Representation Of Information Research Articles

Multimodal Consistency Suppression Factor for Fake News Detection

Recent multi-modal fake news detection methods often use the consistency between textual and visual contents to determine the truth or fake of a news information. Higher levels of textual-visual consistency typically lead to a greater likelihood of classifying a news item as real. However, a critical observation reveals that creators of most fake news intentionally select images that align with the textual content, thereby enhancing the credibility of the news. Consequently, high consistency between textual and visual contents alone cannot guarantee the authenticity of the information. To address this problem, we introduce a novel approach termed Multimodal Consistency-based Suppression Factor to modulate the significance of textual-visual consistency in information assessment. When the textual-visual matching is high, this suppression factor reduces the influence of consistency during the judgment process. Moreover, we use Contrastive Language-Image Pre-training (CLIP) model to extract features and measure the consistency level between modalities to guide multimodal fusion. In addition, we also use a method of compressing and fusing modal information based on Variational Autoencoder (VAE) to reconstruct CLIP features, learning the shared representation of different modal information of CLIP. Finally, extensive experiments were conducted on three publicly datasets, Weibo, Twitter and Weibo21, and the results confirmed that our method outperformed the state-of-the-art methods in the field, and had 0.8%, 2.6% and 4.1% effect improvement on the accuracy rate.

DBAII-Net with multiscale feature aggregation and cross-modal attention for enhancing infant brain injury classification in MRI.

MRI is pivotal in diagnosing brain injuries in infants. However, the dynamic development of the brain introduces variability in infant MRI characteristics, posing challenges for MRI-based classification in this population. Furthermore, manual data selection in large-scale studies is labor-intensive, and existing algorithms often underperform with thick-slice MRI data. To enhance research efficiency and classification accuracy in large datasets, we propose an advanced classification model. We introduce the Dual-Branch Attention Information Interactive Neural Network (DBAII-Net), a cutting-edge model inspired by radiologists' use of multiple MRI sequences. DBAII-Net features two innovative modules: (1) the Convolutional Enhancement Module (CEM), which leverages advanced convolutional techniques to aggregate multi-scale features, significantly enhancing information representation; and (2) the Cross-Modal Attention Module (CMAM), which employs state-of-the-art attention mechanisms to fuse data across branches, dramatically improving positional and channel feature extraction. Performances (accuracy, sensitivity, specificity, area under the curve, etc.) of DBAII-Net were compared with eight benchmark models for brain MRI classification in infants aged 6 months to 2 years. Utilizing a self-constructed dataset of 240 thick-slice brain MRI scans (122 with brain injuries, 118 without), DBAII-Net demonstrated superior performance. On a test set of approximately 50 cases, DBAII-Net achieved average performance metrics of 92.53% accuracy, 90.20% sensitivity, 94.93% specificity, and an area under the curve (AUC) of 0.9603. Ablation studies confirmed the effectiveness of CEM and CMAM, with CMAM significantly boosting classification metrics. DBAII-Net with CEM and CMAM outperforms existing benchmarks in enhancing the precision of brain MRI classification in infants, significantly reducing manual effort in infant brain research. Our code is available at https://github.com/jiazhen4585/DBAII-Net.

WRF-HEATS coupling: Incorporating human behaviors and city topography into urban heat stress evaluation

Urban human thermal stress can be inaccurately estimated along with less-understood heat heterogeneity due to the absence of high-resolution meteorological information and realistic human behavior representation. To this end, we coupled a regional climate model (weather research and forecasting model, WRF) and a human energy balance model (human-environment adaptive thermal stress model, HEATS) to predict pedestrian's dynamic thermal stress at a neighborhood scale. The WRF-human coupling system resolves human-environment heat exchanges based on meteorological and topographical information with the consideration of dynamic human activities. The coupling system has been tested and utilized to study dynamic heat stress in a typical hot, humid, and mountainous city, Hong Kong. Our results revealed widespread heat heterogeneity with up to 7 °C difference in Physiological Subjective Temperature (PST) in the core urban area, and extreme heat exposure (up to 45 °C PST) in calm-wind zones at noon. Heat stress can be further aggregated considering realistic human behaviors such as extra clothing (e.g., protective facemask during pandemics) and physical exercise (e.g., walking along inclined terrain). Optimal-thermal-comfort routes have been designed and suggested based on the simulated neighborhood-scale heat stress map.

