Aware Network Research Articles

STA-net: a deblurring network combined with spatiotemporal information for zinc froth flotation

Abstract The clarification of blurry frames in froth flotation videos is crucial for the identification and fault diagnosis of zinc flotation conditions. Irregular motion blur represents the primary blur phenomenon in froth flotation videos. Existing methods for motion blur removal have not effectively integrated spatio-temporal information, overlooking the utilization of clear frames, thus failing to achieve satisfactory results in the removal of froth flotation video blur frames. Therefore, this paper proposes a Spatio Temporal Awareness network (STA-net) capable of utilizing the temporal and spatial information from consecutive frames to naturally reconstruct froth images with clarity. This network consists of two sub-networks: the Temporal network (T-net) and the Spatio network (S-net). T-net extracts dynamic information between adjacent clear frames and blurry frames, while S-net captures spatial information within frames and enhances spatial texture details for the coarse images reconstructed based on the temporal information extracted by T-net in a layer-by-layer manner. Additionally, considering that blurry image datasets from actual froth flotation processes often lack corresponding clear images as training labels for the network, a method based on probability density functions (PDF) for generating blurry froth image datasets based clear images is proposed. This method extracts motion vectors from froth images to create blurry froth images that closely resemble the actual blurry conditions of froth images. The deblurring performance of STA-net has been validated in practical froth flotation processes.

How higher education institutions may catalyse regional innovation ecosystems: The case of polytechnics in Portugal

This article explores the crucial role of higher education institutions in catalysing regional innovation ecosystems. In particular, it aims to study how polytechnics in Portugal have been involved in this process. The research utilizes an exploratory and reflective case study approach to present real-world evidence of collaborative efforts within innovation networks that involve both businesses and the social economy. Thus, the study raises awareness of collaborative innovation and entrepreneurship networks, emphasizing genuine interactive learning and knowledge exchange. Polytechnic institutions can enhance regional competitiveness by providing education and training for future entrepreneurs and highly skilled professionals, as well as offering technological and managerial solutions that drive value creation and the emergence of new businesses and markets, potentially even on an international level. The study also emphasizes the fourth mission of higher education institutions, particularly polytechnic institutes, focusing on empowering communities through social innovation projects for sustainable local and regional development.

Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94.97% and 94.27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks.

Using social network analysis to assess connections within climate and energy education organizations: A case study conducted by the Climate Literacy and Energy Awareness Network (CLEAN)

Climate change education (CCE) plays a critical role in climate empowerment and addressing the challenge of climate change. We present results from the first social network analysis of CCE in the U.S. We surveyed CCE professionals through an established CCE network and wide dissemination from CCE professionals about their interactions with others across 5 dimensions: (1) climate and energy education; (2) climate action, climate empowerment, or community engagement; (3) workforce development in the climate and energy field; (4) diversity, equity, inclusion, and anti-racism; and (5) climate or energy policy. The resulting network analysis shows some cohesiveness, especially for the first two dimensions; however, network cohesiveness could be improved. The findings also show the keystone organizations and brokers across the CCE field. This study provides a foundation for developing targeted strategies to strengthen CCE for improving coordinated and collaborative climate action.

Social Trust Confirmation-Based Selfish Node Detection Algorithm in Socially Aware Networks

Nodes in socially aware networks (SANs) may act selfishly on individual bases due to resource constraints and socially selfish behavior arising from the social preferences of nodes. In response to such selfish behaviors exhibited by nodes, this paper proposes a social trust confirmation-based selfish node detection algorithm (STCDA). This algorithm first utilizes a subjective forwarding willingness detection mechanism to discern selfishness. If a node’s energy is insufficient or its message rejection rate is too high—that is, the node cannot or is unwilling to forward messages—it indicates that the node is selfish. Otherwise, it is evaluated more thoroughly through the node’s social trust detection mechanisms. It calculates the social trust level of nodes based on the benefits of forwarding messages, thereby distinguishing between individually selfish nodes and socially selfish nodes in the network. If further evaluation is needed, the final judgment will be made using the message confirmation feedback detection mechanism. This checks the message information forwarded by nodes in the network. If nodes fail to forward messages after receiving them—excluding reasons such as message expiration or temporary insufficient cache space—it indicates that the nodes are selfish. Results from experimental simulations show that this algorithm performs better than traditional algorithms. Under conditions of 80% selfish nodes, a message TTL of 300 min, and 10 MB of cache space, it improves the message delivery rate by 5.87% and reduces the average delay by 6.2% compared to the existing comprehensive confirmation-based selfish node detection algorithm.

