Context Of Information Processing Research Articles

GoalBERT: A Lightweight Named-Entity Recognition Model Based on Multiple Fusion

Named-Entity Recognition (NER) as a core task in Natural Language Processing (NLP) aims to automatically identify and classify specific types of entities from unstructured text. In recent years, the introduction of Transformer architecture and its derivative BERT model has pushed the performance of NER to unprecedented heights. However, these models often have high requirements for computational power and memory resources, making it difficult to train and deploy them on small computing platforms. Although ALBERT as a lightweight model uses parameter sharing and matrix decomposition strategies to reduce memory consumption to some extent consumption, it does not effectively reduce the computational load of the model. Additionally, due to its internal sharing mechanism, the model’s understanding ability of text is reduced leading to poor performance in named-entity recognition tasks. To address these challenges, this manuscript proposes an efficient lightweight model called GoalBERT. The model adopts multiple fusion technologies by integrating a lightweight and efficient BiGRU that excels at handling context into part of the Transformer’s self-attention layers. This reduces the high computational demand caused by stacking multiple self-attention layers while enhancing the model’s ability to process context information. To solve the problem of gradient disappearance and explosion during training, residual connections are added between core layers for more stable training and steady performance improvement. Experimental results show that GoalBERT demonstrates recognition accuracy comparable to standard models with accuracy surpassing ALBERT by 10% in multi-entity type scenarios. Furthermore, compared to standard models, GoalBERT reduces memory requirements by 200% and improves training speed by nearly 230%. Experimental results indicate that GoalBERT is a high-quality lightweight model.

Towards Frugal Industrial AI: a framework for the development of scalable and robust machine learning models in the shop floor

AbstractArtificial intelligence (AI) among other digital technologies promise to deliver the next level of process efficiency of manufacturing systems. Although these solutions such as machine learning (ML) based condition monitoring and quality inspection are becoming popular, these work under very limited conditions. Solutions do not scale-up in the real environment, where there is a mix of manufacturing equipment, where the quality and quantity of data available changes from machine to machine, or where the process changes, changing the distribution of data (i.e. concept drift). This is particularly challenging in highly reconfigurable and flexible environments. Having to develop machine learning models from scratch every single time is not cost-effective, time-consuming, requires expert knowledge that is typically not available in the manufacturing environment as well as can be challenging when data is not available in high volumes. Model robustness, reusability, adaptability and life cycle management are the keys to scale-up this technology in the manufacturing industry. In this work, a conceptual framework to enable simple and robust ML model development for the shop floor is introduced. Referred here as Frugal Industrial AI, the approach takes advantage of existing models and their context to build more robust ones in a data-efficient manner. Using a semantic knowledge base of how to construct these models for different manufacturing applications and semi-automating the development or reuse of solutions through semantic similarity, it is demonstrated how models can be developed in a more streamlined way. In addition, it is demonstrated how capturing process context information is important for the effective reuse of existing models through continual learning. This is key to building more robust ML solutions that can deal with real changing manufacturing environments, avoiding retraining from scratch as well as enabling the non-expert to use AI effectively on the shop floor.

Action Segmentation in the Brain: The Role of Object-Action Associations.

Motion information has been argued to be central to the subjective segmentation of observed actions. Concerning object-directed actions, object-associated action information might as well inform efficient action segmentation and prediction. The present study compared the segmentation and neural processing of object manipulations and equivalent dough ball manipulations to elucidate the effect of object-action associations. Behavioral data corroborated that objective relational changes in the form of (un-)touchings of objects, hand, and ground represent meaningful anchor points in subjective action segmentation rendering them objective marks of meaningful event boundaries. As expected, segmentation behavior became even more systematic for the weakly informative dough. fMRI data were modeled by critical subjective, and computer-vision-derived objective event boundaries. Whole-brain as well as planned ROI analyses showed that object information had significant effects on how the brain processes these boundaries. This was especially pronounced at untouchings, that is, events that announced the beginning of the upcoming action and might be the point where competing predictions are aligned with perceptual input to update the current action model. As expected, weak object-action associations at untouching events were accompanied by increased biological motion processing, whereas strong object-action associations came with an increased contextual associative information processing, as indicated by increased parahippocampal activity. Interestingly, anterior inferior parietal lobule activity increased for weak object-action associations at untouching events, presumably because of an unrestricted number of candidate actions for dough manipulation. Our findings offer new insights into the significance of objects for the segmentation of action.