Information fusion in order-2 fuzzy environments: A matrix transformation perspective

Order-2 information granules, as a representation of multi-layer structured information, have recently rekindled discussions. It is usually generated by abstracting order-1 information granules. When the dependence relationship and corresponding membership function of the order-1 information granules (reference information granules) are known, Pedrycz et al. used a method based on gradient optimization to complete the aggregation of the order-2 information granules. In this paper, we discuss a more specific scenario: not only the dependencies between reference information granules are captured, but also the order-1 information granules can be expressed as specific fuzzy information distributions. For this, we propose a fusion scheme of order-2 fuzzy sets based on matrix transformation called CQRP. To our knowledge, it is the first method that completely integrates the structural information of the order-2 environment into the fusion of order-2 fuzzy sets. In the process, we also creatively proposed the attraction and exclusion of structural information, deepening the understanding of structural information. Through sufficient comparison and analysis, we prove that it makes fuller use of information in the order-2 environment and is more reasonable and effective in tasks such as classification and identification.

Registered Report Stage II: Decoding the category information from evoked potentials to visible and invisible visual objects

Previous studies that use decoding methods and EEG to investigate the neural representation of the category information of visual objects focused mainly on consciously processed visual objects. It remains unclear whether the category information of unconsciously processed visual objects can be decoded and whether the decoding performance is different for consciously and unconsciously processed visual objects. The present study compared the neural decoding of the animacy category of visible and invisible visual objects via EEG and decoding methods. The results revealed that the animacy of visible visual objects could be decoded above the chance level by the P200, N300, and N400, but not by the early N/P100. However, the animacy of invisible visual objects could not be decoded above the chance level by neither early nor late ERP components. The decoding accuracy was greater for visible visual objects than that for invisible visual objects for the P200, N300 and N400. These results suggested that access to animacy category information for visual objects requires conscious processing.

Contraband detection algorithm for X‐ray security inspection images based on global semantic enhancement

AbstractAiming at the problem of low detection accuracy caused by overlapping occlusion and noise disturbance, a contraband detection algorithm for X‐ray security inspection images based on global semantic enhancement is proposed to achieve accurate contraband target detection by enhancing global semantic information. First, the disturbance suppression module is used to weaken the noise disturbance at different positions by local suppression and aggregate finer detail information. Then, the parallel cascade search module is used to capture long‐range dependencies and strengthen the representation of global semantic information, which helps the model identify contraband under overlapping occlusion. Finally, the contribution of different features is adaptively adjusted through the feature‐weighted fusion module, which promotes the effective fusion of multi‐scale features and improves the accuracy of model detection. The method in this article has been extensively evaluated and experimented on three mainstream benchmark datasets: SIXray, OPIXray, and PIDray. The mAPs of three datasets reach 93.5%, 91.9%, and 85.9%, respectively. The experimental results fully demonstrate that the method in this article has better performance compared with the latest method, which can meet the practical application requirements of real‐time target detection.

E2TNet: Efficient enhancement Transformer network for hyperspectral image classification

Recently, Convolutional Transformer-based models have become popular in hyperspectral image (HSI) classification tasks and gained competitive classification performance. However, some Convolutional Transformer-based models fail to effectively mine the global correlations of coarse-grained and fine-grained features, which is adverse to recognizing the refined scale variation information of land-cover. The combination of convolution operations and multihead self-attention mechanisms also increases the computational cost, leading to low classification efficiency. In addition, shallow spectral–spatial features are directly input into the encoder, which inevitably incurs redundant spectral information. Therefore, this paper proposes an efficient enhancement Transformer network (E2TNet) for HSI classification. Specifically, this paper first designs a spectral–spatial feature fusion module to extract spectral and spatial features from HSI cubes and fuse them. Second, considering that redundant spectral information has a negative impact on classification performance, this paper designs a spectral–spatial feature weighted module to improve the feature representation of critical spectral information. Finally, to explore the global correlations of coarse-grained and fine-grained features and improve classification efficiency, an efficient multigranularity information fusion module is embedded in the encoder of E2TNet. The experiment is conducted on four benchmark hyperspectral datasets, and the experimental results demonstrate that the proposed E2TNet is better than several Convolutional Transformer-based classification models in terms of classification accuracy and classification efficiency.

