Context-aware Features Research Articles

UFO-Net: A Linear Attention-Based Network for Point Cloud Classification

Three-dimensional point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we designed an augmented sampling and grouping module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to extract point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first tried to use a linearly normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module was adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that this method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which is 0.5% higher than PCT. Our network also achieved 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT.

C-DeepTrust: A Context-Aware Deep Trust Prediction Model in Online Social Networks.

Trust prediction provides valuable support for decision making, information dissemination, and product promotion in online social networks. As a complex concept in the social network community, trust relationships among people can be established virtually based on: 1) their interaction behaviors, e.g., the ratings and comments that they provided; 2) the contextual information associated with their interactions, e.g., location and culture; and 3) the relative temporal features of interactions and the time periods when the trust relationships hold. Most of the existing works only focus on some aspects of trust, and there is not a comprehensive study of user trust development that considers and incorporates 1)-3) in trust prediction. In this article, we propose a context-aware deep trust prediction model C-DeepTrust to fill this gap. First, we conduct user feature modeling to obtain the user's static and dynamic preference features in each context. Static user preference features are obtained from all the ratings and reviews that a user provided, while dynamic user preference features are obtained from the items rated/reviewed by the user in time series. The obtained context-aware user features are then combined and fed into the multilayer projection structure to further mine the context-aware latent features. Finally, the context-aware trust relationships between users are calculated by their context-aware feature vector cosine similarities according to the social homophily theory, which shows a pervasive property of social networks that trust relationships are more likely to be developed among similar people. Extensive experiments conducted on two real-world datasets show the superior performance of our approach compared with the representative baseline methods.

Context Elements Taxonomy for Intelligent Transportation Systems

Design and development of context-aware Intelligent Transportation Systems (ITS) are not trivial due to the large number of possible context elements that may be relevant to the application and the lack of structured information to guide system designers in this task. This paper proposes that context elements with common characteristics can be grouped into categories, and these categories can be organized in a taxonomy. This taxonomy could help system designers with the task of modeling and developing new context-aware ITS. We performed a literature review of 68 articles describing 70 ITS applications with context-aware features to identify context elements used in this type of application. Furthermore, we also analyzed three commercial ITS applications. We used data collected from the analysis of these 73 projects to define the categories and identify their relationships. We propose a taxonomy with 79 categories, with 57 leaf categories (a category without children subcategories). We also performed two experiments to validate whether the exposure to this taxonomy could improve the quality of an ITS application during its design, with favorable results showing a 2.7 times increase in the average amount of relevant context elements used in the application. Finally, we compiled a knowledge base of which context element categories are used in the 73 analyzed projects. It is another companion information that can be used to help system designers. The proposed taxonomy of context element categories organizes the information of the context-aware ITS domain in a way that can ease the task of designing such systems and improve the usage of context-aware features. The overall methodology used in this work to create the taxonomy for the ITS domain could be applied to other popular domains of context-aware applications.

Bridging Multi-Scale Context-Aware Representation for Object Detection

Feature Pyramid Network (FPN) exploits multi-scale fusion representation to deal with scale variances in object detection. However, it ignores the context information gap across different levels. In this paper, we develop a plug-and-play detector, the multi-scale context-aware feature pyramid network to unleash the power of feature pyramid representation. Based on the dilated feature map at the highest level of the backbone, we propose the cross-scale context aggregation block to make full use of context information in the feature pyramid. Moreover, we extract discriminative features among different levels by the adaptive context aggregation block for robust object detection. Comprehensive experiments on MS-COCO demonstrate the effectiveness and efficiency of the proposed network, where about 1.0 ~ 3.0 AP improvements are achieved compared with existing FPN-based methods. In addition, we also conduct extensive experiments on pixel-level prediction tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., instance segmentation, semantic segmentation, and panoptic segmentation, which further verify the effectiveness of the proposed method.

Library book recommendation with CNN-FM deep learning approach

PurposeTraditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.Design/methodology/approachThe authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.FindingsThe authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.Research limitations/implicationsIt requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.Practical implicationsThe embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.Originality/valueThe proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

