Feature Segmentation Research Articles

UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation

Word segmentation is a critical task in natural language processing for southeast Asian Abugida languages, including Thai, Burmese, and Khmer. Existing approaches demonstrate that models using fixed-length windowed context inputs can achieve high segmentation accuracy; however, they often rely on low-level character features or language-specific preprocessing. Character-based methods can limit feature learning, while language-specific features add complexity due to specialized preprocessing requirements. This paper introduces UnifiedCut, which is a neural model that leverages multiple n-grams within a windowed multi-head attention mechanism. This design captures segmentation features from local contexts and multi-perspective n-gram inputs, enhancing generalization and recall, particularly for out-of-vocabulary words. Compared to CNN- and RNN-based approaches, UnifiedCut’s multi-head attention enables finer-grained feature extraction and greater parallelism, resulting in a faster, more scalable solution. Comprehensive experiments on public datasets for Thai, Burmese, and Khmer show that UnifiedCutachieves state-of-the-art performance in word segmentation.

Global Context Volume construction and semantics-guided disparity refinement for stereo matching

The accuracy of stereo matching has been greatly improved with the advent of convolutional neural networks. However, existing methods still perform poorly in regions of low-texture and reflections due to insufficient matching information. In this paper, we propose a novel semantic stereo matching network named GSSNet, which combines global context information with high-level semantic clues to further enrich the matching features. Specifically, we employ Swin Transformer as a joint feature extractor to capture multi-scale global features for both semantic segmentation and disparity estimation. By fusing low-resolution multi-scale feature maps progressively, we construct a Global Context Volume (GCV) representation covering long-range receptive field for initial disparity estimation. Initial disparities are further refined with a high-level embedding learned from the semantic segmentation branch of the network through residual learning. During training, due to the lack of joint annotations, we also propose a simple and effective pseudo-labeling strategy. Comprehensive experimental results demonstrated on various datasets manifest the effectiveness of the proposed GSSNet. Among all published methods as of 9, February 2024, our approach ranks 1st on KITTI 2012 leaderboard and 3rd on KITTI 2015 leaderboard, and produces competitive results on Scene Flow and Cityscapes datasets. Code will be available at https://github.com/Twil-7/GSSNet.

Broad Distributed Game Learning for intelligent classification in rolling bearing fault diagnosis

As a new Single Layer Feedforward Network (SLFN) architecture, Broad Learning System (BLS) has been widely used in the field of fault diagnosis because of its fast-training speed and high generalization capability. However, when features in different classes of signals are similar or weak, BLS generates a large number of redundant features that may be difficult to classify accurately. In view of this, a new Broad Distributed Game Learning (BDGL) method is proposed in this paper, which maps data into the game space by constructing two non-parallel game hyperplanes to achieve game and segmentation of different similar features, thereby making the data linearly differentiable in the game space. Meanwhile, a linear distribution constraint term is designed to reduce noise fitting and weak feature learning in training data learning by limiting the complexity of model parameters, thereby making the solution of the objective function simpler and faster. By comparing the Precision, Recall, F-score, Kappa and Accuracy of BDGL and the comparison methods on the two types of rolling bearing experimental data, the results show that BDGL has a high classification accuracy. In addition, the experimental results on small and noisy samples once again demonstrate the effectiveness of BDGL, which provides an efficient solution for rolling bearing fault diagnosis.

Machine Learning for Automated Oil Palm Fruit Grading: The Role of Fuzzy C-Means Segmentation and Textural Features

Oil palm fruit, a high-demand and economically valuable crop, faces grading challenges in Malaysia due to labour-intensive methods, resulting in unharvested ripe fruits and yield losses. Extensive research has been conducted on outdoor palm fruit classifiers, but most high-accuracy research in this field relies heavily on large image datasets. This creates a trade-off between employing more sophisticated deep-learning approaches that require extensive data and utilising traditional techniques that have lower data requirements. Therefore, the aim is to propose an outdoor image-based fresh fruit bunch (FFB) classification system, emphasising pre-processing technique and feature extraction techniques suitable for limited datasets. To achieve this, the FFB was localised within the original images using the YOLOv4-Tiny algorithm. Subsequently, a salient segmentation method utilising superpixel-based Fast Fuzzy C-means (FFCM) clustering is employed to remove the background from the images. Next, colour moment and opposite colour local binary pattern (OCLBP) features are extracted from the segmented images to capture important information for classification. Finally, the extracted features are fed into a Multilayer Perceptron (MLP) classifier, which enables the system to predict five classes: damaged, empty, unripe, ripe, and overripe. The developed system demonstrated a commendable performance in accurately classifying fruit bunches, achieving an accuracy of 93.68%. In conclusion, the proposed system effectively addresses the issue of unripe fruit harvesting and contributes to the advancement of state-of-the-art methods in classifying outdoor FFB images.

