Semantic Awareness Research Articles

Artificially Intelligent Vehicle-to-Grid Energy Management: A Semantic-Aware Framework Balancing Grid Demands and User Autonomy

As the adoption of electric vehicles increases, the challenge of managing bidirectional energy flow while ensuring grid stability and respecting user preferences becomes increasingly critical. This paper aims to develop an intelligent framework for vehicle-to-grid (V2G) energy management that balances grid demands with user autonomy. The research presents VESTA (vehicle energy sharing through artificial intelligence), featuring the semantic-aware vehicle access control (SEVAC) model for efficient and intelligent energy sharing. The methodology involves developing a comparative analysis framework, designing the SEVAC model, and implementing a proof-of-concept simulation. VESTA integrates advanced technologies, including artificial intelligence, blockchain, and edge computing, to provide a comprehensive solution for V2G management. SEVAC employs semantic awareness to prioritise critical vehicles, such as those used by emergency services, without compromising user autonomy. The proof-of-concept simulation demonstrates VESTA’s capability to handle complex V2G scenarios, showing a 15% improvement in energy distribution efficiency and a 20% reduction in response time compared to traditional systems under high grid demand conditions. The results highlight VESTA’s ability to balance grid demands with vehicle availability and user preferences, maintaining transparency and security through blockchain technology. Future work will focus on large-scale pilot studies, improving AI reliability, and developing robust privacy-preserving techniques.

Automatic rating method based on deep transfer learning for machine translation considering contextual semantic awareness

With the acceleration of globalization, machine translation (MT) plays an increasingly prominent role in cross-language communication. However, how to evaluate the quality of machine translation, especially considering the nuances in different semantic contexts, remains a challenge. This paper proposes an automatic scoring model for machine translation quality based on deep transfer learning, which aims to accurately perceive and evaluate the quality of translated texts in different semantic contexts. Firstly, a pre-trained deep neural network model is used to extract the semantic feature representation of the sentences in the source language and the target language, so as to capture the semantic information of the sentences. Then, deep transfer learning is used to map the semantic features of source language and target language into the shared feature space. By sharing feature space, an effective relationship is established between the semantic representations of two languages, so as to achieve cross-language quality evaluation. The experimental results show that the model has made significant progress in evaluating the quality of machine translation. Compared with traditional methods, this model can evaluate translation quality more accurately and has better generalization ability. The automatic scoring model of machine translation quality based on deep transfer learning not only improves the accuracy and efficiency of machine translation quality assessment, but also provides new ideas and methods for future research.

Multi-spectral image fusion for moving object detection

This study delves into the problem of moving object detection in infrared and visible images. While existing approaches primarily focus on single-task detection using single-spectral image data, such as thermal infrared or visible images, they often ignore the differences between different spectral images and the interconnectedness of related tasks such as image fusion and segmentation. To tackle these problems, we present a novel multi-spectral image fusion network with quality and semantic awareness for moving object detection (MOD), particularly in scenarios where ground truth labels for both infrared and visible images are unavailable. Our approach fuses multi-spectral images, incorporating additional subtasks in the image fusion to obtain content and quality perception of infrared and visible images. In addition, we design a novel residual global perception module (RGPM) and multi-spectral fusion loss, which can capture more hidden features and contextual information across various scales. This enhanced capability leads to more precise detection and tracking of moving objects, particularly in challenging situations involving occlusions and dynamic backgrounds. Compared with single-spectral moving object detection optimization, the hurdles of utilizing deep learning for multi-spectral image fusion, e.g., without ground truth labels and harmful noise, are significantly mitigated. Extensive quantitative and qualitative comparative experiments demonstrate its effectiveness, robustness, and superior performance compared to contemporary methods. Concisely, the proposed fusion representation learning has gained 44.2%,31.2%,36.3%,41.6%,5.3%,31.2%,76.2% on EI, SF, DF, AG, MI, SD and Nabf metrics compared with the best competitors.

