Integration Of Contextual Information Research Articles

S[formula omitted]T-Net: A novel electroencephalogram signals-oriented emotion recognition model

In this paper, a novel skipping spatial–spectral–temporal network (S3T-Net) is developed to handle intra-individual differences in electroencephalogram (EEG) signals for accurate, robust, and generalized emotion recognition. In particular, aiming at the 4D features extracted from the raw EEG signals, a multi-branch architecture is proposed to learn spatial–spectral cross-domain representations, which benefits enhancing the model generalization ability. Time dependency among different spatial–spectral features is further captured via a bi-directional long-short term memory module, which employs an attention mechanism to integrate context information. Moreover, a skip-change unit is designed to add another auxiliary pathway for updating model parameters, which alleviates the vanishing gradient problem in complex spatial–temporal network. Evaluation results show that the proposed S3T-Net outperforms other advanced models in terms of the emotion recognition accuracy, which yields an performance improvement of 0.23% , 0.13%, and 0.43% as compared to the sub-optimal model in three test scenes, respectively. In addition, the effectiveness and superiority of the key components of S3T-Net are demonstrated from various experiments. As a reliable and competent emotion recognition model, the proposed S3T-Net contributes to the development of intelligent sentiment analysis in human–computer interaction (HCI) realm.

Recent Trends in Machine and Deep Learning for Verbal and Non-verbal Emotion Detection

Abstract: Emotion recognition, both verbal and non-verbal, is a crucial component of artificial intelligence, psychology, and human-computer interaction. Emotion recognition is an integral component that significantly contributes to the improvement of communication and interaction. The research endeavors to conduct a thorough analysis and synthesis of the most recent developments in Deep Learning (DL) and Machine Learning (ML) techniques. Specifically, the study concentrates on the recognition of both verbal and non-verbal emotions. In contrast to previous research concentrated on verbal or non-verbal emotion detection separately, the study attempts to reconcile the gap between the two by demonstrating how ML and DL can be utilized effectively to detect emotions. The study also examines new methods, including multimodal data and integration of contextual information. Additionally, the research has examined the ethical implications and difficulties associated with emotion detection technologies. Findings have also revealed the wide-reaching implications for various sectors, including healthcare, education, customer service, and entertainment, where comprehending human emotions plays a crucial role in enhancing user experience and outcomes. In conclusion, the study provides invaluable knowledge to practitioners and researchers, which may facilitate the development of more advanced and accurate systems.

SPEECH RECOGNITION SYSTEM

Speech recognition technology has witnessed remarkable progress in recent years, fueled by advancements in machine learning, deep neural networks, and signal processing techniques. This paper presents a comprehensive review of the current state-of-the-art in speech recognition systems, highlighting key methodologies and breakthroughs that have contributed to their improved performance. The paper explores various aspects, including acoustic modeling, language modeling, and the integration of contextual information, shedding light on the challenges faced and innovative solutions proposed in the field. Furthermore, the paper discusses the impact of large-scale datasets and transfer learning on the robustness and adaptability of speech recognition models. It delves into recent developments in end-to-end models and their potential to simplify the architecture while enhancing accuracy. The integration of real-time and edge computing for speech recognition applications is also explored, emphasizing the implications for practical implementations in diverse domains such as healthcare, telecommunications, and smart devices. In addition to reviewing the current landscape, the paper provides insights into future prospects and emerging trends in speech recognition research. The role of multimodal approaches, incorporating visual and contextual cues, is discussed as a potential avenue for further improvement. Ethical considerations related to privacy and bias in speech recognition systems are also addressed, emphasizing the importance of responsible development and deployment. By synthesizing current research findings and anticipating future directions, this paper contributes to the evolving discourse on speech recognition technologies, providing a valuable resource for researchers, practitioners, and industry professionals in the field. Key Words: Real-time processing , Machine learning , Deep neural networks , Technology advancements , Contextual information , Large-scale datasets Transfer learning , End-to-end models , Real-time processing Edge computing , Multimodal approaches Ethical considerations , Privacy , Bias , Future prospects Research review.