A semi-automatic cardiovascular annotation and quantification toolbox utilizing prior knowledge-guided feature learning

Annotation and quantification of cardiovascular in intravascular ultrasound (IVUS) images are of great significance in assisting the clinical diagnosis and treatment of coronary heart disease. However, it is time-consuming and expensive for cardiologists to manually annotate cardiovascular and calculate parameters in IVUS images using different software during the diagnosis. In this paper, we propose an IVUS analysis toolbox (SaCAQ) for semi-automatic cardiovascular annotation and quantification via prior knowledge-guided feature learning. The SaCAQ consists of two main modules: (1) a semi-automatic cardiovascular annotation module and (2) a cardiovascular quantification module. The semi-automatic cardiovascular annotation module can accurately segment the vascular wall by a IVUS images segmentation model (ISM-IVUS). The ISM-IVUS first extracts vascular morphological features and pixel intensity change features for the complex image segmentation by the expert prior knowledge feature extraction mechanism. Secondly, vascular wall is accurately segmented by the multi-scale feature extraction of attention U-Net. Moreover, the segmentation result checking mechanism is applied to verify the automatic segmentation result, which enhances the reliability of SaCAQ. Plaque regions of interest to doctors are detected by the rule-based plaque detection method, which can assist in the quantification task. Finally, image measurement methods and medically recognized calculation formulas are used in the cardiovascular quantification module to obtain clinical parameters, which significantly improves the confidence of the results. The SaCAQ is verified on our own dataset containing 5242 IVUS slice images and 383 sets of parameters. The experiments demonstrate that the SaCAQ achieves high accuracy of vascular wall segmentation: mIoU of 86.16%, Dice coefficient of 93.43%, and Hausdorff distance of 0.2171 mm. Moreover, ISM-IVUS has good segmentation speed and robustness. The calculated parameters show good correlation and p-values. All of these results indicate that the proposed SaCAQ provides an efficient, accurate, and reliable toolbox for clinical diagnosis of cardiovascular disease. The SaCAQ and user manual can be publicly available from https://github.com/zHwiz/SaCAQ.

DIEN: A dual-factor iterative enhancement network with the global Re-calibration feature for coronary artery segmentation

ObjectiveCoronary artery segmentation is a prerequisite for assessing the severity of coronary artery disease (CAD), which has a high prevalence and mortality rate. However, segmentation of the coronary arteries is challenging due to their complex anatomy diverse morphology, and small target size. MethodsThis study proposes a dual-factor iterative enhancement network (DIEN) for automatic coronary artery segmentation in coronary computed tomography angiographic (CCTA) images. The DIEN includes a dual-factor layer-by-layer iterative convolution (DLIC) module and a global recalibration feature selection (GRFS) module. The DLIC in the encoding part aims to capture diverse semantic information from coronary arteries with different morphologies based on extracting multiple features under different receptive fields. The GRFS bridges the semantic gap between the encoding and decoding parts by exploiting the global information of the slices to improve the feature representation of the coronary artery. ResultsExperiments show that the DIEN outperforms seven state-of-the-art algorithms on a variety of metrics on two datasets. On a self-collected dataset, the DIEN achieved a 1.01 %, 0.69 %, 1.91 %, and 0.63 % improvement over U-Net in IoU, dice, recall, and precision, respectively. In comparison to U-Net, the IoU, dice, recall, and precision obtained by the DIEN on the public dataset were improved by 1.03 %, 0.73 %, 0.12 %, and 0.23 %, respectively. ConclusionCompared with state-of-the-art approaches, the proposed model achieves good performance in segmenting coronary arteries both on our dataset and a publicly available dataset. Significance: Our method can accurately segment coronary arteries, and can contribute to improved diagnosis and assessment of CAD.