Heterogeneous propagation graph convolution network for a recommendation system based on a knowledge graph

Recently, there has been a surge of interest in recommendation systems that leverage knowledge graphs, primarily because of their effectiveness in addressing sparsity and cold-start challenges inherent in collaborative filtering approaches. In most previous studies, researchers have focused on the way knowledge associations are encoded in knowledge graphs, but have not sufficiently highlighted the signals of collaboration that are implicit in the interaction between users and items. As a result, the learned embeddings do not provide a complete representation of the semantic information. In this paper, we describe a new model called a heterogeneous propagation graph convolution network for a recommendation system combined with a knowledge graph (HP-GCN). It adopts a heterogeneous propagation to generate user embedding representations, thereby combining encoded collaborative signals and auxiliary knowledge in knowledge graphs. Furthermore, we incorporate an attention mechanism to differentiate the contributions made by diverse neighbors as opposed to those made by users. Since most graph convolutions tend to suffer from over-smoothing when the number of convolutional layers increases, leading to insufficient utilization of high-order information, this paper uses an improved graph convolution strategy to generate item embeddings. This strategy has two different aggregation mechanisms embedding into different subgraphs, which can more fully utilize high-order information and mitigate the over-smoothing problem. Thus, we are able to efficiently prevent negative information originating from higher-order neighbors into the process of embedding learning. In extensive experiments, we applied HP-GCN to four large-scale real datasets for music, books, movies, and restaurants. The experimental outcomes revealed that HP-GCN generally surpassed the baseline methods in both recommendation accuracy and diversity, showing superior recommendation performance overall.

A hybrid-fuzzy-decision-making framework for digital technologies selection

In the era of Industry X.0, manufacturers are increasingly adopting digital technologies such as robotics, big data, mobile communications, and information technology to enhance productivity and profitability. However, navigating this digital revolution presents significant challenges, particularly in selecting the most suitable technologies amidst uncertainties and the lack of precise quantitative data. Fuzzy logic, initially introduced to manage vagueness and imprecision, has proven to be an essential tool in various domains for handling these uncertainties. However, as systems and environments become more complex, traditional fuzzy logic exhibits limitations in effectively representing and managing deeper levels of uncertainty. This realization has driven the development of several extensions to fuzzy logic, designed to address challenges associated with uncertainty representation. Yet, a central question remains: Which fuzzy logic extension is the most relevant for better dealing with uncertainty? This study introduces a hybrid-fuzzy decision-making framework for technology selection, which allows for more than one representation of fuzzy information. Using a multi-expert, multi-criteria decision-making (MCDM) strategy, the study seeks to support organizations in selecting the most suitable digital technology for their operations, particularly when precise quantitative data is unavailable, and decisions must rely on expert opinions. The suggested framework is designed to handle decision-makers' hesitancy, subjectivity, and ambiguity in the decision-making process. To showcase the practical application of the proposed methodology, a case study was conducted to select appropriate technologies in the Moroccan automotive sector. The results demonstrate that, regardless of the fuzzy logic extension used, digital automation and automatic identification consistently rank among the top technologies, underscoring their importance according to most experts. Our proposed approach is particularly effective in resolving conflicts between different fuzzy logic extensions, thereby facilitating the ranking of the remaining technologies. Notably, additive manufacturing ranked third, followed by the other technologies.