Adaptive Graph Neural Network with Incremental Learning Mechanism for Knowledge Graph Reasoning

Knowledge graphs are extensively utilized in diverse fields such as search engines, recommendation systems, and dialogue systems, and knowledge graph reasoning plays an important role in the aforementioned domains. Graph neural networks demonstrate the capability to effectively capture and process the graph structure inherent in knowledge graphs, leveraging the relationships between nodes and edges to enable efficient reasoning. Current research on graph neural networks relies on predefined propagation paths. The models based on predefined propagation paths overlook the correlation between entities and query relations, limiting adaptability and scalability. In this paper, we propose an adaptive graph neural network with an incremental learning mechanism to search the adaptive propagation path in order to retain promising targets. In detail, the incremental learning mechanism is able to filter out unrelated entities in the propagation path by incorporating the node sampling technique to increase the learning efficiency of the model. In addition, the incremental learning mechanism can select semantically related entities, which promotes the capture of meaningful connections among different nodes in the knowledge graph. At the same time, we apply the parameter initialization module to accelerate the convergence and improve the robustness of the model. Experimental results on benchmark datasets demonstrate that the adaptive graph neural network with the incremental learning mechanism has excellent semantic awareness ability, which surpasses several excellent knowledge graph reasoning models.

Energy-efficient data collection in UAV-assisted semantic awareness IoT network

Semantic communication shows great potential in reducing data redundancy and improving data collection efficiency in the Internet of Things (IoT) networks. However, IoT devices have limited energy storage capacity, so it is not possible to transmit semantic information to cloud center all the time. To this end, we propose an energy-efficient uploading mechanism in the UAV-assisted semantic awareness data collection system. Specially, we minimize the maximum energy consumption of IoT devices via jointly optimizing user scheduling and UAV trajectory. Since the formulated optimization problem is non-convex, we propose an efficient iterative algorithm to obtain a feasible solution. As the number of iterations increases, the user min–max energy consumption gradually converges to a steady state. Simulation results show that the proposed algorithm has good convergence. In addition, the simulation results of our proposed scheme show that the system performance is greatly improved compared with the conventional bit information transmission and non-optimization schemes.

Examining How Semantics Influences Communication in the Classroom

This study explored how students’ cognition is shaped by their cultural background and the function of semantics in classroom communication. To examine open-ended questions, the researcher employed a qualitative technique called thematic analysis. Fifty participants from level 4, English Language Unit, Preparatory Studies Centre, the University of Technology and Applied Sciences, Salalah, the Sultanate of Oman involved in the study. Some important conclusions emerged from the analysis. Students first stressed the need for semantics in maintaining comprehension and preventing misconceptions. Second, students’ interpretations of meaning were impacted by their cultural origins, underscoring the importance of culturally sensitive instruction. Third, when it came to understanding semantic ideas, students favored interactive learning techniques like role-plays and conversations. Fourth, teaching semantics was not like teaching other language issues; to close the theory-practice gap, additional real-world examples were needed. Lastly, students thought that adding exercises to enhance semantic awareness would help them communicate more effectively. These results imply that using participatory, culturally aware teaching strategies might enhance students’ communication and semantic awareness in heterogeneous classrooms.

Rate splitting with semantics as a generalized multi-access framework for intelligent reflecting surfaces

The rapid advancement of modern communication technologies necessitates the development of generalized multi-access frameworks and the continuous implementation of rate splitting, augmented with semantic awareness. This trend, coupled with the mounting pressure on wireless services, underscores the need for intelligent approaches to radio signal propagation. In response to these challenges, intelligent reflecting surfaces (IRS) have garnered significant attention for their ability to control data transmission systems in a goal-oriented and dynamic manner. This innovation is largely attributed to equitable resource allocation and the dynamic enhancement of network performance. However, the integration of rate-splitting multi-access (RSMA) architecture with semantic considerations imposes stringent requirements on IRS platforms to ensure seamless connectivity and broad coverage for a diverse user base without interference. Semantic communications hinge on a knowledge base-a centralized repository of integrated information related to the transmitted data-which becomes critically important in multi-antenna scenarios. This article proposes a novel set of design strategies for RSMA-IRS systems, enabled by reconfigurable intelligent surface synergizing with semantic communication principles. An experimental analysis is presented, demonstrating the effectiveness of these design guidelines in the context of Beyond 5G/6G communication systems. The RSMA-IRS model, infused with semantic communication, offers a promising solution for future wireless networks. Performance evaluations of the proposed approach reveal that, despite an increase in the number of users, the delay in the RSMA-IRS framework incorporating semantics is 2.94% less than that of a RSMA-IRS system without semantic integration.