Frequency aware high-quality computer-generated holography via multilevel wavelet learning and channel attention.

Deep learning-based computer-generated holography offers significant advantages for real-time holographic displays. Most existing methods typically utilize convolutional neural networks (CNNs) as the basic framework for encoding phase-only holograms (POHs). However, recent studies have shown that CNNs suffer from spectral bias, resulting in insufficient learning of high-frequency components. Here, we propose a novel, to our knowledge, frequency aware network for generating high-quality POHs. A multilevel wavelet-based channel attention network (MW-CANet) is designed to address spectral bias. By employing multi-scale wavelet transformations, MW-CANet effectively captures both low- and high-frequency features independently, thus facilitating an enhanced representation of high-frequency information crucial for accurate phase inference. Furthermore, MW-CANet utilizes an attention mechanism to discern and allocate additional focus to critical high-frequency components. Simulations and optical experiments confirm the validity and feasibility of our method.

Enhancing the Minimum Awareness Failure Distance in V2X Communications: A Deep Reinforcement Learning Approach.

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle's ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL-JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety.

A pixel-level grasp detection method based on Efficient Grasp Aware Network

Abstract This work proposes a novel grasp detection method, the Efficient Grasp Aware Network (EGA-Net), for robotic visual grasp detection. Our method obtains semantic information for grasping through feature extraction. It efficiently obtains feature channel weights related to grasping tasks through the constructed ECA-ResNet module, which can smooth the network’s learning. Meanwhile, we use concatenation to obtain low-level features with rich spatial information. Our method inputs an RGB-D image and outputs the grasp poses and their quality score. The EGA-Net is trained and tested on the Cornell and Jacquard datasets, and we achieve 98.9% and 95.8% accuracy, respectively. The proposed method only takes 24 ms for real-time performance to process an RGB-D image. Moreover, our method achieved better results in the comparison experiment. In the real-world grasp experiments, we use a 6-degree of freedom (DOF) UR-5 robotic arm to demonstrate its robust grasping of unseen objects in various scenes. We also demonstrate that our model can successfully grasp different types of objects without any processing in advance. The experiment results validate our model’s exceptional robustness and generalization.

Frequency distribution-aware network based on discrete cosine transformation (DCT) for remote sensing image super resolution.

Single-image super-resolution technology based on deep learning is widely used in remote sensing. The non-local feature reflects the correlation information between different regions. Most neural networks extract various non-local information of images in the spatial domain but ignore the similarity characteristics of frequency distribution, which limits the performance of the algorithm. To solve this problem, we propose a frequency distribution aware network based on discrete cosine transformation for remote sensing image super-resolution. This network first proposes a frequency-aware module. This module can effectively extract the similarity characteristics of the frequency distribution between different regions by rearranging the frequency feature matrix of the image. A global frequency feature fusion module is also proposed. It can extract the non-local information of feature maps at different scales in the frequency domain with little computational cost. The experiments were on two commonly-used remote sensing datasets. The experimental results show that the proposed algorithm can effectively complete image reconstruction and performs better than some advanced super-resolution algorithms. The code is available at https://github.com/Liyszepc/FDANet.

Transient alteration of Awareness triggered by direct electrical stimulation of the brain

BackgroundAwareness is a state of consciousness that enables a subject to interact with the environment. Transient alteration of awareness (AA) is a disabling sign of many types of epileptic seizures. The brain mechanisms of awareness and its alteration are not well known. Objective/HypothesisTransient and isolated AA induced by electrical brain stimulation during a stereoelectroencephalography (SEEG) recording represents an ideal model for studying the associated modifications of functional connectivity and locating the hubs of awareness networks. MethodsWe investigated the SEEG signals-based brain functional connectivity (FC) changes vs background occurring during AA triggered by three thalamic and two insular stimulations in three patients explored by SEEG in the frame of presurgical evaluation for focal drug-resistant epilepsy. The results were compared to the stimulations of the same sites that did not induce clinical changes (negative stimulations). ResultsWe observed decreased node strength in the pulvinar, insula, and parietal associative cortices during the thalamic and insular stimulations that induced AA. The link strengths characterizing functional coupling between the thalamus and the insular, prefrontal, temporal, or parietal associative cortices were also decreased. In contrast, there was an increased synchronization between the precuneus and the temporal lateral cortex. These FC changes were absent during the negative stimulations. ConclusionOur study highlights the role of the pulvinar, insular, and parietal hubs in maintaining the awareness networks and paves the way for invasive or non-invasive neuromodulation protocols to reduce AA manifestations during epileptic seizures.