DSG-GAN:A dual-stage-generator-based GAN for cross-modality synthesis from PET to CT

PET/CT devices typically use CT images for PET attenuation correction, leading to additional radiation exposure. Alternatively, in a standalone PET imaging system, attenuation and scatter correction cannot be performed due to the absence of CT images. Therefore, it is necessary to explore methods for generating pseudo-CT images from PET images. However, traditional PET-to-CT synthesis models encounter conflicts in multi-objective optimization, leading to disparities between synthetic and real images in overall structure and texture. To address this issue, we propose a staged image generation model. Firstly, we construct a dual-stage generator, which synthesizes the overall structure and texture details of images by decomposing optimization objectives and employing multiple loss functions constraints. Additionally, in each generator, we employ improved deep perceptual skip connections, which utilize cross-layer information interaction and deep perceptual selection to effectively and selectively leverage multi-level deep information and avoid interference from redundant information. Finally, we construct a context-aware local discriminator, which integrates context information and extracts local features to generate fine local details of images and reasonably maintain the overall coherence of the images. Experimental results demonstrate that our approach outperforms other methods, with SSIM, PSNR, and FID metrics reaching 0.8993, 29.6108, and 29.7489, respectively, achieving the state-of-the-art. Furthermore, we conduct visual experiments on the synthesized pseudo-CT images in terms of image structure and texture. The results indicate that the pseudo-CT images synthesized in this study are more similar to real CT images, providing accurate structure information for clinical disease analysis and lesion localization.

Composite Backbone Small Object Detection Based on Context and Multi-Scale Information with Attention Mechanism

Object detection has gained widespread application across various domains; nevertheless, small object detection still presents numerous challenges due to the inherent limitations of small objects, such as their limited resolution and susceptibility to interference from neighboring elements. To improve detection accuracy of small objects, this study presents a novel method that integrates context information, attention mechanism, and multi-scale information. First, to realize feature augmentation, a composite backbone network is employed which can jointly extract object features. On this basis, to efficiently incorporate context information and focus on key features, the composite dilated convolution and attention module (CDAM) is designed, consisting of a composite dilated convolution module (CDM) and convolutional block attention module (CBAM). Then, a feature elimination module (FEM) is introduced to reduce the feature proportion of medium and large objects on feature layers; the impact of neighboring objects on small object detection can thereby be mitigated. Experiments conducted on MS COCO validate the superior performance of the method compared with baseline detectors, while it yields an average enhancement of 0.8% in overall detection accuracy, with a notable enhancement of 2.7% in small object detection.

Chemogenetic inhibition of the ventral hippocampus but not its direct projection to the prelimbic cortex attenuates context-specific operant responding.

Previous work has demonstrated the importance of the prelimbic cortex (PL) in contextual control of operant behavior. However, the associated neural circuitry responsible for providing contextual information to the PL is not well understood. In Pavlovian fear conditioning the ventral hippocampus (vH) and its projection to the PL have been shown to be important in supporting the effects of context on learning. The present experiments used chemogenetic inhibition of the direct vH-PL projection or the vH to determine involvement in expression of context-specific operant behavior. Rats were injected with an inhibitory DREADD (hM4Di) or mCherry-only into the vH, and subsequently trained to perform a lever press response for a food pellet in a distinct context. The DREADD ligand clozapine-n-oxide (CNO) was then delivered directly into the PL (experiment 1) and then systemically (experiment 2) prior to tests of the response in the training context as well as an equally familiar but untrained context. vH (systemic CNO) but not vH-PL (intra-PL CNO) inhibition was found to attenuate operant responding in its acquisition context. A third experiment, using the same rats, showed that chemogenetic inhibition of vH also reduced Pavlovian contextual fear. The present results suggest that multisynapatic connections between the vH and PL may be responsible for integration of contextual information with operant behavior.

Semantic-specific and domain-general mechanisms for integration and update of contextual information.

Recent research has highlighted the importance of domain-general processes and brain regions for language and semantic cognition. Yet, this has been mainly observed in executively demanding tasks, leaving open the question of the contribution of domain-general processes to natural language and semantic cognition. Using fMRI, we investigated whether neural processes reflecting context integration and context update-two key aspects of naturalistic language and semantic processing-are domain-specific versus domain-general. Thus, we compared neural responses during the integration of contextual information across semantic and non-semantic tasks. Whole-brain results revealed both shared (left posterior-dorsal inferior frontal gyrus, left posterior inferior temporal gyrus, and left dorsal angular gyrus/intraparietal sulcus) and distinct (left anterior-ventral inferior frontal gyrus, left anterior ventral angular gyrus, left posterior middle temporal gyrus for semantic control only) regions involved in context integration and update. Furthermore, data-driven functional connectivity analysis clustered domain-specific versus domain-general brain regions into distinct but interacting functional neural networks. These results provide a first characterisation of the neural processes required for context-dependent integration during language processing along the domain-specificity dimension, and at the same time, they bring new insights into the role of left posterior lateral temporal cortex and left angular gyrus for semantic cognition.

