metadata sets by means of the component-based encapsulation method, so as to complete the integrated coupling of the model and the data; and finally, realises the integrated coupling expression of the model and the data in the digital pipeline business system. The results show that this method integrates the expression of digital pipeline element models and data, solves the problems of silos, redundancy and conflicts of heterogeneous models and data from multiple sources, ensures the integrity of the attributes of the models and data elements, and promotes the structured expression and application of the models and data.

Research on Autonomous Underwater Vehicle Path Optimization Using a Field Theory-Guided A* Algorithm

Autonomous Underwater Vehicles (AUVs) have become indispensable tools in the fields of ocean exploration, resource exploitation, and environmental monitoring. Path planning and obstacle avoidance are crucial to improve the operational capabilities of AUVs. However, most algorithms focus only on macro-global or micro-local path planning and rarely address both problems simultaneously. This study extends the classical A* algorithm by integrating field theory principles. The resulting Field Theory Augmented A* (FT-A*) algorithm combines the constraints in the AUV’s dynamics and the threats posed by obstacles to ensure a safe navigation distance. The paths planned by the FT-A* algorithm were subsequently re-optimised in conjunction with Dubins curves, taking into account path smoothness and redundant node problems. Simulation experiments confirm that the improved algorithm can effectively help AUVs navigate safely around obstacles, which greatly improves navigation safety and increases the arithmetic power and navigation efficiency. The proposed FT-A* algorithm provides a robust solution for underwater path planning and demonstrates great practical value for AUV operation in complex marine environments.

A multiscale approach for network intrusion detection based on variance–covariance subspace distance and EQL v2

As an important network defense approach, network intrusion detection is mainly used to identify anomaly traffic behavior. However, dominant network intrusion detection approaches are now struggling to identify the complex and variable means of attack, leading to high false alarm rate. Additionally, the feature redundancy and class imbalance problem in the intrusion detection dataset also constrain the performance of detection methods. This paper proposes a multiscale intrusion detection approach based on variance–covariance subspace distance and Equalization Loss v2 (EQL v2). Firstly, the variance–covariance subspace distance is used for feature selection on the preprocessed dataset to determine a set of representative feature subsets that can effectively approximate the original feature space. Secondly, the loss function, EQL v2, is adopted to balance the positive and negative gradients, addressing the class imbalance problem. Finally, a pyramid depthwise separable convolution model is proposed to capture the multiscale information of the traffic, and the convolutional layer in the depthwise convolution is replaced with self-supervised predictive convolutional attention block to compensate for the performance loss caused by the parameter reduction. Extensive experiments demonstrated that the proposed approach exhibits better performance on the three datasets of NSL-KDD, UNSW_NB15, and CIC-IDS-2017, with accuracy rates of 99.19%, 97.81%, and 99.83%, respectively, effectively improve the intrusion detection performance.

Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data

Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system’s interpretability, and enhancing the Decision-Making System (DMS) quality. FS also aids in addressing the problems of data redundancy and noise, which can negatively impact the system’s performance. FS is critical to developing practical and dependable CDSS in IoT-based healthcare sectors. This research proposes a two-phase FS model. Phase-I employs an ensemble of five Filter Methods (FM), followed by a Pearson Correlation Method (PCM). Phase-II uses the Binary Optimized Genetic Grey Wolf Optimization Algorithm (BOGGWOA) as a Wrapper Method (WM). This recommended model integrates the most valuable features of each filter. Then, it uses the Pearson Correlation Coefficient (PCC) to get rid of features that aren’t needed, a Support Vector Machine (SVM) to guess how accurate their classification will be, and BOGGWOA as the Wrapper Method (WM) to pick the most essential features with the best CA.