A phenotypic drug discovery approach by latent interaction in deep learning

Contemporary drug discovery paradigms rely heavily on binding assays about the bio-physicochemical processes. However, this dominant approach suffers from overlooked higher-order interactions arising from the intricacies of molecular mechanisms, such as those involving cis -regulatory elements. It introduces potential impairments and restrains the potential development of computational methods. To address this limitation, I developed a deep learning model that leverages an end-to-end approach, relying exclusively on therapeutic information about drugs. By transforming textual representations of drug and virus genetic information into high-dimensional latent representations, this method evades the challenges arising from insufficient information about binding specificities. Its strengths lie in its ability to implicitly consider complexities such as epistasis and chemical–genetic interactions, and to handle the pervasive challenge of data scarcity. Through various modeling skills and data augmentation techniques, the proposed model demonstrates outstanding performance in out-of-sample validations, even in scenarios with unknown complex interactions. Furthermore, the study highlights the importance of chemical diversity for model training. While the method showcases the feasibility of deep learning in data-scarce scenarios, it reveals a promising alternative for drug discovery in situations where knowledge of underlying mechanisms is limited.

Diversifying Collaborative Filtering via Graph Spreading Network and Selective Sampling.

Graph neural network (GNN) is a robust model for processing non-Euclidean data, such as graphs, by extracting structural information and learning high-level representations. GNN has achieved state-of-the-art recommendation performance on collaborative filtering (CF) for accuracy. Nevertheless, the diversity of the recommendations has not received good attention. Existing work using GNN for recommendation suffers from the accuracy-diversity dilemma, where slightly increases diversity while accuracy drops significantly. Furthermore, GNN-based recommendation models lack the flexibility to adapt to different scenarios' demands concerning the accuracy-diversity ratio of their recommendation lists. In this work, we endeavor to address the above problems from the perspective of aggregate diversity, which modifies the propagation rule and develops a new sampling strategy. We propose graph spreading network (GSN), a novel model that leverages only neighborhood aggregation for CF. Specifically, GSN learns user and item embeddings by propagating them over the graph structure, utilizing both diversity-oriented and accuracy-oriented aggregations. The final representations are obtained by taking the weighted sum of the embeddings learned at all layers. We also present a new sampling strategy that selects potentially accurate and diverse items as negative samples to assist model training. GSN effectively addresses the accuracy-diversity dilemma and achieves improved diversity while maintaining accuracy with the help of a selective sampler. Moreover, a hyper-parameter in GSN allows for adjustment of the accuracy-diversity ratio of recommendation lists to satisfy the diverse demands. Compared to the state-of-the-art model, GSN improved R @20 by 1.62%, N @20 by 0.67%, G @20 by 3.59%, and E @20 by 4.15% on average over three real-world datasets, verifying the effectiveness of our proposed model in diversifying overall collaborative recommendations.

Suppressing the Maintenance of Information in Working Memory Alters Long-term Memory Traces.

The sensory recruitment hypothesis conceptualizes information in working memory as being activated representations of information in long-term memory. Accordingly, changes made to an item in working memory would be expected to influence its subsequent retention. Here, we tested the hypothesis that suppressing information from working memory, which can reduce short-term access to that information, may also alter its long-term neural representation. We obtained fMRI data (n = 25; 13 female / 12 male participants) while participants completed a working memory removal task with scene images as stimuli, followed by a final surprise recognition test of the examined items. We applied a multivariate pattern analysis to the data to quantify the engagement of suppression on each trial, to track the contents of working memory during suppression, and to assess representational changes afterward. Our analysis confirms previous reports that suppression of information in working memory involves focused attention to target and remove unwanted information. Furthermore, our findings provide new evidence that even a single dose of suppression of an item in working memory can (if engaged with sufficient strength) produce lasting changes in its neural representation, particularly weakening the unique, item-specific features, which leads to forgetting. Our study sheds light on the underlying mechanisms that contribute to the suppression of unwanted thoughts and highlights the dynamic interplay between working memory and long-term memory.

