Task Characteristics Research Articles

Children's reading outcomes in digital and print mediums: A systematic review

BackgroundGiven the growing reliance on digital devices, an increasing number of studies have examined the effects of text medium on reading outcomes in development; however, the results have been mixed. The goal of this systematic review is to look at how print and digital formats affect reading comprehension, engagement and other reading outcomes (e.g. vocabulary, reading speed) in children and adolescents aged 1–17 years old while also considering the influence of several participant, task and study characteristics.MethodsA comprehensive search strategy involving seven electronic databases yielded 88 eligible articles comparing digital and print formats on reading outcomes published between 2000 and 2023 (3 reviewer inter‐rater reliability: k = .54–.78). Three major characteristics were coded: participant‐level (grade/age, diverse populations, testing language); task‐level (text‐genre, shared reading, digital comparability); study‐level (publication recency, study quality) characteristics. Contingency tables were created for all studies, then for each reading outcome and for participant, task, and study characteristics separately to classify the percentage of studies that demonstrated outcomes favouring print, digital, no difference or reliance on specific reading measures or other factors.ResultsExcept in the case of engagement as an outcome, the most common finding was no difference between digital and print. When participant, task and study characteristics were examined separately for the various reading outcomes, the results varied. More studies examining reading comprehension (particularly of informational text and in older children) found ‘print is better’, whereas ‘digital is better’ was more common in studies examining engagement, other outcomes such as vocabulary and diverse learners.ConclusionsThis review highlights the importance of examining multiple interacting factors when studying the impact of print versus digital mediums on reading outcomes in children and adolescents.

LANGUAGE LEARNING THROUGH TASK-BASED SYNCHRONOUS COMPUTER-MEDIATED COMMUNICATION

A limited number of studies have been conducted on the influence of task features on language production in computer-mediated communication (CMC) environments despite the role of tasks on the language production of L2 students who are mostly non-native speakers (NNSs). Among the prominent hypotheses on the relationship between tasks and language production are the Cognition Hypothesis and the Trade-Off Hypothesis. The current study examined the effect of task structure on student language production in terms of syntactic complexity and fluency in CMC environments. A one-shot design study involved 46 NNS undergraduates in Malaysia and Japan. The respondents were divided into two groups: (1) respondents who performed the task with task structure (+TS) and (2) respondents who performed the task without task structure (-TS). The data were then analysed using a t-test. The present study supported the hypothesis positing that the utilisation of +TS contributes to an increase in syntactic complexity among NNSs, as well as the hypothesis proposing that the use of +TS results in improved fluency. The results also suggest examining additional variables related to task difficulty when designing tasks for CMC environments to assess their effects on fluency and syntactic complexity.

ANALYSIS OF SATISFACTION AND USEFULNESS OF LEARNING MANAGEMENT SYSTEM (LMS) USING EUCS AND TTF METHODS

Student satisfaction with using the Learning Management System (LMS) is one factor determining the achievement of bold learning objectives in tertiary institutions. This study aims to test the satisfaction and usefulness of LMS for students in learning to dare to use several variables in End User Computing Satisfaction (EUCS) and Task Technology Fit (TTF). The relationship between variables in EUCS and TTF was tested using the Structural Equation Model (SEM) based on 184 respondent data obtained from students using Moodle LMS at a university in Jakarta. The study's results revealed that the variable task characteristics and technological characteristics significantly affected TTF and EUCS tasks, and TTF positively affected EUCS. The results of the study provide recommendations that the resolution of LMS technology and the ease of use of LMS are the dominant factors influencing student satisfaction in using LMS.