Biomedical text summarization (BTS) is proving to be an emerging area of work and research with the need for sustainable healthcare applications such as evidence-based medicine practice (EBM) and telemedicine which help effectively support healthcare needs of the society. However, with the rapid growth in the biomedical literature and the diversities in its structure and resources, it is becoming challenging to carry out effective text summarization for better insights. The goal of this work is to conduct a comprehensive systematic literature review of significant and high-impact literary work in BTS with a deep understanding of its major artifacts such as databases, semantic similarity measures, and semantic enrichment approaches. In the systematic literature review conducted, we applied search filters to find high-impact literature in the biomedical text summarization domain from IEEE, SCOPUS, Elsevier, EBSCO, and PubMed databases. The systematic literature review (SLR) yielded 81 works; those were analyzed for qualitative study. The in-depth study of the literature shows the relevance and efficacy of the deep learning (DL) approach, context-aware feature extraction techniques, and their relevance in BTS. Biomedical question answering (BQA) system is one of the most popular applications of text summarizations for building self-sufficient healthcare systems and are pointing to future research directions. The review culminates in realization of a proposed framework for the BQA system MEDIQA with design of better heuristics for content screening, document screening, and relevance ranking. The presented framework provides an evidence-based biomedical question answering model and text summarizer that can lead to real-time evidence-based clinical support system to healthcare practitioners.

Flow-based intrusion detection system in Vehicular Ad hoc Network using context-aware feature extraction

Autonomous transportation systems have an immense impact on the way of our day-to-day commute or traveling. Connectedness is an inseparable part of these systems, and it is possible through Intelligent Transportation Systems applications. These applications use a network system known as Vehicular Ad hoc Network (VANET). However, the safety that is provided by VANETs can be easily compromised by malicious users. Hence there is a need for an Intrusion Detection System (IDS). We designed an IDS model that can collect network data cooperatively from vehicles and Roadside Units (RSUs). For training the core of our proposed IDS we generated synthetic network data with the help of the widely used Network Simulator 3 (ns-3) and Simulation of Urban Mobility (SUMO) simulation tools. We also generated separate test data that is not just a split part of the training data. This ensures the elimination of the misleading performance result of an overfitted model. We employed a multi-class IDS using Convolutional Neural Network (CNN) with a novel feature extraction method known as Context-Aware Feature Extraction-Based CNN (CAFECNN). The CAFECNN model takes advantage of the collected network flow data to detect both passive and active types of attacks. The results show that the proposed model is stronger in identification of hard-to-detect passive attacks compared to traditional machine learning methods. We also evaluated the model using real-time simulations and observed an immediate improvement in the performance of the network despite the intruders, especially in terms of packet delivery ratio and network throughput.

Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions.

The human genome encodes over 500 distinct protein kinases which regulate nearly all cellular processes by the specific phosphorylation of protein substrates. While advances in mass spectrometry and proteomics studies have identified thousands of phosphorylation sites across species, information on the specific kinases that phosphorylate these sites is currently lacking for the vast majority of phosphosites. Recently, there has been a major focus on the development of computational models for predicting kinase-substrate associations. However, most current models only allow predictions on a subset of well-studied kinases. Furthermore, the utilization of hand-curated features and imbalances in training and testing datasets pose unique challenges in the development of accurate predictive models for kinase-specific phosphorylation prediction. Motivated by the recent development of universal protein language models which automatically generate context-aware features from primary sequence information, we sought to develop a unified framework for kinase-specific phosphosite prediction, allowing for greater investigative utility and enabling substrate predictions at the whole kinome level. We present a deep learning model for kinase-specific phosphosite prediction, termed Phosformer, which predicts the probability of phosphorylation given an arbitrary pair of unaligned kinase and substrate peptide sequences. We demonstrate that Phosformer implicitly learns evolutionary and functional features during training, removing the need for feature curation and engineering. Further analyses reveal that Phosformer also learns substrate specificity motifs and is able to distinguish between functionally distinct kinase families. Benchmarks indicate that Phosformer exhibits significant improvements compared to the state-of-the-art models, while also presenting a more generalized, unified, and interpretable predictive framework. Code and data are available at https://github.com/esbgkannan/phosformer. Supplementary data are available at Bioinformatics online.

3D Reconstruction for Multi-view Objects

Deep learning-based 3D reconstruction neural networks have achieved good performance on generating 3D features from 2D features. However, they often lead to feature loss in reconstruction. In this paper we propose a multi-view object 3D reconstruction neural network, named P2VNet. The depth estimation module of the front and back layers of P2VNet realizes the smooth transformation from 2D features to 3D features, which improves the performance of single view reconstruction. A multi-scale fusion sensing module in multi-view fusion is also proposed, where more receptive fields are added to generate richer context-aware features. We also introduce 3DFocal Loss to replace binary cross-entropy to address the problems of unbalanced space occupation of the voxel grid and complex division of partial grid occupation. Our experimental results have demonstrated that P2VNet has achieved higher accuracy than existing works.