Knowledge Representation Learning Method Based on Semantic Enhancement of External Information

Background: Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured information representation of triples or introducing only one additional kind of information, which has large limitations and reduces the representation efficiency. Objective: This study aims to combine entity description information and textual relationship description information with triadic structure information, and then use the linear mapping method to linearly transform the structure vector and text vector to obtain the joint representation vector. Methods: A knowledge representation learning (DRKRL) model that fuses external information for semantic enhancement is proposed, which combines entity descriptions and textual relations with a triadic structure. For entity descriptions, a vector representation is performed using a bi-directional long- and short-term memory network (Bi-LSTM) model and an attention mechanism. For the textual relations, a convolutional neural network is used to vectorially encode the relations between entities, and then an attention mechanism is used to obtain valuable information as complementary information to the triad. Results: Link prediction and triadic group classification experiments were conducted on the FB15K, FB15K-237, WN18, WN18RR, and NELL-995 datasets. Theoretical analysis and experimental results show that the DRKRL model proposed in this paper has higher accuracy and efficiency compared with existing models. Conclusion: Combining entity description information and textual relationship description information with triadic structure information can make the model have better performance and effectively improve the knowledge representation learning ability.

Development of an Apparatus of Imaginative Information Representation for Neuromorphic Devices

Development of an Apparatus of Imaginative Information Representation for Neuromorphic Devices

Properties of Cubic Fuzzy Competition Graphs with Applications

This study extends the concept of competition graphs to cubic fuzzy competition graphs by introducing additional variations including cubic fuzzy out-neighbourhoods, cubic fuzzy in-neighbourhoods, open neighbourhood cubic fuzzy graphs, closed neighbourhood cubic fuzzy graphs, cubic fuzzy (k) neighbourhood graphs and cubic fuzzy [k]-neighbourhood graphs. These variations provide further insights into the relationships and competition within the graph structure, each with its own defined characteristics and examples. These cubic fuzzy CMGs are further classified as cubic fuzzy k-competition graphs that show competition in the k th order between components, p -competition cubic fuzzy graphs that concentrate on competition in terms of membership degrees, and m -step cubic fuzzy competition graphs that analyze competition in terms of steps. Further, some related results about independent strong vertices and edges have been obtained for these cubic fuzzy competition graph classes. Finally, the proposed concept of cubic fuzzy competition graphs is supported by a numerical example. This example showcases how the framework of cubic fuzzy competition graphs can be practically applied to the predator-prey model to illustrate the representation and analysis of ambiguous information within the graph structures.

FDADNet: Detection of Surface Defects in Wood-Based Panels Based on Frequency Domain Transformation and Adaptive Dynamic Downsampling

The detection of surface defects on wood-based panels plays a crucial role in product quality control. However, due to the complex background and low contrast of defects in wood-based panel images, features extracted by traditional deep learning methods based on spatial domain processing often contain noise and blurred boundaries, which severely affects detection performance. To address these issues, we have proposed a wood-based panel surface defect detection method based on frequency domain transformation and adaptive dynamic downsampling (FDADNet). Specifically, we designed a Multi-axis Frequency Domain Weighted Information Representation Module (MFDW), which effectively decoupled the indistinguishable low-contrast defects from the background in the transform domain. Gaussian filtering was then employed to eliminate noise and blur between the defects and the background. Additionally, to tackle the issue of scale differences in defects that led to difficulties in accurate capture, we designed an Adaptive Dynamic Convolution (ADConv) module for downsampling. This method flexibly compressed and enhanced features, effectively improving the differentiation of the features of objects of varying scales in the transform space, and ultimately achieved effective defect detection. To compensate for the lack of data, we constructed a dataset of wood-based panel surface defects, WBP-DET. The experimental results showed that the proposed FDADNet effectively improved the detection performance of wood-based panel surface defects in complex scenarios, achieving a solid balance between efficiency and accuracy.

Fusion-driven fault diagnosis based on adaptive tuning feature mode decomposition and synergy graph enhanced transformer for bearings under noisy conditions