Leveraging Large-scale AI Models for Transforming China's Marine Industry: A TOE Framework Analysis

As an important driving force leading a new round of scientific and technological revolution and industrial change, artificial intelligence is becoming a hot spot. Promoting the deep integration of "artificial intelligence + ocean" will help build a world-class Marine artificial intelligence industry cluster with global competitiveness. How to effectively apply artificial intelligence to the Marine industry to solve the problems of resource redundancy and information island caused by industrial agglomeration is urgent for innovation and breakthrough. In this paper, TOE and modified T-TOE framework are used to analyze the above problems, and the following conclusions are drawn: 1) From the perspective of technical factors, intelligent information technology plays an important role in promoting the construction of large-scale models at the level of hardware and software; From the perspective of organizational factors, enterprise digital transformation and open sharing platform play an important role in supporting organization construction. From the perspective of environmental factors, policy support and market orientation play an important role in encouraging the construction of large models. 2) From the perspective of T-T, the breakthrough and application of technology need to balance the contradiction between development and conflict; From the perspective of T-O, intelligent supply-demand matching and transition resistance have both positive and negative effects; From the perspective of T-E, there is a problem of actively promoting multidimensional interactive innovation and achieving a balance. Relevant studies provide new ideas for the comprehensive development scenario of Marine industry and inject new impetus into the development of Marine industry.

Unsupervised machine learning classification for accelerating FE[formula omitted] multiscale fracture simulations

An approach is proposed to accelerate multiscale simulations of heterogeneous quasi-brittle materials exhibiting an anisotropic damage response. The present technique uses unsupervised machine learning classification based on k-means clustering to select integration points in the macro mesh within an FE2 strategy to track redundant micro nonlinear problems and to avoid unnecessary Representative Volume Element (RVE) calculations. More specifically, a classification vector including strains and internal damage variables is defined for each macro integration point. The macro internal damage variables are constructed using harmonic analysis of damage. At each step of the macro iterations, the integrations points are grouped into clusters and only one nonlinear problem is solved for each cluster. As a result, the computations are accelerated within an FE2 scheme by reducing the total number of RVE problems to be solved. The developed algorithm includes a macro regularization and an arc-length technique to capture macro snap-back due to the softening. Applications are proposed to simulate the response of different heterogeneous quasi-brittle materials with strong anisotropic responses. speed-up factors of the order of 12 to 15 can be achieved without the need to build a database, and without reduced-order modeling approximations at the micro level. Estimates of structural strength can be obtained with Speed-up factors between 45 and 85.

Multi-label feature selection based on minimizing feature redundancy of mutual information

Multi-label feature selection is an indispensable technology in the preprocessing of multi-label high-dimensional data. Approaches utilizing information theory and sparse models hold promise in this domain, demonstrating strong performance. Although there have been extensive literatures using l1 and l2,1-norms to identify label-specific features and common features in the feature space, they all ignore the redundant information interference problem when different features are learned simultaneously. Considering that features and labels in multi-label data are rarely linearly correlated, the MFS-MFR approach is presented to generate a representation of the nonlinear correlation between features and labels using the mutual information estimator. Following that, MFS-MFR detects specific and common features in the feature-label mutual information space using two coefficient matrices constrained by the l1 and l2,1-norms, respectively. In particular, we define a non-zero correlation constraint that effectively minimizes the redundant correlation between the two matrices. Moreover, a manifold regularization term is devised to preserve the local information of the mutual information space. To solve the optimization model with nonlinear binary regular term, we employ a novel solution approach called S-FISTA. Extensive experiments across 15 multi-label benchmark datasets, comparing against 11 top-performing multi-label feature selection methods, demonstrate the superior performance of MFS-MFR.

An Evaluation Model for Node Influence Based on Heuristic Spatiotemporal Features.

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks.

Event triggering fixed time secondary control of DC microgrid considering FDI attacks

SummaryAiming at the problem that distributed secondary control is vulnerable to FDI attacks in DC microgrid, which affects the stability of the system, and the problems of communication redundancy and energy loss in traditional distributed secondary control, an event triggered fixed time distributed secondary control scheme based on high‐order differentiator is proposed. The high‐order differentiator is used to estimate the FDI attack signal of the system. Then, using the estimated attack signal, a fixed time distributed secondary controller based on event triggering is designed to eliminate the impact of the attack signal, and realize the adjustment of bus voltage and accurate power distribution. The strict stability and the absence of Zeno behavior are proved. The simulation results show that the proposed control strategy can significantly reduce the impact of attacks on the system, and the event triggered fixed time control reduces the energy consumption of the system and the update numbers of the controller, making the system have better dynamic performance.