TCRNet: A Trifurcated Cascaded Refinement Network for Salient Object Detection

Due to the rapid development of deep learning, salient object detection has achieved promising results in recent years. However, how to construct a powerful saliency detection network to generate saliency maps with completely highlighted salient objects and effectively suppressed background noise still remains an open and challenging problem. In this paper, we propose a novel trifurcated cascaded refinement network (TCRNet) to explore multi-level feature fusion and global information representation. Specifically, all the side-output features from the backbone network are divided into three branches (i.e., global information, deep information and shallow information) according to feature similarities, and then the global contexts, semantic knowledge and fined details respectively generated by the three branches are integrated in a cascaded refinement way. This refinement strategy aims to guide the feature learning of important regions and suppress background noises. Moreover, we propose a cross-level feature fusion (CFF) module to fuse multi-level similar features within a same branch by exploring their interrelationship, thus capturing the important semantic information and structure details. To this end, the complementarity of similar features is utilized to refine feature at each level by sharing an encoded spatial weight map. Besides, we design a context-aware feature learning (CFL) module to capture global contexts in a single layer by taking account of both single-feature representation and multi-feature interaction, thus providing important location information of salient objects. Extensive experiments on five benchmarks are conducted to demonstrate that the proposed network TCRNet performs favorably against 20 state-of-the-art salient object detection methods on five benchmark datasets, indicating the effectiveness and superiority of our method.

Multi-Exposure Image Fusion via Multi-Scale and Context-Aware Feature Learning

In this letter, a deep learning (DL)-based multi-exposure image fusion (MEF) method via multi-scale and context-aware feature learning is proposed, aiming to overcome the defects of existing traditional and DL-based methods. The proposed network is based on an auto-encoder architecture. First, an encoder that combines the convolutional network and Transformer is designed to extract multi-scale features and capture the global contextual information. Then, a multi-scale feature interaction (MSFI) module is devised to enrich the scale diversity of extracted features using cross-scale fusion and Atrous spatial pyramid pooling (ASPP). Finally, a decoder with a nest connection architecture is introduced to reconstruct the fused image. Experimental results show that the proposed method outperforms several representative traditional and DL-based MEF methods in terms of both visual quality and objective assessment. The source code and trained model are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yuliu316316/MSCA-MEF</uri> .

Crowd Counting Based on Context-Aware and Multi Scale Feature Fusion

Crowd counting plays an important role in public security. Estimating the number of people in an image with congested crowd accurately is a challenging task. The crowd counting method based on fully convolutional network can perform well in crowd image with complex scene. In this paper, to address the counting problems of occlusion，background clutter and perspective effect, we proposed a simple but effective method called Context-aware Multi scale Fusion Network(CMF Net).The CMF Net applied VGG network as backbone to extract coarse features. Then, three context-aware multi-scale fusion modules (CMFM) are adopted. Each CMFM consist of multi-scale feature extraction module (MEM) and context-aware feature extraction module (CEM). In addition, we propose adaptively dense connection to promoted information transmission in the counting network. Experiments on four datasets demonstrate that our network achieves competitive and effective results.

Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network

Background and ObjectiveWhole slide image (WSI) classification and lesion localization within giga-pixel slide are challenging tasks in computational pathology that requires context-aware representations of histological features to adequately infer nidus. The existing weakly supervised learning methods mainly treat different locations in the slide as independent regions and cannot learn potential nonlinear interactions between instances based on i.i.d assumption, resulting in the model unable to effectively utilize context-ware information to predict the labels of WSIs and locate the region of interest (ROI). MethodsHere, we propose an interpretable classification model named bidirectional Attention-based Multiple Instance Learning Graph Convolutional Network (ABMIL-GCN), which hierarchically aggregates context-aware features of instances into a global representation in a topology fashion to predict the slide labels and localize the region of lymph node metastasis in WSIs. ResultsWe verified the superiority of this method on the Camelyon16 dataset, and the results show that the average predicted ACC and AUC of the proposed model after flooding optimization can reach 90.89% and 0.9149, respectively. The average accuracy and ACC score are improved by more than 7% and 4% compared with the existing state-of-the-art algorithms. ConclusionsThe results demonstrate that context-aware GCN outperforms existing weakly supervised learning methods by introducing spatial correlations between the neighbor image patches, which also addresses the ‘accuracy-interpretability trade-off’ problem. The framework provides a novel paradigm for the clinical application of computer-aided diagnosis and intelligent systems.