Precision Denoising in Medical Imaging via Generative Adversarial Network-Aided Low-Noise Discriminator Technique

Medical imaging is significant for accurate diagnosis, and here, noise often degrades image quality, thus making it challenging to identify important information. Denoising is a component of traditional image pre-processing that helps prevent incorrect disease diagnosis. Mitigating the noise becomes difficult if there are differences in the low-level segment features. Therefore, a Generative Adversarial Network (GAN)-aided Low-Noise Discriminator (LND) is introduced to improve the denoising effectiveness in medical images with a balanced image resolution with noise mitigation. The LND function is a key that distinguishes between high- and low-noise areas based on segmented features, which are also achieved by tuning the peak signal-to-noise ratio (PSNR). Considering the training sequences, the LND-identified intervals lessen the sequences to improve the changes in pixel reconstruction. The generator function in this method is responsible for increasing the PSNR improvements over the different pixels cumulatively. The proposed method successfully improves the pixel reconstruction by 11.05% and PSNR by 9.75%, with 9.75% less reconstruction time and 13.11% less extraction error for the higher pixel distribution ratios than other contemporary methods.

Edge-guided and hierarchical aggregation network for robust medical image segmentation

Medical image segmentation with the convolutional neural networks (CNNs), significantly enhances clinical analysis and disease diagnosis. However, medical images inherently exhibit large intra-class variability, minimal inter-class differences, and substantial noise levels. Extracting robust contextual information and aggregating discriminative features for fine-grained segmentation remains a formidable task. Additionally, existing methods often struggle with producing accurate mask edges, leading to blurred boundaries and reduced segmentation precision. This paper introduces a novel Edge-guided and Hierarchical Aggregation Network (EHANet) which excels at capturing rich contextual information and preserving fine spatial details, addressing the critical issues of inaccurate mask edges and detail loss prevalent in current segmentation models. The Inter-layer Edge-aware Module (IEM) enhances edge prediction accuracy by fusing early encoder layers, ensuring precise edge delineation. The Efficient Fusion Attention Module (EFA) adaptively emphasizes critical spatial and channel features while filtering out redundancies, enhancing the model’s perception and representation capabilities. The Adaptive Hierarchical Feature Aggregation Module (AHFA) module optimizes feature fusion within the decoder, maintaining essential information and improving reconstruction fidelity through hierarchical processing. Quantitative and qualitative experiments on four public datasets demonstrate the effectiveness of EHANet in achieving superior mIoU, mDice, and edge accuracy against eight other state-of-the-art segmentation methods, highlighting its robustness and precision in diverse clinical scenarios.

Is standard employment contract good for all? The gendered institutional logic of employment in South Korea

In coordinated market economies, nonstandard forms of employment are more common among women than among men. However, the existing scholarly literature devoted little attention to the examination of gendered employment patterns by skill level in newly emerged coordinated economies. This article employs a gender-based approach within the Varieties of Capitalism framework to analyse the effects of gender segmentation across diverse occupational skill levels in South Korea. The findings, derived from workers’ surveys and workplace interviews, indicate that the features of gender segmentation in employment outcomes vary according to skill level. Although the feminist critique on dualisation scenarios is supported, it is evident that the strict employment protection afforded by standard employment contracts does not benefit women uniformly. This discrepancy arises from employers' gendered logic, which is embedded in the country-specific gendered institutional structure of the production regime. The South Korean experience problematises the skill-based approach to dualisation scenarios, underscoring notable divergences from the evidence observed in mature coordinated market economies.