Global semantic-guided graph attention network for Siamese tracking with ranking loss

Most Siamese trackers based on graph attention models focus on the topology between nodes when extracting features. These approaches ignore contextual information regarding the overall structure of the target, resulting in a diminished capacity for target discrimination. In this paper, to resolve this issue, we propose a global semantic-guided graph attention network for Siamese tracking. Firstly, in order to balance the node constraints with the overall information of the target, a global semantic awareness module (GSA) is proposed. It utilizes the attention strategy to focus on the overall characteristics of the target, aiming to mine the relationship of the target context. Incorporating it into the semantic-guided graph attention network through cross-correlation operations can avoid the problem of focusing only the information between nodes without being able to effectively characterize the target. Secondly, to enhance the accuracy of single-point feature regression results, a novel extreme point feature aggregation module (EPA) is proposed. We obtain feature information of the extreme points on the bounding box and combine them with single-point features for the bounding box feature representation to improve the regression accuracy. Finally, to establish a connection or bridge between the classification and regression subnetworks, a categorical regression alignment network is designed. An IOU-guided ranking loss is introduced to align the classification confidence with the IOU of the corresponding positive sample localization prediction to solve the problem of misalignment of the classification branch with the regression branch effectively. Experiments conducted on five challenging benchmarks datasets, namely GOT-10K, OTB-100, LaSOT, UAV123, and TC128, demonstrate that our approach exhibits superior performance compared to other tracking methods in terms of tracking accuracy and execution efficiency.

G^2SAM: Graph-Based Global Semantic Awareness Method for Multimodal Sarcasm Detection

Multimodal sarcasm detection, aiming to detect the ironic sentiment within multimodal social data, has gained substantial popularity in both the natural language processing and computer vision communities. Recently, graph-based studies by drawing sentimental relations to detect multimodal sarcasm have made notable advancements. However, they have neglected exploiting graph-based global semantic congruity from existing instances to facilitate the prediction, which ultimately hinders the model's performance. In this paper, we introduce a new inference paradigm that leverages global graph-based semantic awareness to handle this task. Firstly, we construct fine-grained multimodal graphs for each instance and integrate them into semantic space to draw graph-based relations. During inference, we leverage global semantic congruity to retrieve k-nearest neighbor instances in semantic space as references for voting on the final prediction. To enhance the semantic correlation of representation in semantic space, we also introduce label-aware graph contrastive learning to further improve the performance. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance in multimodal sarcasm detection. The code will be available at https://github.com/upccpu/G2SAM.

SALSA: Semantically-Aware Latent Space Autoencoder

In deep learning for drug discovery, molecular representations are often based on sequences, known as SMILES, which allow for straightforward implementation of natural language processing methodologies, one being the sequence-to-sequence autoencoder. However, we observe that training an autoencoder solely on SMILES is insufficient to learn molecular representations that are semantically meaningful, where semantics are specified by the structural (graph-to-graph) similarities between molecules. We demonstrate by example that SMILES-based autoencoders may map structurally similar molecules to distant codes, resulting in an incoherent latent space that does not necessarily respect the semantic similarities between molecules. To address this shortcoming we propose Semantically-Aware Latent Space Autoencoder (SALSA) for molecular representations: a SMILES-based transformer autoencoder modified with a contrastive task aimed at learning graph-to-graph similarities between molecules. To accomplish this, we develop a novel dataset comprised of sets of structurally similar molecules and opt for a supervised contrastive loss that is able to incorporate full sets of positive samples. We evaluate semantic awareness of SALSA representations by comparing to its ablated counterparts, and show empirically that SALSA learns representations that maintain 1) structural awareness, 2) physicochemical awareness, 3) biological awareness, and 4) semantic continuity.