Similar gaze behaviour during dialogue perception in congenitally deaf children with cochlear Implants and normal hearing children.

Perceptual and speech production abilities of children with cochlear implants (CIs) are usually tested by word and sentence repetition or naming tests. However, these tests are quite far apart from daily life linguistic contexts. Here, we describe a way of investigating the link between language comprehension and anticipatory verbal behaviour promoting the use of more complex listening situations. The setup consists in watching the audio-visual dialogue of two actors. Children's gaze switches from one speaker to the other serve as a proxy of their prediction abilities. Moreover, to better understand the basis and the impact of anticipatory behaviour, we also measured children's ability to understand the dialogue content, their speech perception and memory skills as well as their rhythmic skills, that also require temporal predictions. Importantly, we compared children with CI performances with those of an age-matched group of children with normal hearing (NH). While children with CI revealed poorer speech perception and verbal working memory abilities than NH children, there was no difference in gaze anticipatory behaviour. Interestingly, in children with CI only, we found a significant correlation between dialogue comprehension, perceptual skills and gaze anticipatory behaviour. Our results extend to a dialogue context of previous findings showing an absence of predictive deficits in children with CI. The current design seems an interesting avenue to provide an accurate and objective estimate of anticipatory language behaviour in a more ecological linguistic context also with young children. What is already known on the subject Children with cochlear implants seem to have difficulties extracting structure from and learning sequential input patterns, possibly due to signal degradation and auditory deprivation in the first years of life. They also seem to have a reduced use of contextual information and slow language processing among children with hearing loss. What this paper adds to existing knowledge Here we show that when adopting a rather complex linguistic context such as watching a dialogue of two individuals, children with cochlear implants are able to use the speech and language structure to anticipate gaze switches to the upcoming speaker. What are the clinical implications of this work? The present design seems an interesting avenue to provide an accurate and objective estimate of anticipatory behaviour in a more ecological and dynamic linguistic context. Importantly, this measure is implicit and it has been previously used with very young (normal-hearing) children, showing that they spontaneously make anticipatory gaze switches by age two. Thus, this approach may be of interest to refine the speech comprehension assessment at a rather early age after cochlear implantation where explicit behavioural tests are not always reliable and sensitive.

Neuromechanical proficiency in elite performance decision-making: An event-related potential (ERP) analysis

This research focuses intensively on the neural mechanisms of action selection responses in elite athletes during high-stakes decision-making. It emphasizes the neuromechanical dimensions of these processes, contrasting the responses of expert and novice players within a theoretical framework of neural information processing in high-performance contexts. Utilizing advanced EEG technologies, including event-related potential (ERP) analysis, the study captures a comprehensive view of both behavioral and neurophysiological data. The central aim is to unravel the intricate neural underpinnings that distinguish elite athletes in their decision-making strategies. Key findings highlight: (1) Enhanced accuracy and swifter reaction times in elite athletes during the action selection phase; (2) Significant neurophysiological differences, marked by pronounced N1 peak amplitudes with prolonged latencies, reduced P2 peak amplitudes with stable latencies, decreased P3 peak amplitudes with reduced latencies, and increased average PSW amplitudes. These discoveries significantly advance our understanding of the neural foundations of expert decision-making in high-performance sports. This study not only sheds light on the cognitive and neural dynamics of elite sports performance but also provides a foundation for developing training and performance enhancement techniques in various high-stakes domains.