Research on Path Planning of a Mining Inspection Robot in an Unstructured Environment Based on an Improved Rapidly Exploring Random Tree Algorithm

To ensure the safe production of mines, the intelligent trend of underground mining operations is gradually advancing. However, the operational environment of subterranean mining is intricate, making the conventional path-planning algorithm used by mining inspection robots frequently inadequate for real requirements. To safeguard the mining inspection robot, targeting the problem of low search efficiency and path redundancy in the path planning of the existing rapidly exploring random tree (RRT) algorithm in the narrow and complex unstructured environment, a path-planning algorithm combining improved RRT and a probabilistic road map (PRM) is proposed. Initially, the target area is efficiently searched according to the fan-shaped goal orientation strategy and the adaptive step size expansion strategy. Subsequently, the PRM algorithm and the improved RRT algorithm are combined to reduce the redundant points of the planning path. Ultimately, considering the kinematics of the vehicle, the path is optimized by the third-order Bessel curve. The experimental simulation results show that the proposed path-planning algorithm has a higher success rate, smoother path, and shorter path length than other algorithms in complex underground mining environments, which proves the effectiveness of the proposed algorithm.

A Transformer-based approach to highly granular source code authorship attribution

Traditional source code authorship identification methods often rely on features such as textual similarity, programming style or metadata, however, these methods often struggle to extract the precise source code authoring style when dealing with large-scale code bases or complex programming patterns, resulting in poor performance. Therefore, this paper proposes a Transformer-based high fine-grained source code author attribution method.Aiming at the problem of roughness of existing literature on word segmentation, this paper proposes a high fine-grained source code segmentation method to extract higher fine-grained features. Aiming at the problem of feature dimension redundancy, this paper adopts the Transformer network to locate sensitive features that can characterise the author's style.To verify the effectiveness of the model, it was tested on GCJ-C++ and GCJ-Java datasets. The experimental results show that the proposed method model achieves higher recognition accuracy in the source code authorship attribution problem compared to the traditional methods.

A Transformer-based approach to highly granular source code authorship attribution

Traditional source code authorship identification methods often rely on features such as textual similarity, programming style or metadata, however, these methods often struggle to extract the precise source code authoring style when dealing with large-scale code bases or complex programming patterns, resulting in poor performance. Therefore, this paper proposes a Transformer-based high fine-grained source code author attribution method.Aiming at the problem of roughness of existing literature on word segmentation, this paper proposes a high fine-grained source code segmentation method to extract higher fine-grained features. Aiming at the problem of feature dimension redundancy, this paper adopts the Transformer network to locate sensitive features that can characterise the author's style.To verify the effectiveness of the model, it was tested on GCJ-C++ and GCJ-Java datasets. The experimental results show that the proposed method model achieves higher recognition accuracy in the source code authorship attribution problem compared to the traditional methods.

Optimizing Storage Efficiency Through Pipelining and Parallelization for Enhanced Data Deduplication

Nowadays, huge devices are connected to the internet to deal with many cloud environments for storing data efficiently. Also, it is used to store big data determined in business activities. However, the data storage in the cloud avoids duplicate copies of files and reduces complexity issues. However, this platform is utilized by many users for storing the file in cloud and this creates a data redundancy problem. Also, fingerprint indexing is a major issue in data storage in the cloud. So in order to overcome this issue a novel Enhanced Arithmetic Optimization Algorithm with Mixed-Kernel-based Extreme Learning Machine-based Random Forest (EAOA-MKELM-RF) method is proposed that overcome the deduplication issues. In spite, the scalability issues are solved, and the limitations determined in fingerprint indexing. Further, the pipelining and parallelizing hash function values are utilized to address the scalability issue in the deduplication process. The data storage container organizes the programs and files into data blocks, metadata files and nonduplicate blocks. However, the proposed methods determine the similarity issues and accelerate the fingerprint queries for better scalability. The datasets employed to evaluate the proposed approach are CorrAL-100 dataset, Madelon dataset, XOR-100 dataset, Infocom’05 dataset and Linux kernel dataset. The experimentation results demonstrated that the proposed method attained a superior accuracy by 96.8% in terms of restoration accuracy, robustness and delivery ratio. Additionally, the proposed approach reduces buffer time, improving overall system performance. This validation optimized the storage resources, diminished storage costs and improved overall data management.