Fuzzy speech emotion recognition considering semantic awareness

Speech emotion recognition is of great significance in the industry such as social robots, health care, and intelligent education. Due to the obscurity of emotional expression in speech, most works on speech emotion recognition (SER) ignore the consistency of speech emotion recognition, leading to fuzzy expression and low accuracy in emotional recognition. In this paper, we propose a semantic aware speech emotion recognition model to alleviate this issue. Specifically, a speech feature extraction module based on CNN and Transformer is designed to extract local and global information from the speech. Moreover, a semantic embedding support module is proposed to use text semantic information as auxiliary information to assist the model in extracting emotional features of speech, and can effectively overcome the problem of low recognition rate caused by emotional ambiguity. In addition, the model uses a key-value pair attention mechanism to fuse the features, which makes the fusion of speech and text features preferable. In experiments on two benchmark corpora IEMOCAP and EMO-DB, the recognition rates of 74.3% and 72.5% were obtained under respectively, which show that the proposed model can significantly improve the accuracy of emotion recognition.

Towards explainable artificial intelligence in deep vision-based odometry

Visual Odometry (VO) is a crucial process for estimating camera motion in real-time based on visual information captured. The emergence of deep learning has significantly transformed VO and Explainable Artificial Intelligence (XAI) in Deep Vision-Based Odometry. This survey paper explores the latest advancements in VO facilitated by deep learning methods, focusing on explainability and interpretability. It provides an overview of state-of-the-art deep learning techniques and dissects each model into its elemental building blocks to understand their explainable and interpretable aspects. The survey also highlights research gaps in optical flow robustness, occlusion and dynamic objects, real-time processing, drift correction, semantic awareness, and sensor integration. The aim is to catalyze future innovations in deep learning-based VO and stimulate dialogue about potential directions for the next wave of research, emphasizing explainability and interpretability as integral components of advanced systems.

A Mood Semantic Awareness Model for Emotional Interactive Robots.

The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human-machine dialogue systems. In this study, the Icc_dialogue model is proposed to enhance the semantic awareness of moods for emotional interactive robots. Equipped with a voice interaction module, emotion calculation is conducted based on model responses, and rules for calculating users' degree of interest are formulated. By evaluating the degree of interest, the system can determine whether it should transition to a new topic to maintain the user's interest. This model can also address issues such as overly purposeful responses and rigid emotional expressions in generated replies. Simultaneously, this study explores topic continuation after answering a question, the construction of dialogue rounds, keyword counting, and the creation of a target text similarity matrix for each text in the dialogue dataset. The matrix is normalized, weights are assigned, and the final text score is calculated. In the text with the highest score, the content of dialogue continuation is determined by calculating a subsequent sentence with the highest similarity. This resolves the issue in which the conversational bot fails to continue dialogue on a topic after answering a question, instead waiting for the user to voluntarily provide more information, resulting in topic interruption. As described in the experimental section, both automatic and manual evaluations were conducted to validate the significant improvement in the mood semantic awareness model's performance in terms of dialogue quality and user experience.

Investigation of meaning and centrality relationship in the statues used in Çankırı urban landscape

Statue is the three-dimensional concrete form created by the designer with materials that he determines depending on his purpose. Statues in urban landscapes are elements of artistic, aesthetic and cultural importance in the life of societies. In this study, answers to the following two questions are sought: (1) Does the meaning of statues in urban landscapes reach the urbanite? (2) Are the location and characteristics of the placement sites of the statues a factor in this semantic awareness? In this context, first of all, general design principles within the scope of urban landscape design were discussed in this study. Then, 8 statues selected from Çankırı city center were photographed and classified according to their types of use (cultural, historical, traditional, aesthetic, and industrial). Then, the physical components of statues in their relations with urban landscapes, such as volume, form, material and placement, and the factors affecting the perception of societies of statues were examined within the scope of Gestalt theory perception theory and design principles. Finally, the relationships of the statues with the urban landscape were evaluated in terms of their centrality relations using the Graph Common software. As a result of the research, it was observed that the statues in Çankırı were generally not complicated, had symbolic qualities and appropriate preferences were made in the selection of statues. In addition, the statues reflect the historical and cultural structure of the city, but there is a need for sculptural works that will further support the city's identity. Although there are some errors in the location and size of the Ox cart Statue, Embrace Stone and Yaren statues, there are no major problems that cannot be corrected. As a result of the study, recommendations were put forward in the light of all these findings.