MSSD-Net: Multi-Scale SAR Ship Detection Network

In recent years, the development of neural networks has significantly advanced their application in Synthetic Aperture Radar (SAR) ship target detection for maritime traffic control and ship management. However, traditional neural network architectures are often complex and resource intensive, making them unsuitable for deployment on artificial satellites. To address this issue, this paper proposes a lightweight neural network: the Multi-Scale SAR Ship Detection Network (MSSD-Net). Initially, the MobileOne network module is employed to construct the backbone network for feature extraction from SAR images. Subsequently, a Multi-Scale Coordinate Attention (MSCA) module is designed to enhance the network’s capability to process contextual information. This is followed by the integration of features across different scales using an FPN + PAN structure. Lastly, an Anchor-Free approach is utilized for the rapid detection of ship targets. To evaluate the performance of MSSD-Net, we conducted extensive experiments on the Synthetic Aperture Radar Ship Detection Dataset (SSDD) and SAR-Ship-Dataset. Our experimental results demonstrate that MSSD-Net achieves a mean average precision (mAP) of 98.02% on the SSDD while maintaining a compact model size of only 1.635 million parameters. This indicates that MSSD-Net effectively reduces model complexity without compromising its ability to achieve high accuracy in object detection tasks.

Are we always aggressive or only behind the wheel? Exploring differences and similarities through social information processing theory

Questions such as “Are people always aggressive?” or “Is it only inside the car?” have been studied in previous research. However, the focus is mostly on anger and anger expression and not on an interpersonal perspective. The current study aims to explore this gap in the research by analyzing aggressive driving behavior through the application of Social Information Processing Theory (SIP). For that purpose, the trait of aggression was compared with aggressive driving behavior and SIP patterns on and off the road. To achieve these goals, 562 Portuguese drivers completed a self-reported survey concerning trait aggression, aggressive driving behavior, and social information processing in various contexts. The results indicated that deficits in SIP positively correlate with aggressive behavior in daily and driving contexts. Secondly, the trait of aggression is positively correlated with aggressive driving behavior. Finally, SIP deficits in the daily context were also positively correlated with SIP deficits in the driving context. Nevertheless, significant differences were found between SIP variables in the daily context and the driving context. The results, implications, and limitations of the study are discussed.

Efficient multi-scale network for semantic segmentation of fine-resolution remotely sensed images

Abstract Semantic segmentation of remote sensing urban scene images has diverse practical applications, including land cover mapping, urban change detection, environmental protection, and economic evaluation. However, classical semantic segmentation networks encounter challenges such as inadequate utilization of multi-scale semantic information and imprecise edge target segmentation in high-resolution remote sensing images. In response, this article introduces an efficient multi-scale network (EMNet) tailored for semantic segmentation of common features in remote sensing images. To address these challenges, EMNet integrates several key components. Firstly, the efficient atrous spatial pyramid pooling module is employed to enhance the relevance of multi-scale targets, facilitating improved extraction and processing of context information across different scales. Secondly, the efficient multi-scale attention mechanism and multi-scale jump connections are utilized to fuse semantic features from various levels, thereby achieving precise segmentation boundaries and accurate position information. Finally, an encoder-decoder structure is incorporated to refine the segmentation results. The effectiveness of the proposed network is validated through experiments conducted on the publicly available DroneDeploy image dataset and Potsdam dataset. Results indicate that EMNet achieves impressive performance metrics, with mean intersection over union (MIoU), mean precision (MPrecision), and mean recall (MRecall) reaching 75.99%, 86.76%, and 85.07%, respectively. Comparative analysis demonstrates that the network proposed in this article outperforms current mainstream semantic segmentation networks on both the DroneDeploy and Potsdam dataset.

Dynamic Nonlinear Spatial Integrations on Encoding Contrasting Stimuli of Tectal Neurons.