Event Extraction With Dynamic Prefix Tuning and Relevance Retrieval

We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have several significant challenges, including using suboptimal prompts, static event type information, and the overwhelming number of irrelevant event types. In this paper, we propose a generative template-based method with dynamic prefixes and a relevance retrieval framework for event extraction ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GREE</small> ) by first integrating context information with type-specific prefixes to learn a context-specific prefix for each context, and then retrieving the relevant event types with an adaptive threshold. Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OneIE</small> on ACE 2005 and achieves the best performances on ERE. Additionally, our model is proven to be portable to new types of events effectively.

Weakly supervised human skin segmentation using guidance attention mechanisms

Human skin segmentation is a crucial task in computer vision and biometric systems, yet it poses several challenges such as variability in skin colour, pose, and illumination. This paper presents a robust data-driven skin segmentation method for a single image that addresses these challenges through the integration of contextual information and efficient network design. In addition to robustness and accuracy, the integration into real-time systems requires a careful balance between computational power, speed, and performance. The proposed method incorporates two attention modules, Body Attention and Skin Attention, that utilize contextual information to improve segmentation results. These modules draw attention to the desired areas, focusing on the body boundaries and skin pixels, respectively. Additionally, an efficient network architecture is employed in the encoder part to minimize computational power while retaining high performance. To handle the issue of noisy labels in skin datasets, the proposed method uses a weakly supervised training strategy, relying on the Skin Attention module. The results of this study demonstrate that the proposed method is comparable to, or outperforms, state-of-the-art methods on benchmark datasets.

Networked Sensing for Radiation Detection, Localization, and Tracking

The detection, identification, and localization of illicit radiological and nuclear material continue to be key components of nuclear non-proliferation and nuclear security efforts around the world. Networks of radiation detectors deployed at strategic locations in urban environments have the potential to provide continuous radiological/nuclear (R/N) surveillance and provide high probabilities of intercepting threat sources. The integration of contextual information from sensors such as video, Lidar, and meteorological sensors can provide significantly enhanced situational awareness, and improved detection and localization performance through the fusion of the radiological and contextual data. In this work, we present details of our work to establish a city-scale multi-sensor network testbed for intelligent, adaptive R/N detection in urban environments, and develop new techniques that enable city-scale source detection, localization, and tracking.

SaGCN: Semantic-Aware Graph Calibration Network for Temporal Sentence Grounding

Temporal sentence grounding is a challenging task that aims to localize the semantic corresponding segment from the untrimmed video according to the given query language description. Existing methods either utilize a cross-modal matching architecture following a scan-and-rank pipeline or directly predict the probabilities of being the target boundary for each frame based on the entire video content. However, such methods are weak when some of the critical semantic concepts in the query are actually relevant to multiple video segments or the desired video segment contains a query-irrelevant scene due to ignoring query semantic concepts and local and global cross-modal context. In this paper, we propose a novel semantic-aware graph calibration network (SaGCN) to address the issues mentioned above. Specifically, we first introduce a semantic-aware local relational graph module to capture the inherent relationships among the specific semantic concept relevant local contextual information for fine-grained cross-modal information interactions. Then, a semantic-aware global relational graph module is derived for global contextual information integration and achieving cross-modal alignment. Finally, an attention-based calibration module is designed for eliminating the irrelevant information maintained in the visual modality under the guidance of query description. Extensive experiments verify the effectiveness of our proposed SaGCN on two widely used datasets (Charades-STA and TACoS), in which we achieve significant and consistent improvement compared to the state-of-the-art approaches.