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

AbstractThe detection of sound events has become increasingly important due to the development of signal processing methods, social media, and the need for automatic labeling methods in applications such as smart cities, navigation, and security systems. For example, in such applications, it is often important to detect sound events at different levels, such as the presence or absence of an event in the segment, or to specify the beginning and end of the sound event and its duration. This study proposes a method to reduce the feature dimensions of a Sound Event Detection (SED) system while maintaining the system’s efficiency. The proposed method, using Empirical Mode Decomposition (EMD), Intrinsic Mode Functions (IMFs), and extraction of locally regulated features from different IMFs of the signal, shows a promising performance relative to the conventional features of SED systems. In addition, the feature dimensions of the proposed method are much smaller than those of conventional methods. To prove the effectiveness of the proposed features in SED tasks, two segment-based approaches for event detection and sound activity detection were implemented using the suggested features, and their effectiveness was confirmed. Simulation results on the URBAN SED dataset showed that the proposed approach reduces the number of input features by more than 99% compared with state-of-the-art methods while maintaining accuracy. According to the obtained results, the proposed method is quite promising.

Diverse and tailored image generation for zero-shot multi-label classification

Recently, zero-shot multi-label classification has garnered considerable attention owing to its capacity to predict unseen labels without human annotations. Nevertheless, prevailing approaches often use seen classes as imperfect proxies for unseen classes, resulting in suboptimal performance. Drawing inspiration from the success of text-to-image generation models in producing realistic images, we propose an innovative solution: generating synthetic data to construct a training set explicitly tailored for proxyless training of unseen labels. Our approach introduces a novel image generation framework that produces multi-label synthetic images of unseen classes for classifier training. To enhance the diversity of the generated images, we leverage a pretrained large language model to generate diverse prompts. Employing a pretrained multimodal contrastive language-image pretraining (CLIP) model as a discriminator, we assessed whether the generated images accurately represented the target classes. This enables automatic filtering of inaccurately generated images and preserves classifier accuracy. To refine the text prompts for more precise and effective multi-label object generation, we introduced a CLIP score-based discriminative loss to fine-tune the text encoder in the diffusion model. In addition, to enhance the visual features of the target task while maintaining the generalization of the original features and mitigating catastrophic forgetting resulting from fine-tuning the entire visual encoder, we propose a feature fusion module inspired by transformer attention mechanisms. This module aids in capturing the global dependencies between multiple objects more effectively. Extensive experimental results validated the effectiveness of our approach, demonstrating significant improvements over state-of-the-art methods. The code is available at https://github.com/TAKELAMAG/Diff-ZS-MLC.

Exploring the construct of interactional competence in different types of oral communication assessment

Abstract Research on interaction in speaking assessment suggests that both verbal and nonverbal interaction are integral parts of the construct of interactional competence (Galaczi & Taylor, 2018; Plough et al., 2018; Young, 2011). However, little has been done to investigate which features significantly contribute to interactional competence scores. This study, therefore, examined which interaction features that raters noticed in individual scripted interview and paired discussion tasks to gain an insight into the interactional competence construct, providing validity evidence for an inclusion of interactional competence in speaking assessment. Sixty-eight student performances were rated based on interaction rating scales. Exploratory factor analysis revealed four factors: nonverbal communication, topic management, interactional management, and interactive listening. Logistic regressions showed that while raters attended to more topic management features in the individual scripted interview task, they noticed more interactional management features in the paired discussion task. Simple regressions showed that nonverbal communication and topic management features predicted interactional competence scores in the individual scripted interview task, whereas nonverbal communication, topic management, interactional management, and interactive listening features were predictors of scores in the paired discussion task. The findings suggest that both nonverbal and verbal interaction features are important in the interactional competence construct with the paired task providing test-takers with more opportunities to demonstrate their interactional ability.

MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis.