Animals detect targets using a variety of visual cues, with the visual salience of these cues determining which environmental features receive priority attention and further processing. Surround modulation plays a crucial role in generating visual saliency, which has been extensively studied in avian tectal neurons. Recent work has reported that the suppression of tectal neurons induced by motion contrasting stimulus is stronger than that by luminance contrasting stimulus. However, the underlying mechanism remains poorly understood. In this study, we built a computational model (called Generalized Linear-Dynamic Modulation) which incorporates independent nonlinear tuning mechanisms for excitatory and inhibitory inputs. This model aims to describe how tectal neurons encode contrasting stimuli. The results showed that: (1) The dynamic nonlinear integration structure substantially improved the accuracy (significant difference (p < 0.001, paired t-test) in the goodness of fit between the two models) of the predicted responses to contrasting stimuli, verifying the nonlinear processing performed by tectal neurons. (2) The modulation difference between luminance and motion contrasting stimuli emerged from the predicted response by the full model but not by that with only excitatory synaptic input (spatial luminance: 89 ± 2.8% (GL_DM) vs. 87 ± 2.1% (GL_DMexc); motion contrasting stimuli: 87 ± 1.7% (GL_DM) vs. 83 ± 2.2% (GL_DMexc)). These results validate the proposed model and further suggest the role of dynamic nonlinear spatial integrations in contextual visual information processing, especially in spatial integration, which is important for object detection performed by birds.

Investigating the Effect of Contextual Cueing with Face Stimuli on Electrophysiological Measures in Younger and Older Adults.

Extracting repeated patterns from our surroundings plays a crucial role in contextualizing information, making predictions, and guiding our behavior implicitly. Previous research showed that contextual cueing enhances visual search performance in younger adults. In this study, we investigated whether contextual cueing could also improve older adults' performance and whether age-related differences in the neural processes underlying implicit contextual learning could be detected. Twenty-four younger and 25 older participants performed a visual search task with contextual cueing. Contextual information was generated using repeated face configurations alongside random new configurations. We measured RT difference between new and repeated configurations; ERPs to uncover the neural processes underlying contextual cueing for early (N2pc), intermediate (P3b), and late (r-LRP) processes; and multiscale entropy and spectral power density analyses to examine neural dynamics. Both younger and older adults showed similar contextual cueing benefits in their visual search efficiency at the behavioral level. In addition, they showed similar patterns regarding contextual information processing: Repeated face configurations evoked decreased finer timescale entropy (1-20 msec) and higher frequency band power (13-30 Hz) compared with new configurations. However, we detected age-related differences in ERPs: Younger, but not older adults, had larger N2pc and P3b components for repeated compared with new configurations. These results suggest that contextual cueing remains intact with aging. Although attention- and target-evaluation-related ERPs differed between the age groups, the neural dynamics of contextual learning were preserved with aging, as both age groups increasingly utilized more globally grouped representations for repeated face configurations during the learning process.

TransGEM: a molecule generation model based on Transformer with gene expression data.

It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. The code is available at https://github.com/hzauzqy/TransGEM.

ConGraph: Advanced Persistent Threat Detection Method Based on Provenance Graph Combined with Process Context in Cyber-Physical System Environment

With the wide use of Cyber-Physical Systems (CPS) in many applications, targets of advanced persistent threats (APTs) have been extended to the IoT and industrial control systems. Provenance graph analysis based on system audit logs has become a promising way for APT detection and investigation. However, we cannot afford to ignore that existing provenance-based APT detection systems lack the process–context information at system runtime, which seriously limits detection performance. In this paper, we proposed ConGraph, an approach for detecting APT attacks using provenance graphs combined with process context; we presented a module for collecting process context to detect APT attacks. This module collects file access behavior, network access behavior, and interactive relationship features of processes to enrich semantic information of the provenance graph. It was the first time that the provenance graph was combined with multiple process–context information to improve the detection performance of APT attacks. ConGraph extracts process activity features from the provenance graphs and submits the features to a CNN-BiLSTM model to detect underlying APT activities. Compared to some state-of-the-art models, our model raised the average precision rate, recall rate, and F-1 score by 13.12%, 25.61%, and 24.28%, respectively.