Predicting length of stay in ICU and mortality with temporal dilated separable convolution and context-aware feature fusion

In healthcare, Intensive Care Unit (ICU) bed management is a necessary task because of the limited budget and resources. Predicting the remaining Length of Stay (LoS) in ICU and mortality can assist clinicians in managing ICU beds efficiently. This study proposes a deep learning method based on several successive Temporal Dilated Separable Convolution with Context-Aware Feature Fusion (TDSC-CAFF) modules, and a multi-view and multi-scale feature fusion for predicting the remaining LoS and mortality risk for ICU patients. In each TDSC-CAFF module, temporal dilated separable convolution is used to encode each feature separately, and context-aware feature fusion is proposed to capture comprehensive and context-aware feature representations from the input time-series features, static demographics, and the output of the last TDSC-CAFF module. The CAFF outputs of each module are accumulated to achieve multi-scale representations with different receptive fields. The outputs of TDSC and CAFF are concatenated with skip connection from the output of the last module and the original time-series input. The concatenated features are processed by the proposed Point-Wise convolution-based Attention (PWAtt) that captures the inter-feature context to generate the final temporal features. Finally, the final temporal features, the accumulated multi-scale features, the encoded diagnosis, and static demographic features are fused and then processed by fully connected layers to obtain prediction results. We evaluate our proposed method on two publicly available datasets: eICU and MIMIC-IV v1.0 for LoS and mortality prediction tasks. Experimental results demonstrate that our proposed method achieves a mean squared log error of 0.07 and 0.08 for LoS prediction, and an Area Under the Receiver Operating Characteristic Curve of 0.909 and 0.926 for mortality prediction, on eICU and MIMIC-IV v1.0 datasets, respectively, which outperforms several state-of-the-art methods.

Variability Management in Dynamic Software Product Lines for Self-Adaptive Systems—A Systematic Mapping

Context: Dynamic software product lines (DSPLs) have considerably increased their adoption for variability management for self-adaptive systems. The most widely used models for managing the variability of DSPLs are the MAPE-K control loop and context-aware feature models (CFMs). Aim: In this paper, we review and synthesize evidence of using variability constraint approaches, methodologies, and challenges for DSPL. Method: We conducted a systematic mapping, including three research questions. This study included 84 papers published from 2010 to 2021. Results: The main results show that open-dynamic variability shows a presence in 57.1% of the selected papers, and on the other hand, closed-dynamic variability appears in 38.1%. The most commonly used methodology for managing a DSPL environment is based on proprietary architectures (60.7%), where the use of CFMs predominates. For open-dynamic variability approaches, the MAPE-K control loop is mainly used. The main challenges in DSPL management are based on techniques (28.6%) and open variation (21.4%). Conclusions: Open-dynamic variability has prevailed over the years as the primary approach to managing variability in DSPL, where its primary methodology is the MAPE-K control loop. Response RQ3 requires further review.

Learning diverse and deep clues for person reidentification

Extracting robust features has been the core of person re-identification(ReID). The existing convolutional neural network-based methods pay more attention to local features rather than the connection between local features. Given that human bodies possess certain structural information, it is absolutely necessary to strengthen the connection of local features for the ReID task. This paper proposes a two-stage attention network termed Width and Depth Channel Attention Network (WDC-Net) for ReID. Unlike conventional attention-based methods, which only focus on the single local features, our network exploits diverse feature representations to alleviate the missing information problem caused by occlusion. Precisely, for the first stage, it splits the local associations of the feature map through a multi-scale perspective to extract relatively independent multi-level local features of the human body. For the second stage, the correlation of multi-level local features is reconstructed through grouped pyramid structure to obtain a more robust global feature representation. We also propose an adaptive margin weight adjustment strategy to enhance the adaptability of the attention weights. Large-scale ReID datasets are tested to evaluate our method. On Market1501 and DukeMTMC, the proposed method achieves 90.7 % /96.4% mAP/R-1 and 81.8 % /90.8% mAP/R-1, respectively. It is worth highlighting that the proposed method also achieves 55.3 % /65.3% mAP/R-1 on the challenging Occluded-Duke dataset. Extensive experimental results demonstrate the superiority of our method, which achieves state-of-the-art performance on ReID. • An efficient framework is used to handle both historic ReID and occluded ReID. • Exploring diverse local features through the width channel attention. • Constructing global context-aware features with pyramid attention. • Enhancing the adaptability of the attention weights.