An in vivo three-dimensional, kinematics study of the cervical vertebrae under physiological loads in patients with cervical spondylosis

Background ContextStudies of in vivo kinematic differences between healthy individuals and those with cervical spondylosis (CS) have been reported, but only movements under non-physiological loads have been investigated. Differences in the in vivo, cervical kinematics between healthy individuals and those with CS are unknown. PurposeTo investigate the in vivo, cervical kinematics of patients with CS under physiological loads. Study DesignThis was a retrospective, case-controlled study that used three-dimensional (3D) to 3D registration techniques combined with conical beam computed tomography (CBCT) to investigate the cervical kinematics of patients with CS. Patient SampleTwenty individuals diagnosed with CS were selected for study participation and matched with 20 participants who did not have CS and were in good health. Outcome MeasuresPfirrmann grading,intervertebral range of motion(ROM), kinematics and cross-sectional area of posterior neck muscles(CAPNM). MethodsAll study participants underwent seven CBCT scans of their cervical vertebrae. The 3D segmental motion features of the vertebra in vivo were calculated using 3D-to-3D volume registration to overlay images of the vertebra at each functional position. The 3D range of motion (ROM) of each cervical segment was expressed with six degrees of freedom using Euler angles and translated onto a coordinate system. A kinematic subgroup analysis was conducted based on the severity of symptoms within the CS group, and differences in muscle volume between the CS and control groups were also evaluated. Project supported by the National Natural Science Foundation of China (Grant No. 81960408,82260445), Training plan for academic and technical leaders of major disciplines in Jiangxi Province(Grant No.20204BCJL22047), Clinical Cultivation Project of The First Affiliated Hospital of Nanchang University (Grant No.YFYLCYJPY 20220203).The authors declare no conflict of interest in preparing this article. ResultsThe CS group exhibited noticeable reductions in the primary rotational ROMs of left-right rotation at C4-C5, C5-C6, C6-C7, C4-C7, and C1-C7 compared to the controls. During left-right bending, there were no significant differences in the primary ROMs, coupled translations, or rotations between the two groups. However, compared to controls, the CS group had significantly lower primary ROMs for C4 C7, C1-C7 and C5-C6 during flexion-extension. During left-right rotation, the primary rotations and coupled lateral bending at C6-C7 were significantly increased in the mild CS group compared to the moderate CS group. In the mild CS group, the primary ROM of the C4-C5 and C5-C6 during flexion-extension was significantly greater than that of the moderate CS group. ConclusionsFor the first time, the in vivo 3D kinematics of the cervical spine during head movement under physiological load in CS individuals have been adequately described and compared with healthy cervical vertebrae, which can be used as a reference point for future studies. The application of CBCT helps to obtain accurate and precise movement information of CS patients and effectively enhance the evaluation results obtained from imaging information.

Exploring EFL students’ pronunciation learning supported by corpus-based language pedagogy

Recent advances in corpus-based language pedagogy (CBLP) have proven effective for teaching text-based language skills such as reading and writing. However, its application to spoken language, particularly for improving pronunciation, has received little attention. In this study, we investigated the effectiveness of a CBLP-supported pronunciation course focusing on segmental and suprasegmental features to enhance students’ pronunciation accuracy and fluency. Seventy-four EFL students participated in a 14-week quasi-experimental study using pre- and post-tests, surveys, and interviews. Results indicated significant improvements in both the segmental and suprasegmental aspects of students’ pronunciation, with large effect sizes in reading accuracy, phonetic accuracy, and pronunciation fluency. Interview responses revealed that students perceived this CBLP-supported approach as beneficial, reporting positive impacts on their access to authentic spoken language, awareness of pronunciation features, and listening and speaking skills. Students also appreciated the opportunities for autonomous learning and expressed a desire for continued access to spoken corpora, along with further teacher guidance and exposure to written corpora for enhancing other skills.

Transparent Depth Completion Using Segmentation Features

Estimating the depth of transparent objects is one of the well-known challenges of RGB-D cameras due to the reflection and refraction effects. Previously, researchers propose to correct the depth of transparent objects by using their estimated segmentation masks because it is possible to recover the internal depth of an object just from its boundary, as illustrated by those depth-from-silhouette methods. However, these algorithms only use segmentation masks. They ignore the internal structure information from the mask segmentation features, which we argue are more useful for transparent depth estimation. In this work, we demonstrate the effectiveness of segmentation features for transparent object depth estimation. We show that it is even possible to recover the depth map just from segmentation features, without any RGB or depth map as input. Based on this observation, we propose DualTransNet which uses segmentation features for transparent depth completion. In our DualTransNet, we feed segmentation features from an extra module to the main network for better depth completion quality. Extensive experiments have shown the superiority of segmentation features as well as the state-of-the-art performance of our network.

Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and Downsampling

To address the challenges of limited detection precision and insufficient segmentation of small to medium-sized objects in dynamic and complex scenarios, such as the dense intermingling of pedestrians, vehicles, and various obstacles in urban environments, we propose an enhanced methodology. Firstly, we integrated a point cloud processing module utilizing the DBSCAN clustering algorithm to effectively segment and extract critical features from the point cloud data. Secondly, we introduced a fusion attention mechanism that significantly improves the network’s capability to capture both global and local features, thereby enhancing object detection performance in complex environments. Finally, we incorporated a CSPNet downsampling module, which substantially boosts the network’s overall performance and processing speed while reducing computational costs through advanced feature map segmentation and fusion techniques. The proposed method was evaluated using the KITTI dataset. Under moderate difficulty, the BEV mAP for detecting cars, pedestrians, and cyclists achieved 87.74%, 55.07%, and 67.78%, reflecting improvements of 1.64%, 5.84%, and 5.53% over PointPillars. For 3D mAP, the detection accuracy for cars, pedestrians, and cyclists reached 77.90%, 49.22%, and 62.10%, with improvements of 2.91%, 5.69%, and 3.03% compared to PointPillars.

EFL students’ beliefs and practices regarding pronunciation self-regulated learning

Abstract The past three decades have witnessed growing interest in pronunciation research focused on a range of topics such as the effects of explicit phonetic instruction on second/foreign language (L2) learners’ pronunciation uptake, teachers’ pronunciation teaching practices and beliefs, pronunciation representation in prescribed textbooks, and L2 students’ perceptions of their teachers’ pronunciation instruction. However, L2 learners’ beliefs and practices regarding pronunciation self-regulated learning (PSRL) is under-explored. The present study examines how Vietnamese tertiary EFL learners practice PSRL and what beliefs they hold about it. Data were collected from a questionnaire completed by 215 EFL students at a Vietnamese public university and semi-structured interviews with ten of the students. Results showed that the students demonstrated a strong commitment to self-practicing English pronunciation and that their PSRL practices centered on segmental features through listen-and-repeat activities from YouTube pronunciation lesson videos and textbook audio-recordings. The study further showed that the students believed PSRL had a positive impact on their own pronunciation but called for phonetic instruction to guide their self-practice, followed by teachers giving feedback on their pronunciation. Implications for L2 pronunciation instruction and PSRL are discussed.

Dual-feature enhanced hybrid convolutional network for imbalanced fault diagnosis of rolling bearings

Abstract Deep learning has been extensively applied in the rolling bearing fault diagnosis domain due to its superior data analysis and feature extraction capabilities. However, in practical applications, the normal operating state occupies most of the service life of equipment, and the occurrence probability of each kind of fault is different, leading to imbalanced data that significantly degrades the performance of the neural network. In order to solve this problem, a dual-feature enhanced hybrid convolutional network (DEHCNet) is proposed. Firstly, an impulse segment enhancement module (ISEM) is constructed to enhance impulse segment features in the raw data, helping the network to learn fault features more accurately. Then, a hybrid convolutional module (HCM) is designed to fully mine discriminant fault features of minority classes from imbalanced data. In addition, a feature-enhanced combinational pooling module (FCPM) is devised to guide the network to focus more on the critical features and maximize the retention of key features in dimensionality reduction operations, thereby reducing the influence of data imbalance on the classifier. Finally, two distinct datasets are used to verify DEHCNet. Experimental results show that this network has better diagnostic accuracy and robustness under conditions of data imbalance.

NN2ViT: Neural Networks and Vision Transformers based approach for Visual Anomaly Detection in Industrial Images

Ensuring product quality through automated anomaly detection is crucial in manufacturing. Traditional methods often struggle to capture both local and global features effectively, relying heavily on predefined templates that limit their adaptability and accuracy. To address these challenges, this study propose NN2ViT, a novel approach that integrates a Single Shot Detector (SSD) for local feature detection and the Segment Anything Model (SAM) for global feature segmentation. This integration allows for a comprehensive analysis of anomalies in industrial images. Our method improves anomaly segmentation performance by fine-tuning SAM for precise segmentation in industrial product images. Experiments on the MVTec benchmark dataset demonstrate that NN2ViT outperforms traditional models and achieved the highest 95.54% and 96.23% Image AUROC and AP scores, respectively thus enhancing interpretability and adaptability to various anomaly patterns. This research presents a significant advancement in manufacturing quality control, contributing to improved product quality and operational efficiency.