Dissimilarities of neural representations of extinction trials are associated with extinction learning performance and renewal level.

Renewal of extinguished responses is associated with higher activity in specific extinction-relevant brain regions, i.e., hippocampus (HC), inferior frontal gyrus (IFG), and ventromedial PFC (vmPFC). HC is involved in processing of context information, while IFG and vmPFC use such context information for selecting and deciding among competing response options. However, it is as yet unknown to what extent trials with changed versus unchanged outcome, or extinction trials that evoke renewal (i.e., extinction context differs from acquisition and test context: ABA trials) and trials that do not (i.e., same context in all phases: AAA trials) are represented differentially in extinction-relevant brain regions. In this study, we applied representational similarity analysis (RSA) to determine differences in neural representations of these trial types and their relationship to extinction error rates and renewal level. Overall, individuals with renewal (REN) and those without (NoREN) did not differ significantly in their discrimination levels between ABA and AAA extinction trials, with the exception of right posterior HC, where REN exhibited more pronounced context-related discrimination. In addition, higher dissimilarity of representations in bilateral posterior HC, as well as in several IFG regions, during extinction learning was linked to lower ABA renewal rates. Both REN and NoREN benefitted from prediction error feedback from ABA extinction errors for context- and outcome-related discrimination of trials in IFG, vmPFC, and HC, but only the NoREN group also benefitted from error feedback from AAA extinction errors. Thus, while in both groups the presence of a novel context supported formation of distinct representations, only in NoREN the expectancy violation of the surprising change of outcome alone had a similar effect. In addition, only in NoREN context-related discrimination was linked to error feedback in vmPFC. In summary, the findings show that context- and outcome-related discrimination of trials in HC, vmPFC, and IFG is linked to extinction learning errors, regardless of renewal propensity, and at the same time point towards differential context processing strategies in REN and NoREN. Moreover, better discrimination of context-related trials during extinction learning promotes less renewal during extinction recall, suggesting that renewal may be related to suboptimal context-related trial discrimination.

Dorsal CA1 lesions of the hippocampus impact mating tactics in prairie voles by shifting non-monogamous males' use of space to resemble monogamous males.

Alternative mating tactics within mating systems are characterized by discrete patterns of spatio-temporal overlap with same-and opposite-sex conspecifics and mating-relevant outcomes. Socially monogamous "residents" maintain relatively small home range sizes, have territories that almost exclusively overlap with their mating partners, and are more likely to produce offspring than non-bonded "wandering" conspecifics. Because mating tactics appear to be so closely tied to patterns of space use, differences in spatial cognitive abilities might differentially impact individual males' decisions to adopt a particular mating tactic and/or how efficient they are within their chosen mating tactic. Yet few studies have considered how the hippocampus, a brain region important for encoding cognitive maps and for processing contextual information, might impact how individuals adopt mating tactics or the spatio-temporal behaviors closely associated with them. We assessed the impact of lesions to the dorsal CA1 (dCA1) region of the hippocampus on male prairie vole space use, reproductive success, and mating tactics in semi-natural outdoor field conditions. Interestingly, dCA1 lesions did not impact the proportion of males that adopted resident or wandering mating tactics, and dCA1 lesions did not impact a male's ability to form a pair bond in the lab. In contrast, we found that lesioning the dCA1 shifted the home range size of reproductively successful and unsuccessful males. Furthermore, we found that patterns of space use among residents were unaffected by dCA1 lesions, whereas wanderers with dCA1 lesions showed pronounced reductions of their space use habits and resembled non-lesioned residents. Collectively, our study supports the hypothesis that wanderer male prairie voles rely on dCA1-mediated spatial cognition to navigate their world in a way that resident males do not. Such differences might have implications for how individuals efficiently attract and defend mates, obtain resources, defend territories, and outcompete rivals.