Deep Effective k-mer representation learning for polyadenylation signal prediction via co-occurrence embedding

The polyadenylation process begins as the transcription of a gene terminates, and polyadenylation signal prediction by the genomic sequence is the key to understanding the mechanism of mRNA metabolism. Computational algorithms are widely proposed for polyadenylation signal prediction, but they mainly rely on sequences’ statistical and compositional characteristics. In this work, we conduct adaptively context-aware feature learning for polyadenylation signal prediction via a deep model and co-occurrence embedding. Specifically, we devise an activation function with dynamic parameters to control the scale of features channel-wisely for the positive and negative part value of neuron outputs, and then use the statistical information of k-mer occurrences as features of polyadenylation data, which is pre-trained by an unsupervised learning algorithm. Next, by combining multiscale convolutions with adaptive coefficients, long short-term memory networks jointly extract spatial and temporal contextual patterns. The patterns are fused to obtain contextual information by an identity skip connection and addition strategy. Experimental results demonstrate that our proposed model achieves more remarkable performance than state-of-the-art methods across polyadenylation signal benchmarks, and an ablation study shows the effectiveness of the model, and k-mer embedding is a good feature representation for polyadenylation signals.

Long-Distance Vehicle Dynamic Detection and Positioning Based on Gm-APD Lidar and LIDAR-YOLO

Aiming at the problems of long-distance night dynamic object detection in military operations, such as difficult imaging of traditional equipment, a small number of object pixels, a large difference in object size at different distances, and poor real-time performance. A long-distance vehicle detection method based on the LIDAR-YOLO network for Gm-APD (Geiger mode avalanche photodiode) lidar image is proposed. This method is improved based on YOLOv5s, which can detect long-distance vehicles and accurately locate motion trajectories in real-time. Firstly, the intensity image I of the lidar is preprocessed and combined with the distance image R, the BC_IIR image is obtained, and the vehicle detection data set is established. Then, according to the obvious vehicle features in the BC_IIR image, the low-complexity backbone network LIDARNet is redesigned, and several attention mechanism modules are added. The network feature extraction ability is enhanced by comparing and selecting the CA attention mechanism module. In the network prediction layer, context-aware feature map fusion is added to improve the detection precision of vehicles at different distances. Next, a GIOU loss is introduced to improve the detection precision of occluded vehicles. Finally, the method can depict the trajectory of the vehicle in 3D space based on distance information. After testing, the network achieves a detection speed of 62.67 frames per second (FPS), recall rate (R) = 98.19%, precision rate (P) = 95.53%, and F1 is 0.9675. The results show that the algorithm has better performance than other methods.

Improved U-Net-Like Network for Visual Saliency Detection Based on Pyramid Feature Attention

As a widely used technology, visual saliency detection has attracted a lot of attention in the past decades. Although a large number of methods, especially fully convolutional neural network- (FCN-) based approaches, have been proposed and achieved remarkable performance, it is still of great value to extend representative architecture to visual saliency detection task. In this paper, we propose an improved U-Net-like network, pyramid feature attention-based U-Net-like (PFAU-Net) for visual saliency detection problem. The main improvements of the proposed model include that in order to enable the network to extract features with more representation ability, we introduce a context-aware feature extraction (CFE) module and a channel attention module into the U-shaped backbone to obtain valuable multiscale features, and a feature pyramid path is also utilized in the decoder part of the network to take advantages of multilevel information. Moreover, we construct the loss function using three terms including pixel-level cross-entropy, image-level intersection over union (IoU), and a structural similarity term, which aim to make the model learn more saliency related knowledge. To verify the effectiveness of the proposed model, we conduct extensive experiments on six widely used public datasets, and the experimental results indicate that (1) our improved model can significantly improve the performance of the backbone network on all test datasets, and (2) our proposed model can outperform comparison FCN-based networks and nonneural network approaches. Both objective and qualitative evaluations verify the effectiveness of our proposed model.

A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining

Process mining aims to gain knowledge of business processes via the discovery of process models from event logs generated by information systems. The insights revealed from process mining heavily rely on the quality of the event logs. Activities extracted from different data sources or the free-text nature within the same system may lead to inconsistent labels. Such inconsistency would then lead to redundancy in activity labels, which refer to labels that have different syntax but share the same behaviours. Redundant activity labels can introduce unnecessary complexities to the event logs. The identification of these labels from data-driven process discovery are difficult and rely heavily on human intervention. Neither existing process discovery algorithms nor event data preprocessing techniques can solve such redundancy efficiently. In this paper, we propose a multi-view approach to automatically detect redundant activity labels by using not only context-aware features such as control–flow relations and attribute values but also semantic features from the event logs. Our evaluation of several publicly available datasets and a real-life case study demonstrate that our approach can efficiently detect redundant activity labels even with low-occurrence frequencies. The proposed approach can add value to the preprocessing step to generate more representative event logs.