Accurately predicting the driver genes of cancer is of great significance for carcinogenesis progress research and cancer treatment. In recent years, more and more deep-learning-based methods have been used for predicting cancer driver genes. However, deep-learning algorithms often have black box properties and cannot interpret the output results. Here, we propose a novel cancer driver gene mining method based on heterogeneous network meta-paths (MCDHGN), which uses meta-path aggregation to enhance the interpretability of predictions. MCDHGN constructs a heterogeneous network by using several types of multi-omics data that are biologically linked to genes. And the differential probabilities of SNV, DNA methylation, and gene expression data between cancerous tissues and normal tissues are extracted as initial features of genes. Nine meta-paths are manually selected, and the representation vectors obtained by aggregating information within and across meta-path nodes are used as new features for subsequent classification and prediction tasks. By comparing with eight homogeneous and heterogeneous network models on two pan-cancer datasets, MCDHGN has better performance on AUC and AUPR values. Additionally, MCDHGN provides interpretability of predicted cancer driver genes through the varying weights of biologically meaningful meta-paths. https://github.com/1160300611/MCDHGN.

Four Task Characteristics and Their Influence on Speaking Performance and Engagement

Four Task Characteristics and Their Influence on Speaking Performance and Engagement

Combining dual attention mechanism and efficient feature aggregation for road and vehicle segmentation from UAV imagery

Unmanned aerial vehicles (UAVs) have gained significant popularity in recent years due to their ability to capture high-resolution aerial imagery for various applications, including traffic monitoring, urban planning, and disaster management. Accurate road and vehicle segmentation from UAV imagery plays a crucial role in these applications. In this paper, we propose a novel approach combining dual attention mechanisms and efficient multi-layer feature aggregation to enhance the performance of road and vehicle segmentation from UAV imagery. Our approach integrates a spatial attention mechanism and a channel-wise attention mechanism to enable the model to selectively focus on relevant features for segmentation tasks. In conjunction with these attention mechanisms, we introduce an efficient multi-layer feature aggregation method that synthesizes and integrates multi-scale features at different levels of the network, resulting in a more robust and informative feature representation. Our proposed method is evaluated on the UAVid semantic segmentation dataset, showcasing its exceptional performance in comparison to renowned approaches such as U-Net, DeepLabv3+, and SegNet. The experimental results affirm that our approach surpasses these state-of-the-art methods in terms of segmentation accuracy.

MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3-D CT Lesions.

With the renaissance of deep learning, automatic diagnostic algorithms for computed tomography (CT) have achieved many successful applications. However, they heavily rely on lesion-level annotations, which are often scarce due to the high cost of collecting pathological labels. On the other hand, the annotated CT data, especially the 3-D spatial information, may be underutilized by approaches that model a 3-D lesion with its 2-D slices, although such approaches have been proven effective and computationally efficient. This study presents a multiview contrastive network (MVCNet), which enhances the representations of 2-D views contrastively against other views of different spatial orientations. Specifically, MVCNet views each 3-D lesion from different orientations to collect multiple 2-D views; it learns to minimize a contrastive loss so that the 2-D views of the same 3-D lesion are aggregated, whereas those of different lesions are separated. To alleviate the issue of false negative examples, the uninformative negative samples are filtered out, which results in more discriminative features for downstream tasks. By linear evaluation, MVCNet achieves state-of-the-art accuracies on the lung image database consortium and image database resource initiative (LIDC-IDRI) (88.62%), lung nodule database (LNDb) (76.69%), and TianChi (84.33%) datasets for unsupervised representation learning. When fine-tuned on 10% of the labeled data, the accuracies are comparable to the supervised learning models (89.46% versus 85.03%, 73.85% versus 73.44%, 83.56% versus 83.34% on the three datasets, respectively), indicating the superiority of MVCNet in learning representations with limited annotations. Our findings suggest that contrasting multiple 2-D views is an effective approach to capturing the original 3-D information, which notably improves the utilization of the scarce and valuable annotated CT data.

Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.

Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However, previous studies are largely node-centralized and ignore edge features for graph classification tasks, resulting in moderate performance of graph classification accuracy. Moreover, the generalizability of GNN model is still far from satisfactory in brain disorder [e.g., autism spectrum disorder (ASD)] identification due to considerable individual differences in symptoms among patients as well as data heterogeneity among different sites. In order to address the above limitations, this study proposes a novel adversarial learning-based node-edge graph attention network (AL-NEGAT) for ASD identification based on multimodal MRI data. First, both node and edge features are modeled based on structural and functional MRI data to leverage complementary brain information and preserved in the constructed weighted adjacent matrix for individuals through the attention mechanism in the proposed NEGAT. Second, two AL methods are employed to improve the generalizability of NEGAT. Finally, a gradient-based saliency map strategy is utilized for model interpretation to identify important brain regions and connections contributing to the classification. Experimental results based on the public Autism Brain Imaging Data Exchange I (ABIDE I) data demonstrate that the proposed framework achieves a classification accuracy of 74.7% between ASD and typical developing (TD) groups based on 1007 subjects across 17 different sites and outperforms the state-of-the-art methods, indicating satisfying classification ability and generalizability of the proposed AL-NEGAT model. Our work provides a powerful tool for brain disorder identification.

A Bayesian network for simultaneous keyframe and landmark detection in ultrasonic cine

Accurate landmark detection in medical imaging is essential for quantifying various anatomical structures and assisting in diagnosis and treatment planning. In ultrasound cine, landmark detection is often associated with identifying keyframes, which represent the occurrence of specific events, such as measuring target dimensions at specific temporal phases. Existing methods predominantly treat landmark and keyframe detection as separate tasks without harnessing their underlying correlations. Additionally, owing to the intrinsic characteristics of ultrasound imaging, both tasks are constrained by inter-observer variability, leading to potentially higher levels of uncertainty. In this paper, we propose a Bayesian network to achieve simultaneous keyframe and landmark detection in ultrasonic cine, especially under highly sparse training data conditions. We follow a coarse-to-fine landmark detection architecture and propose an adaptive Bayesian hypergraph for coordinate refinement on the results of heatmap-based regression. In addition, we propose Order Loss for training bi-directional Gated Recurrent Unit to identify keyframes based on the relative likelihoods within the sequence. Furthermore, to exploit the underlying correlation between the two tasks, we use a shared encoder to extract features for both tasks and enhance the detection accuracy through the interaction of temporal and motion information. Experiments on two in-house datasets (multi-view transesophageal and transthoracic echocardiography) and one public dataset (transthoracic echocardiography) demonstrate that our method outperforms state-of-the-art approaches. The mean absolute errors for dimension measurements of the left atrial appendage, aortic annulus, and left ventricle are 2.40 mm, 0.83 mm, and 1.63 mm, respectively. The source code is available at github.com/warmestwind/ABHG.

Shift-insensitive perceptual feature of quadratic sum of gradient magnitude and LoG signals for image quality assessment and image classification

Most existing full-reference (FR) Image quality assessment (IQA) models work in the premise of that the two images should be well registered. Shifting an image would lead to an inaccurate evaluation of image quality, because small spatial shifts are far less noticeable than structural distortion for human observers. To this regard, we propose to study an IQA feature that is shift-insensitive to the basic primitive structure of images, i.e., image edge. According to previous studies, the image gradient magnitude (GM) and the Laplacian of Gaussian (LoG) operator that depict the edge profiles of natural images are highly efficient structural features in IQA tasks. In this paper, we find that the Quadratic sum of the normalized GM and the LoG signals (QGL) has excellent shift-insensitive property in representing image edges after theoretically solving the selection problem of a ratio parameter to balance the GM and LoG signals. Based on the proposed QGL feature, two FR-IQA models can be built directly by measuring the similarity map with mean and standard deviation pooling strategies, named mQGL and sQGL, respectively. Experimental results show that the proposed sQGL and mQGL work robustly on four benchmark IQA databases, and QGL-based models show great shift-insensitive property to spatial translation and image rotation while judging the image quality. In addition, we explore the feasibility of combining QGL feature with deep neural networks, and verify that it can help to promote image pattern recognition in texture classification tasks.