Multimodal feature integration network for lithology identification from point cloud data

Accurate lithology identification from outcrop surfaces is crucial for interpreting geological 3D data. However, challenges arise due to factors such as severe weathering and vegetation coverage, which hinder achieving ideal identification results with both accuracy and efficiency. The integration of 3D point cloud technology and deep learning methodologies presents a promising solution to address these challenges. In this study, we propose a novel multimodal feature integration network designed to distinguish various rock types from point clouds. Our network incorporates a multimodal feature integration block equipped with multiple attention mechanisms to extract representative deep features, along with a hierarchical feature separation block to leverage these features for precise segmentation of points corresponding to different lithologies. Furthermore, we introduce a specialized loss function tailored for rock type identification to enhance network training. Through experiments involving point cloud sampling strategies and loss function evaluation, we identify the optimal network configuration. Comparative analyses against baseline methods demonstrate the superiority of our proposed network across diverse study areas reconstructed from UAV images and laser scanner data, exhibiting improved visual appearance and metric values (Accuracy = 0.978, mean Accuracy = 0.895, mean IoU = 0.857). These findings underscore the efficacy of the multimodal feature integration network as a promising approach for lithology identification tasks in various digital outcrop models derived from heterogeneous data sources.

Building Change Detection Network Based on Multilevel Geometric Representation Optimization Using Frame Fields

To address the challenges of accurately segmenting irregular building boundaries in complex urban environments faced by existing remote sensing change detection methods, this paper proposes a building change detection network based on multilevel geometric representation optimization using frame fields called BuildingCDNet. The proposed method employs a multi-scale feature aggregation encoder–decoder architecture, leveraging contextual information to capture the characteristics of buildings of varying sizes in the imagery. Cross-attention mechanisms are incorporated to enhance the feature correlations between the change pairs. Additionally, the frame field is introduced into the network to model the complex geometric structure of the building target. By learning the local orientation information of the building structure, the frame field can effectively capture the geometric features of complex building features. During the training process, a multi-task learning strategy is used to align the predicted frame field with the real building outline, while learning the overall segmentation, edge outline, and corner point features of the building. This improves the accuracy of the building polygon representation. Furthermore, a discriminative loss function is constructed through multi-task learning to optimize the polygonal structured information of the building targets. The proposed method achieves state-of-the-art results on two commonly used datasets.

Accurate Feature Extraction from Historical Geologic Maps Using Open-Set Segmentation and Detection

This study presents a novel AI method for extracting polygon and point features from historical geologic maps, representing a pivotal step for assessing the mineral resources needed for energy transition. Our innovative method involves using map units in the legends as prompts for one-shot segmentation and detection in geological feature extraction. The model, integrated with a human-in-the-loop system, enables geologists to refine results efficiently, combining the power of AI with expert oversight. Tested on geologic maps annotated by USGS and DARPA for the AI4CMA DARPA Challenge, our approach achieved a median F1 score of 0.91 for polygon feature segmentation and 0.73 for point feature detection when such features had abundant annotated data, outperforming current benchmarks. By efficiently and accurately digitizing historical geologic map, our method promises to provide crucial insights for responsible policymaking and effective resource management in the global energy transition.

DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition.

Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a DNABert-based method using a multi-scale convolutional neural network, BiLSTM, for enhancer identification. We first designed the DeepDualEnhancer method based only on the DNA sequence input. It mainly consists of a multi-scale Convolutional Neural Network, and BiLSTM to extract features by DNABert and embedding, respectively. Meanwhile, we collected new datasets from the enhancer-promoter interaction field and designed the method DeepDualEnhancer-genomic for inputting DNA sequences and genomic signals, which consists of the transformer sequence attention. Extensive comparisons of our method with 20 other excellent methods through 5-fold cross validation, ablation experiments, and an independent test demonstrated that DeepDualEnhancer achieves the best performance. It is also found that the inclusion of genomic signals helps the enhancer recognition task to be performed better.