Remote Sensing Micro-Object Detection under Global and Local Attention Mechanism

With the rapid advancement of technology, satellite and drone technologies have had significant impacts on various fields, creating both opportunities and challenges. In areas like the military, urban planning, and environmental monitoring, the application of remote sensing technology is paramount. However, due to the unique characteristics of remote sensing images, such as high resolution, large-scale scenes, and small, densely packed targets, remote sensing object detection faces numerous technical challenges. Traditional detection methods are inadequate for effectively detecting small targets, rendering the accurate and efficient detection of objects in complex remote sensing images a pressing issue. Current detection techniques fall short in accurately detecting small targets compared to medium and large ones, primarily due to limited feature information, insufficient contextual data, and poor localization capabilities for small targets. In response, we propose an innovative detection method. Unlike previous approaches that often focused solely on either local or contextual information, we introduce a novel Global and Local Attention Mechanism (GAL), providing an in-depth modeling method for input images. Our method integrates fine-grained local feature analysis with global contextual information processing. The local attention concentrates on details and spatial relationships within local windows, enabling the model to recognize intricate details in complex images. Meanwhile, the global attention addresses the entire image’s global information, capturing overarching patterns and structures, thus enhancing the model’s high-level semantic understanding. Ultimately, a specific mechanism fuses local details with global context, allowing the model to consider both aspects for a more precise and comprehensive interpretation of images. Furthermore, we have developed a multi-head prediction module that leverages semantic information at various scales to capture the multi-scale characteristics of remote sensing targets. Adding decoupled prediction heads aims to improve the accuracy and robustness of target detection. Additionally, we have innovatively designed the Ziou loss function, an advanced loss calculation, to enhance the model’s precision in small target localization, thereby boosting its overall performance in small target detection. Experimental results on the Visdrone2019 and DOTA datasets demonstrate that our method significantly surpasses traditional methods in detecting small targets in remote sensing imagery.

Deeply Supervised Skin Lesions Diagnosis With Stage and Branch Attention.

Accurate and unbiased examinations of skin lesions are critical for the early diagnosis and treatment of skin diseases. Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment. Recent studies have reported that ensembled convolutional neural networks (CNNs) are practical to classify the images for early diagnosis of skin disorders. However, the practical use of these ensembled CNNs is limited as these networks are heavyweight and inadequate for processing contextual information. Although lightweight networks (e.g., MobileNetV3 and EfficientNet) were developed to achieve parameter reduction for implementing deep neural networks on mobile devices, insufficient depth of feature representation restricts the performance. To address the existing limitations, we develop a new lite and effective neural network, namely HierAttn. The HierAttn applies a novel deep supervision strategy to learn the local and global features by using multi-stage and multi-branch attention mechanisms with only one training loss. The efficacy of HierAttn was evaluated by using the dermoscopy images dataset ISIC2019 and smartphone photos dataset PAD-UFES-20 (PAD2020). The experimental results show that HierAttn achieves the best accuracy and area under the curve (AUC) among the state-of-the-art lightweight networks.

Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data

Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and processing diverse datasets from sensors, Internet of Things (IoT) devices, and other urban sources to create a comprehensive understanding of the current city context. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. The integration of real-time contextual information prediction and personalized outdoor activity suggestions showcases the potential for creating more engaging and user-centric Smart City ecosystems. This research attempts to provide personalized recommendations that are in line with users’ preferences, the state of the environment at the time, and other pertinent contextual factors by utilizing data from multiple sources, including IoT devices, mobile applications, and environmental sensors.