Analysis of the Phenomenon and Strategies of Reverse Transfer Between Chinese and English

The reverse side of transfer that is second language effect on first language has not been paid too much attention to since the concept of language transfer has been brought up in 1972. And according to Cooks multi-competence theory, it indeed exists and studying it would have a significant meaning to the personal development of bilinguals and multilinguals. Based on Cooks multi-competence theory, this paper discusses the positive and negative transfer of English to Chinese, mainly focusing on students multi-competence in English classrooms in China and giving some guiding advice to English teachers in China. It finds out that the good things of Chinese-English transfer are the improvement of semantic awareness, expressions and logic. Besides, there are some negative impacts on the aspects of sentence order and culture. According to the findings, four main suggestions to Chinese English teachers are proposed. They are regarding L1 and L2 as each others resources ,attaching importance to logical words, building confidence of bilinguals and paying more attention to communication than accuracy.

FineStyle: Semantic-Aware Fine-Grained Motion Style Transfer with Dual Interactive-Flow Fusion.

We present FineStyle, a novel framework for motion style transfer that generates expressive human animations with specific styles for virtual reality and vision fields. It incorporates semantic awareness, which improves motion representation and allows for precise and stylish animation generation. Existing methods for motion style transfer have all failed to consider the semantic meaning behind the motion, resulting in limited controls over the generated human animations. To improve, FineStyle introduces a new cross-modality fusion module called Dual Interactive-Flow Fusion (DIFF). As the first attempt, DIFF integrates motion style features and semantic flows, producing semantic-aware style codes for fine-grained motion style transfer. FineStyle uses an innovative two-stage semantic guidance approach that leverages semantic clues to enhance the discriminative power of both semantic and style features. At an early stage, a semantic-guided encoder introduces distinct semantic clues into the style flow. Then, at a fine stage, both flows are further fused interactively, selecting the matched and critical clues from both flows. Extensive experiments demonstrate that FineStyle outperforms state-of-the-art methods in visual quality and controllability. By considering the semantic meaning behind motion style patterns, FineStyle allows for more precise control over motion styles. Source code and model are available on https://github.com/XingliangJin/Fine-Style.git.

Scene Uyghur Recognition Based on Visual Prediction Enhancement.

Aiming at the problems of Uyghur oblique deformation, character adhesion and character similarity in scene images, this paper proposes a scene Uyghur recognition model with enhanced visual prediction. First, the content-aware correction network TPS++ is used to perform feature-level correction for skewed text. Then, ABINet is used as the basic recognition network, and the U-Net structure in the vision model is improved to aggregate horizontal features, suppress multiple activation phenomena, better describe the spatial characteristics of character positions, and alleviate the problem of character adhesion. Finally, a visual masking semantic awareness (VMSA) module is added to guide the vision model to consider the language information in the visual space by masking the corresponding visual features on the attention map to obtain more accurate visual prediction. This module can not only alleviate the correction load of the language model, but also distinguish similar characters using the language information. The effectiveness of the improved method is verified by ablation experiments, and the model is compared with common scene text recognition methods and scene Uyghur recognition methods on the self-built scene Uyghur dataset.

LogBASA: Log Anomaly Detection Based on System Behavior Analysis and Global Semantic Awareness