Bearing fault diagnosis is critical for maintaining the reliability of health monitoring in electromechanical systems. However, traditional feature extraction methods often struggle to accurately capture fault information in complex environments. This paper proposes an adaptive tuning feature mode decomposition (ATFMD) method, based on the intrinsic signal characteristics, to effectively extract robust features. ATFMD dynamically adjusts to complex fault signals, providing more representative feature inputs and pruning redundant features induced by noise. Additionally, given the limitations of single-dimensional feature domains in fully revealing fault information, this study constructs feature topology graphs by mapping spatial phase and time–frequency characteristics, offering a comprehensive representation of fault information. To achieve complementary fusion of fault information within the feature topological graph, this paper proposes the synergy graph enhanced transformer (SGET). SGET optimizes the fusion process by reinforcing feature interactions through its synergy graph representation module. Additionally, a hierarchical cross-attention mechanism is employed to modulate attention distribution across feature dimensions, enhancing the sensitivity to critical features during fusion. Experimental validation was conducted on two distinct rotating machinery transmission systems. The results demonstrate that the proposed method maintains exceptional robustness and generalization, even in the presence of severe noise and complex fault conditions. Compared to the leading methods, including MS-DGCNs, CapsFormer, TFT, and ConvFormer, the proposed method achieves notable accuracy improvements of 8.13 %, 8.71 %, 11.94 %, and 6.25 %, respectively, under challenging conditions.

Asymmetric Network Combining CNN and Transformer for Building Extraction from Remote Sensing Images.

The accurate extraction of buildings from remote sensing images is crucial in fields such as 3D urban planning, disaster detection, and military reconnaissance. In recent years, models based on Transformer have performed well in global information processing and contextual relationship modeling, but suffer from high computational costs and insufficient ability to capture local information. In contrast, convolutional neural networks (CNNs) are very effective in extracting local features, but have a limited ability to process global information. In this paper, an asymmetric network (CTANet), which combines the advantages of CNN and Transformer, is proposed to achieve efficient extraction of buildings. Specifically, CTANet employs ConvNeXt as an encoder to extract features and combines it with an efficient bilateral hybrid attention transformer (BHAFormer) which is designed as a decoder. The BHAFormer establishes global dependencies from both texture edge features and background information perspectives to extract buildings more accurately while maintaining a low computational cost. Additionally, the multiscale mixed attention mechanism module (MSM-AMM) is introduced to learn the multiscale semantic information and channel representations of the encoder features to reduce noise interference and compensate for the loss of information in the downsampling process. Experimental results show that the proposed model achieves the best F1-score (86.7%, 95.74%, and 90.52%) and IoU (76.52%, 91.84%, and 82.68%) compared to other state-of-the-art methods on the Massachusetts building dataset, the WHU building dataset, and the Inria aerial image labeling dataset.

New Media in Visual Communication Design and the Information Representation Skills of Art Students

This study examines the interplay between challenges and opportunities in the application of new media in visual communication design among art students. Through qualitative analysis of participant narratives and quantitative evaluations, the study identifies significant challenges related to adapting to new technologies, maintaining creativity, and integrating traditional cultural elements into modern frameworks. Key difficulties include resistance to change and gaps in technical skills, which hinder the effective use of new media tools and the incorporation of cultural symbols. Conversely, the study highlights several opportunities presented by new media. These include the flexibility to work beyond traditional constraints, a diverse array of technological tools that enrich the creative process, and the ability to preserve and promote traditional Chinese culture. New media’s dynamic and adaptable nature enables students to engage audiences more effectively and creatively while maintaining cultural relevance. The synthesis of challenges and opportunities reveals a complex landscape where the benefits of new media are counterbalanced by significant obstacles. Addressing these challenges through improved educational strategies and support systems is crucial for maximizing the advantages of new media. By bridging technical skill gaps and fostering adaptability, students can better utilize new media to enhance visual communication and cultural representation, ultimately leading to more impactful and meaningful creative outputs.

English-to-Hindi translation divergence study of English phrasal verbs

English phrasal verbs pose complex semantic interpretations and cause significant problems in their translation into the target language. The paper selects some frequent and highly polysemous English phrasal verbs listed with 10 or more senses in the English WordNet, collects sample English sentences from the British National Corpus and examines their Hindi translation equivalents in terms of the morpho-syntactic structure of Hindi verbs and the representation of semantic information. This study attempts to present the semantic equivalence in rendering the identical semantics in the verbal system of both languages under consideration. The study found that the most frequent and common way of realising the concepts represented by English phrasal verbs in Hindi is by incorporating the compound verb constructions in the Hindi language. Besides that, we observe multiple mapping patterns of English phrasal verbs in Hindi, including the simple Hindi verb (v), compound verb (v1-v2), and conjunct verb (noun/adjective/adverb-verb) constructions.