An explainable weakly supervised model for multi-disease detection and localization from thoracic X-rays

Thoracic diseases are a major source of mortality, often requiring diagnosis from plain chest X-rays. However, differentiating between complex conditions based on subtle radiographic patterns poses challenges even for experts. Recently, deep learning methods have shown promise in automating thoracic disease detection from chest radiographs. Many existing approaches focus on the diseased organs in the radiographs by utilizing spatial regions that contribute significantly to the model’s prediction. Expert radiologists, on the other hand, first identify the prominent region before determining whether those regions are abnormal or not. Therefore, incorporating localization information through deep learning models could result in significant improvements in automatic disease classification. Motivated by this, we have proposed a generalized weakly supervised Confidence-Aware Probabilistic Class Activation Map (CAPCAM) classification model that localizes anomalies for thoracic disease. The CAPCAM used CX-Ultranet as the backbone with the combination of Confidence Aware Network (CAN) and Anomaly Detection Network (ADN) without having any localization labeling. This learning from the backbone helps the model to utilize all components of the feature extracted and, therefore eliminating the need to train them individually reducing the time taken. We have experimentally shown that the proposed CAPCAM method sets a new state-of-the-art benchmark by achieving accuracy in terms of Intersection of bounding box (IoBB) in the range of 85 % - 94 %, and Dice scores in the range of 88 %-90 % for all thirteen diseases on two publicly available large-scale CXR datasets–NIH, Stanford and CheXpert. Testing across different noise levels and different levels of blurred level assessed real-world viability. We have also added a layer of explainability to show how the image is processed. This study demonstrates deep learning’s potential to augment radiologists’ decision-making by providing fast, accurate automated aids for thoracic disease diagnosis. The proposed CAPCAM model could be readily translatable to improve clinical workflows.

Intra-Pulse Modulation Recognition of Radar Signals Based on Efficient Cross-Scale Aware Network.

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time-frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time-frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios.

Teacher Reflection on their Agency for Change (TRAC) - A tool for school-based social network learning

This short paper introduces an on-line log for Teachers’ Reflection on their Agency for Change (TRAC) designed to collect data and provide automatic visual feedback on teachers’ social networks. The TRAC tool was developed within a research project 'Making SENse of Teacher Agency for Change with Social and Epistemic Network Analysis'. The project aimed to understand how teachers exercise a form of relational agency defined as a capacity to work purposefully with others within and beyond schools, to support all learners, especially those at risk of exclusion, underachievement or other forms of marginalisation. Teachers’ reaching out to others and mobilising their social networks is an essential part of such agency. The study used Social and Epistemic Network Analysis to examine simultaneously the structural properties of teachers' social networks and the nature of content that flows through them. It also provided network visualisations and feedback to facilitate teachers’ professional reflection on how they use their relational agency to build inclusive school communities. The aim was to support teachers’ professional development by raising awareness about their individual and school network properties and nature of interactions within these networks. Part of the project was the development of the TRAC software that enabled teachers who engaged with a web-based reflective log to receive automatic feedback on their individual networks, as well as on thematic school networks, which were presented by the researchers in school development sessions. Network awareness – one’s knowledge of the resources embedded in their social ties – empowers teachers to improve the social environments of their schools. Provided alongside with the summary of research-based features of social network properties (degrees, diversity and intensity of ties), network feedback was used to facilitate professional and school development towards building inclusive communities. During the project, the TRAC tool was tested in partnerships with teachers and other staff in two schools in Stockholm. The project also had a knowledge exchange component to support relational and collaborative learning resulting from engagement with the network feedback. Network feedback based on the data from these sites was used to make adjustments to the prototype feedback tool. This short paper discusses the implications for future uses with a view towards rolling out research-informed professional reflection to larger numbers of schools and teachers anywhere, through dissemination in network learning community.