Micro-expression Recognition Based on DCBAM-EfficientNet Model

To address the problems of low accuracy of existing deep learning-based micro-expression recognition models, numerous network parameters, and the difficulty of mobile deployment of micro-expression recognition models, this paper proposes DCBAM-EfficientNet, a micro-expression recognition model that uses the lightweight network EfficientNet as the backbone network and incorporates the attention module. The network can guarantee the accuracy of micro-expression recognition with relatively few network parameters. The attention mechanism allows the more expressive micro-expression features to be highlighted, and the CBAM attention is improved into a DCBAM model, where the large convolution kernel in the spatial attention module of CBAM is replaced by a dilated convolution with the same receptive field, reducing the network parameters while better preserving the spatial features of the image. The integration of the DCBAM model into the main structure of EfficientNet enables better integration of contextual information. Data enhancement is used to process the micro-expression dataset to decrease the occurrence of overfitting and improve the generalization ability of the model. The results demonstrate that the optimized model DCBAM-EfficientNet can effectively promote the recognition accuracy of micro-expressions, significantly reduce the quantity and volume of model parameters, and provide a reference for the deployment of mobile micro-expression recognition models.

Cloud Detection From High-Resolution Remote Sensing Images Based on Convolutional Neural Networks With Geographic Features and Contextual Information

The diversity and complexity of the subsurface, as well as the similarity of cloud features to those of highlighted surface objects (especially snowy areas), are the main reasons for the current limitations in cloud detection accuracy. This letter proposes a semantic segmentation network fusing geographic features with contextual information for cloud detection named GCI_CD. We design three modules: geographical-cloud information fusion module (to distinguish between clouds and snow), separable ResNet with CSAM encoder (to enhance channel and spatial association information extraction), contextual information integration module (to extract multi-scale features). To the best of our knowledge, this is the first time that geographic information has been fused in a cloud detection method. The validity of our proposed method is demonstrated using a high-resolution GF-1 dataset containing the near-infrared band and dividing the snow-covered and non-snow-covered areas. The experimental results show that GCI_CD has a better performance not only in snow-covered regions but also in the non-snow-covered regions. Especially for snow-covered images, the IOU of GCI_CD’s cloud detection results are improved by 5% on average compared to other cloud detection methods.

A novel MCF-Net: Multi-level context fusion network for 2D medical image segmentation

Medical image segmentation is a crucial step in the clinical applications for diagnosis and analysis of some diseases. U-Net-based convolution neural networks have achieved impressive performance in medical image segmentation tasks. However, the multi-level contextual information integration capability and the feature extraction ability are often insufficient. In this paper, we present a novel multi-level context fusion network (MCF-Net) to improve the performance of U-Net on various segmentation tasks by designing three modules, hybrid attention-based residual atrous convolution (HARA) module, multi-scale feature memory (MSFM) module, and multi-receptive field fusion (MRFF) module, to fuse multi-scale contextual information. HARA module was proposed to effectively extract multi-receptive field features by combing atrous spatial pyramid pooling and attention mechanism. We further design the MSFM and MRFF modules to fuse features of different levels and effectively extract contextual information. The proposed MCF-Net was evaluated on the ISIC 2018, DRIVE, BUSI, and Kvasir-SEG datasets, which have challenging images of many sizes and widely varying anatomy. The experimental results show that MCF-Net is very competitive with other U-Net models, and it offers tremendous potential as a general-purpose deep learning model for 2D medical image segmentation.

FEA-Swin: Foreground Enhancement Attention Swin Transformer Network for Accurate UAV-Based Dense Object Detection.

UAV-based object detection has recently attracted a lot of attention due to its diverse applications. Most of the existing convolution neural network based object detection models can perform well in common object detection cases. However, due to the fact that objects in UAV images are spatially distributed in a very dense manner, these methods have limited performance for UAV-based object detection. In this paper, we propose a novel transformer-based object detection model to improve the accuracy of object detection in UAV images. To detect dense objects competently, an advanced foreground enhancement attention Swin Transformer (FEA-Swin) framework is designed by integrating context information into the original backbone of a Swin Transformer. Moreover, to avoid the loss of information of small objects, an improved weighted bidirectional feature pyramid network (BiFPN) is presented by designing the skip connection operation. The proposed method aggregates feature maps from four stages and keeps abundant information of small objects. Specifically, to balance the detection accuracy and efficiency, we introduce an efficient neck of the BiFPN network by removing a redundant network layer. Experimental results on both public datasets and a self-made dataset demonstrate the performance of our method compared to the state-of-the-art methods in terms of detection accuracy.