System log anomaly detection is important for ensuring stable system operation and achieving rapid fault diagnosis. System log sequences include data on the execution paths and time stamps of system tasks in addition to a large amount of semantic information, which enhances the reliability and effectiveness of anomaly detection. At the same time, considering the correlation between system log sequences can effectively improve fault diagnosis efficiency. However, the existing system log anomaly detection methods mostly consider only the sequence patterns or semantic information on the logs, so their anomaly detection results show a high rate of missed and false alarms. To solve these problems, this paper proposed an unsupervised log anomaly detection model (LogBASA) based on the system behavior analysis and global semantic awareness, aiming to decrease the leakage rate and increase the log sequence anomaly detection accuracy. First, a system log knowledge graph was constructed based on massive, unstructured, and multilevel system log data to represent log sequence patterns, which facilitates subsequent anomaly detection and localization. Then, a self-attention encoder-decoder transformer model was developed for log spatiotemporal association analysis. This model combines semantic mapping and spatiotemporal features of log sequences to analyze system behavior and log semantics in multiple dimensions. Furthermore, a system log anomaly detection method that combines adaptive spatial boundary delineation and sequence reconstruction objective functions was proposed. This method uses special words to characterize the log sequence states, delineates anomaly boundaries automatically, and reconstructs log sequences through unsupervised training for anomaly detection. Finally, the proposed method was verified by numerous experiments on three real datasets. The results indicate that the proposed method can achieve an accuracy rate of 99.3%, 95.1%, and 97.2% on HDFS, BGL, and Thunderbird datasets, which proves the effectiveness and superiority of the LogBASA model.

ROUGE-SEM: Better evaluation of summarization using ROUGE combined with semantics

With the development of pre-trained language models and large-scale datasets, automatic text summarization has attracted much attention from the community of natural language processing, but the progress of automatic summarization evaluation has stagnated. Although there have been efforts to improve automatic summarization evaluation, ROUGE has remained one of the most popular metrics for nearly 20 years due to its competitive evaluation performance. However, ROUGE is not perfect, there are studies have shown that it is suffering from inaccurate evaluation of abstractive summarization and limited diversity of generated summaries, both caused by lexical bias. To avoid the bias of lexical similarity, more and more meaningful embedding-based metrics have been proposed to evaluate summaries by measuring semantic similarity. Due to the challenge of accurately measuring semantic similarity, none of them can fully replace ROUGE as the default automatic evaluation toolkit for text summarization. To address the aforementioned problems, we propose a compromise evaluation framework (ROUGE-SEM) for improving ROUGE with semantic information, which compensates for the lack of semantic awareness through a semantic similarity module. According to the differences in semantic similarity and lexical similarity, summaries are classified into four categories for the first time, including good-summary, pearl-summary, glass-summary, and bad-summary. In particular, the back-translation technique is adopted to rewrite pearl-summary and glass-summary that are inaccurately evaluated by ROUGE to alleviate lexical bias. Through this pipeline framework, summaries are first classified by candidate summary classifier, then rewritten by categorized summary rewriter, and finally scored by rewritten summary scorer, which are efficiently evaluated in a manner consistent with human behavior. When measured using Pearson, Spearman, and Kendall rank coefficients, our proposal achieves comparable or higher correlations with human judgments than several state-of-the-art automatic summarization evaluation metrics in dimensions of coherence, consistency, fluency, and relevance. This also suggests that improving ROUGE with semantics is a promising direction for automatic summarization evaluation.

A Secure Lossless Redundancy Elimination Scheme With Semantic Awareness for Cloud-Assisted Health Systems

Due to the limited scalability and reliability of traditional storage systems, more and more medical data are stored in cloud. However, the cost of cloud-assisted health systems is skyrocketing. How to improve storage utilization and save resource cost for health systems has become urgent problems. Therefore, this study proposes a secure lossless redundancy elimination scheme with semantic awareness, called SLRE, to decrease storage overhead and improve the overall system performance. Compared with existing studies, when the locality of data is almost nonexistent or poor, SLRE can eliminate redundant data well by exploiting semantic properties of similarity and entropy rather than relying on the locality. Then, fine-grained post-deduplication delta compression based on proxy similarity discovers more similar blocks and improves storage efficiency. In addition, filtered Lempel–Ziv compression based on proxy entropy filters data with low redundancy to improve the overall performance. Importantly, in order to ensure the security of medical data after deredundancy, different auditing strategies for medical data with diverse characteristics are proposed to guarantee security and improve the auditing efficiency. The experimental results show that compared with the state-of-the-art methods, the overall compression ratio of SLRE is improved, and the system throughput and auditing efficiency are also improved.