Halal Awareness in Online Social Networks. How Consumers Percept Halal Cosmetics in Indonesia and Malaysia

Halal Awareness in Online Social Networks. How Consumers Percept Halal Cosmetics in Indonesia and Malaysia

Global information aware network with global interaction graph attention for infrared small target detection

AbstractDetecting small targets in infrared images is crucial for ground surveillance and air traffic control. However, distinguishing small infrared targets from similar backgrounds is challenging due to their lack of structural and textural characteristics. To address these challenges, this study proposes a novel global information‐aware network with global interaction graph attention (GIGA) for infrared small target detection. The GIGA consists of a global interaction layer (GILayer), graph attention weights (GAW), and a global relational learning (GRL) module. Specifically, the GILayer dynamically learns global inter‐pixel relationships of small target images by enhancing the dependencies between feature dimensions. The GAW component calculates pixel‐by‐pixel similarity across the entire feature map using graph attention mechanisms, while the GRL module retains critical similarity features in the feature extraction network, thereby facilitating small target detection. Additionally, the multi‐scale context fusion module utilises self‐attention and dilation convolution to complement richer feature details at different scales. Experimental results on both natural and synthetic datasets demonstrate the proposed method's superiority over other state‐of‐the‐art conventional and deep learning approaches in infrared small target detection.

The coupled awareness-epidemic dynamics with individualized self-initiated awareness in multiplex networks

The outbreak of an epidemic often stimulates the generation of public awareness about epidemic prevention. This heightened awareness encourages individuals to take proactive protective measures, thereby curbing the transmission of the epidemic. Previous research commonly adopts an assumption that each individual has the same probability of awakening self-protection awareness after infection. However, in the real-world process, different individuals may generate varying awareness responses due to the differences in the amount of information received. Therefore, in this study, we first propose a coupled awareness-epidemic spreading model, where the self-initiated awareness of each individual can be influenced by the number of aware neighbors. Subsequently, we develop a Micro Markov Chain Approach to analyze the proposed model and explore the effects of different dynamic and structural parameters on the coupled dynamics. Findings indicate that individual awareness awakening can effectively promote awareness diffusion within the proposed coupled dynamics and inhibit epidemic transmission. Moreover, the influence of awareness diffusion on epidemic transmission exhibits a metacritical point, from which the epidemic threshold increases with the increase in the awareness diffusion probability. The research findings also suggest that the increase in the average degree of virtual-contact networks can reduce the value of the metacritical point, while the change in the average degree of the physical-contact networks does not affect the metacritical point. Finally, we conduct extensive experiments on four real networks and obtain results consistent with the above conclusions. The systematic research findings of this study provide new insights for exploring the interaction between individual awareness and epidemic transmission in the real world.

KGCNA: Knowledge Graph Collaborative Neighbor Awareness Network for Recommendation

KGCNA: Knowledge Graph Collaborative Neighbor Awareness Network for Recommendation

A Resource-Based Dynamic Pricing and Forced Forwarding Incentive Algorithm in Socially Aware Networking

In socially aware networking, nodes typically behave selfishly due to resource constraints and social correlations, resulting in low network performance. To incentivize selfish nodes to actively participate in message forwarding, this paper proposes a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). Firstly, the algorithm introduces virtual currency as a transaction medium and then designs a pricing function based on factors such as the node’s resource status, participation contribution, location relevance, and social connectivity. It ensures that the forwarding service is transacted at a reasonable price through bargaining rules. Secondly, a forced forwarding strategy is implemented to compel selfish nodes, which are unwilling to participate in other nodes’ message forwarding, to forward a certain number of non-local messages. Meanwhile, in order to prevent nodes from discarding messages and to ensure successful forwarding to the destination, specific rules are used to allocate contribution values to nodes that successfully participate in message forwarding. Lastly, to avoid false quotation behavior, blockchain technology is employed. Transaction information is packaged into blocks and added to the blockchain after consensus validation by other nodes in the network, ensuring the transparency and immutability of transaction data. Simulation results indicate that compared with the existing incentive algorithms, this algorithm not only enhances message delivery probability but also effectively reduces average latency.

Differential Feature Awareness Network Within Antagonistic Learning for Infrared-Visible Object Detection

Differential Feature Awareness Network Within Antagonistic Learning for Infrared-Visible Object Detection

Graph-Enhanced Spatio-Temporal Interval Aware Network for Next POI Recommendation in Mobile Environment

Graph-Enhanced Spatio-Temporal Interval Aware Network for Next POI Recommendation in Mobile Environment