An auto-encoder bio medical signal transmission through custom convolutional neural network

The transmission of biomedical signals in real-time is extremely difficult and necessitates the use of cloud and internet of things (IoT) infrastructure. Security is also an important consideration, however, to achieve this, a reconstruction method is developed where the entire signal is fed as an input, just the primary portion, the entire signal is taken then encoded, and then deliver to the destination. It is unlocked using a reconstruction technique without any signal attenuation. The key difficulty is how to manage the sensor network once the input is prepared for transmission. This has problems with extremely high network energy consumption and accurate data collection. The accuracy of data reconstruction through is improved by compressive sensing. The lifespan of the network as a whole could be extended, in this study; the proposed proposed system convolutional neural network (PS-CNN) is an integrated model that combines feature selection and auto-encoder. In order to produce the most useful features for particular tasks, our algorithm can eventually separate the appropriate task units from the irrelevant tasks.

ESSR: Evolving Sparse Sharing Representation for Multitask Learning

Multi-task learning uses knowledge transfer among tasks to improve the generalization performance of all tasks. For deep multi-task learning, knowledge transfer is often implemented via sharing all hidden features of tasks. A major shortcoming is that it can lead to negative knowledge transfer across tasks when task correlation is weak. To overcome it, this paper proposes an evolutionary method to learn sparse sharing representations adaptively. By embedding the neural network optimization into evolutionary multitasking, our proposed method finds an optimal combination of tasks and sharing features. It can identify negative correlation and redundant features and then remove them from the hidden feature set. Thus, an optimal sparse sharing subnetwork can be produced for each task. Experiment results show that the proposed method achieve better learning performance with a smaller inference model than other related methods.

Embedding Tasks Into the Latent Space: Cross-Space Consistency for Multi-Dimensional Analysis in Echocardiography.

Multi-dimensional analysis in echocardiography has attracted attention due to its potential for clinical indices quantification and computer-aided diagnosis. It can utilize various information to provide the estimation of multiple cardiac indices. However, it still has the challenge of inter-task conflict. This is owing to regional confusion, global abnormalities, and time-accumulated errors. Task mapping methods have the potential to address inter-task conflict. However, they may overlook the inherent differences between tasks, especially for multi-level tasks (e.g., pixel-level, image-level, and sequence-level tasks). This may lead to inappropriate local and spurious task constraints. We propose cross-space consistency (CSC) to overcome the challenge. The CSC embeds multi-level tasks to the same-level to reduce inherent task differences. This allows multi-level task features to be consistent in a unified latent space. The latent space extracts task-common features and constrains the distance in these features. This constrains the task weight region that satisfies multiple task conditions. Extensive experiments compare the CSC with fifteen state-of-the-art echocardiographic analysis methods on five datasets (10,908 patients). The result shows that the CSC can provide left ventricular (LV) segmentation, (DSC = 0.932), keypoint detection (MAE = 3.06mm), and keyframe identification (accuracy = 0.943). These results demonstrate that our method can provide a multi-dimensional analysis of cardiac function and is robust in large-scale datasets.

Survey of Techniques and Applications in Object Recognition

Abstract: Object detection, which is closely related to video analysis and image comprehension, has received attention in years from researchers. Traditional approaches to object detection rely on crafted features and shallow trainable architectures. However, these methods face challenges. Often reach a plateau, in performance improvement. To overcome these limitations complex ensembles are often created by combining low-level image features with high-level context from object detectors and scene classifiers. The emergence of learning has introduced powerful tools that can learn semantics high level and deeper features. These tools address the limitations of architectures by introducing network structures training strategies and optimization functions. This paper provides a review of learning-based frameworks for object detection. The review starts with an overview of the history of learning with a particular emphasis on the Convolutional Neural Network (CNN) as an exemplary tool. It then delves into generic object detection architectures exploring modifications and effective strategies for improving detection performance. Recognizing the characteristics of detection tasks, the paper also briefly surveys several tasks such, as salient object detection, face detection, crowd analysis, and pedestrian detection.