An Attention Encoder-Decoder Network Based on Generative Adversarial Network for Remote Sensing Image Dehazing

Remote sensing image dehazing is a difficult problem for its complex characteristics. It can be regarded as the preprocessing of high-level tasks of remote sensing images. To remove haze from the hazy remote sensing image, an encoder-decoder based on generative adversarial network is proposed. It first learns the low-frequency information of the image, and then learns the high-frequency information of the image. The skip connection is also added in the network to avoid losing information. To further improve the ability of learning more useful information, a multi-scale attention module is proposed. Meanwhile, a CBlock module is also designed to extract more feature information. It can capture different size of receptive fields. In order to reduce the computational pressure of the network, a distillation module is used in the network. Inspired by multi-scale network, an enhance module is designed and introduced it in the end of the network to further improve the dehazing ability of the network by integrating context information on multi-scale. We compared with five methods and our proposed method on RICE dataset. Experimental results show that our method achieves the best effect, both qualitatively and quantitatively.

Towards Better Representation of Context Into Recommender Systems

Context-aware recommender systems (CARSs) are attracting more and more attention from both the academic community and from industry. Users' contextual situations (e.g., location, time, companion, etc.) which can influence their ratings on items, are taken into consideration. Therefore, more accurate and personalized recommendations can be generated. The integration of contextual information in recommender systems to better model users' preferences under different contextual situations is a key research topic. In this paper, the authors propose a new method for representing contextual situations in recommender systems based on the influence of contextual conditions on ratings using Pearson Correlation Coefficient. The authors show the effectiveness of the proposed method compared to state-of-art methods by experiments on three different datasets widely used in CARSs research community.

Saliency determines the integration of contextual information into stimulus-response episodes.

When humans perform a task, it has been shown that elements of this task, like stimulus (e.g., target and distractor) and response, are bound together into a common episodic representation called stimulus–response episode (or event file). Recently, the context, a completely task-irrelevant stimulus, was found to be integrated into an episode as well. However, instead of being bound directly with the response in a binary fashion, the context modulates the binary binding between the distractor and response. This finding raises the questions of whether the context can also enter into a binary binding with the response, and if so, what determines the way of its integration. In order to resolve these questions, saliency of the context was manipulated in three experiments by changing the loudness (Experiment 1) and emotional valence (Experiment 2A and 2B) of the context. All experiments implemented the four-alternative auditory negative priming paradigm introduced by Mayr and Buchner (2006, Journal of Experimental Psychology: Human Perception and Performance, 32[4], 932–943). Results showed that the integration of context changed as a function of its saliency level. Specifically, the context of low saliency was not bound at all, the context of moderate saliency modulated the binary binding between the distractor and response, whereas the context of high saliency entered into a binary binding with the response. The current results extend a previous finding by Hommel (2004, Trends in Cognitive Sciences, 8[11], 494–500) that there is a saliency threshold which determines whether a stimulus is bound or not, by suggesting that a second threshold determines the specific structure (i.e., binary vs. configural) of the resulting binding.

A dual-branch balance saliency model based on discriminative feature for fabric defect detection

PurposeThe purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.Design/methodology/approachThis paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.FindingsTo evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.Originality/valueThe dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.

STCM-Net: A symmetrical one-stage network for temporal language localization in videos

The task of temporal language localization in the video is to locate a video segment through natural language description for an untrimmed video. Compared with the general video localization task, it is more flexible and complex, which can accurately locate various scenes described by any natural language without making video labels in advance. It can be widely used for the field such as video retrieval and robot intelligent cognition. The main challenges of this task are the extraction of sentence semantics and the integration of contextual information in videos. Among them, contextual video integration can be optimized through the two-dimensional temporal adjacent network. Therefore, complete extraction of the potential information in the query sentence is necessary to solve the task more granularly. At the same time, we found a large amount of time-related information in the query sentence, which helps improve the localization accuracy. Thus, in this paper, we first define the time concept in a sentence and then propose a Sentence Time Concept Mining Network (STCM-Net), an symmetrical one-stage network. Can effectively extract the time concept contained in the query sentence, it can optimize the process of target localization and improve the localization performance. We also evaluate the proposed STCM-Net on three challenging public benchmarks: Charades-STA, ActivityNet Captions, and TACoS. Our STCM-Net gets encouraging improvements compared with the state-of-the-art approaches.