Dopaminergic D2-like receptor stimulation affects attention on contextual information and modulates BOLD activation of extinction-related brain areas

Contextual information is essential for learning and memory processes and plays a crucial role during the recall of extinction memory, and in the renewal effect, which is the context-dependent recovery of an extinguished response. The dopaminergic system is known to be involved in regulating attentional processes by shifting attention to novel and salient contextual cues. Higher dopamine levels are associated with a better recall of previously learned stimulus-outcome associations and enhanced encoding, as well as retrieval of contextual information which promotes renewal. In this fMRI study, we aimed to investigate the impact of processing contextual information and the influence of dopaminergic D2-like receptor activation on attention to contextual information during a predictive learning task as well as upon extinction learning, memory performance, and activity of extinction-related brain areas. A single oral dose of 1.25 mg bromocriptine or an identical-looking placebo was administered to the participants. We modified a predictive learning task that in previous studies reliably evoked a renewal effect, by increasing the complexity of contextual information. We analysed fixations and dwell on contextual cues by use of eye-tracking and correlated these with behavioural performance and BOLD activation of extinction-related brain areas. Our results indicate that the group with dopaminergic D2-like receptor stimulation had higher attention to task-relevant contextual information and greater/lower BOLD activation of brain regions associated with cognitive control during extinction learning and recall. Moreover, renewal responses were almost completely absent. Since this behavioural effect was observed for both treatment groups, we assume that this was due to the complexity of the altered task design.

Changes in fear-associated learning task brain activation over the COVID-19 pandemic period: a preliminary longitudinal analysis.

The COVID-19 pandemic has had profound impacts on people worldwide. Previous studies have shown that fear learning, extinction, recall, and contextual information processing involve the activation of emotion and sensory brain systems, which can be modified. However, it remains unclear whether brain functions associated with these processes have been altered over the pandemic period. We compared pre- and peri-pandemic brain activation during a fear-associated learning task (FALT) using previously collected data. The participants were divided into two groups: the pandemic group (n = 16), who completed a baseline FALT before the pandemic and repeated the task during the pandemic, and the non-pandemic group (n = 77), who completed both sessions before the pandemic began. Compared with the non-pandemic group, the pandemic group exhibited significant decreases in brain activation from baseline to follow-up assessments, including activation in the brainstem during early fear learning, the posterior thalamus/hippocampus during late extinction, and the occipital pole during late recall phases for contextual processing. Furthermore, activations associated with retrieving safety cues were reduced in the posterior cingulate, premotor, and calcarine cortices during the early recall phase, and activations associated with retrieving dangerous cues decreased in the occipital pole during the late recall phase. Additionally, correlations between decreased activation and elevated posttraumatic stress symptoms were observed. These findings suggest that activations associated with processing low arousal contextual information, safety cues, and extinguished fear cues decreased during the pandemic. These changes in brain activation may have contributed to the increase in mental health disturbances observed during this time.

The Ability to Use Contextual Information in Object and Scene Recognition in Patients with Mild Cognitive Impairment.

The ability to understand and make use of object-scene relationships are critical for object and scene recognition. The current study assessed whether patients with mild cognitive impairment (MCI), possibly in the preclinical phase of Alzheimer's disease, exhibited impairment in processing contextual information in scene and object recognition. In Experiment 1, subjects viewed images of foreground objects in either semantic consistent or inconsistent scenes under no time pressure, and they verbally reported the names of foreground objects and backgrounds. Experiment 2 replicated Experiment 1, except that subjects were required to name scene first. Experiment 3 examined object and scene recognition accuracy baselines, recognition difficulty, familiarity with objects/scenes, and object-scene consistency judgements. There were contextual consistency effects on scene recognition for MCI and healthy subjects, regardless of response sequence. Scenes were recognized more accurately under the consistent condition than the inconsistent condition. Additionally, MCI patients were more susceptible to incongruent contextual information, possibly due to inhibitory deficits or over-dependence on semantic knowledge. However, no significant differences between MCI and healthy subjects were observed in consistency judgement, recognition accuracy, recognition difficulty and familiarity rating, suggesting no significant impairment in object and scene knowledge among MCI subjects. The study indicates that MCI patients retain relatively intact contextual processing ability but may exhibit inhibitory deficits or over-reliance on semantic knowledge.