Crowdsourced Fact-checking: Does It Actually Work?

There is an important ongoing effort aimed to tackle misinformation and to perform reliable fact-checking by employing human assessors at scale, with a crowdsourcing-based approach. Previous studies on the feasibility of employing crowdsourcing for the task of misinformation detection have provided inconsistent results: some of them seem to confirm the effectiveness of crowdsourcing for assessing the truthfulness of statements and claims, whereas others fail to reach an effectiveness level higher than automatic machine learning approaches, which are still unsatisfactory. In this paper, we aim at addressing such inconsistency and understand if truthfulness assessment can indeed be crowdsourced effectively. To do so, we build on top of previous studies; we select some of those reporting low effectiveness levels, we highlight their potential limitations, and we then reproduce their work attempting to improve their setup to address those limitations. We employ various approaches, data quality levels, and agreement measures to assess the reliability of crowd workers when assessing the truthfulness of (mis)information. Furthermore, we explore different worker features and compare the results obtained with different crowds. According to our findings, crowdsourcing can be used as an effective methodology to tackle misinformation at scale. When compared to previous studies, our results indicate that a significantly higher agreement between crowd workers and experts can be obtained by using a different, higher-quality, crowdsourcing platform and by improving the design of the crowdsourcing task. Also, we find differences concerning task and worker features and how workers provide truthfulness assessments.

Multi-channel and multi-featured extended orb with gabor filter and non-maximum suppression

This research paper presents an enhanced approach to feature extraction using the Oriented FAST and Rotated BRIEF (ORB) algorithm (Rublee et al., 2014). The proposed method leverages the power of Gabor filters (Mehrotra et al., 1992) in combination with Non-Maximum Suppression (Neuback and Van Gool, 2006) to improve the robustness and efficiency of feature detection in computer vision applications. The process begins with the acquisition of an RGB image, which is then transformed into seven single-channel images representing different color and intensity aspects: red, green, blue, hue, saturation, value, and grayscale. Each single-channel image is independently subjected to Gabor filtering, resulting in seven Gabor-filtered images. These images capture distinct texture and frequency information, enhancing the discriminative power of subsequent feature extraction. Subsequently, the Oriented FAST and Rotated BRIEF (ORB) algorithm is applied to each of the seven Gabor-filtered images to extract key points and descriptors. This step yields a comprehensive set of keypoints (Mallick et al., 2015) and descriptors, capturing rich local feature information across multiple image channels. To further refine the extracted features and eliminate redundant keypoints, Non-Maximum Suppression is employed independently on each single-channel image. This process effectively filters out overlapped and false keypoints, ensuring the selection of only the most salient features for downstream tasks. Experimental results demonstrate the efficacy of the proposed approach in improving feature detection performance compared to traditional methods. The combination of Gabor filtering and Non-Maximum Suppression enhances feature discriminability, robustness to noise, and resistance to image transformations, making it well-suited for various computer vision applications, including object recognition, image matching, and scene understanding.

Differential Graph Convolution Network for point cloud understanding

Smoothing of the graph convolution is not conducive to characterizing local differences of point cloud. To solve this problem, we propose a Differential Graph Convolutional Network (Differ-GCN) for point cloud analysis. First, we propose a new graph construction strategy that can make similar nodes in the local space belong to the same graph, which can better represent the local commonality. After that, the features of the graph are extracted by the similarity matrix. Some of the smoothing information of the graph is removed to optimize the over-smoothing nodes and combined with the local difference of the points to get the beneficial features for downstream tasks. Finally, each neighbor point is processed to generate a mask, and pooling is performed through the mask to reduce information loss. The experiment results show that Differ-GCN performs excellent in object classification and part segmentation. The processing speed of Differ-GCN for point cloud is much faster than the state-of-the-art methods.