Semantic Constraints Research Articles

An encoding of abstract dialectical frameworks into higher-order logic

Abstract An approach for encoding abstract dialectical frameworks and their semantics into classical higher-order logic is presented. Important properties and semantic relationships are formally encoded and proven using the proof assistant Isabelle/HOL. This approach allows for the computer-assisted analysis of abstract dialectical frameworks using automated and interactive reasoning tools within a uniform logic environment. Exemplary applications include the formal analysis and verification of meta-theoretical properties, and the generation of interpretations and extensions under specific semantic constraints.

Unsupervised Part Discovery via Dual Representation Alignment.

Object parts serve as crucial intermediate representations in various downstream tasks, but part-level representation learning still has not received as much attention as other vision tasks. Previous research has established that Vision Transformer can learn instance-level attention without labels, extracting high-quality instance-level representations for boosting downstream tasks. In this paper, we achieve unsupervised part-specific attention learning using a novel paradigm and further employ the part representations to improve part discovery performance. Specifically, paired images are generated from the same image with different geometric transformations, and multiple part representations are extracted from these paired images using a novel module, named PartFormer. These part representations from the paired images are then exchanged to improve geometric transformation invariance. Subsequently, the part representations are aligned with the feature map extracted by a feature map encoder, achieving high similarity with the pixel representations of the corresponding part regions and low similarity in irrelevant regions. Finally, the geometric and semantic constraints are applied to the part representations through the intermediate results in alignment for part-specific attention learning, encouraging the PartFormer to focus locally and the part representations to explicitly include the information of the corresponding parts. Moreover, the aligned part representations can further serve as a series of reliable detectors in the testing phase, predicting pixel masks for part discovery. Extensive experiments are carried out on four widely used datasets, and our results demonstrate that the proposed method achieves competitive performance and robustness due to its part-specific attention. The code will be released upon paper acceptance.

Image Generation of Animation Drawing Robot Based on Knowledge Distillation and Semantic Constraints

Image Generation of Animation Drawing Robot Based on Knowledge Distillation and Semantic Constraints

A Review on Semantic and Syntactic Constraints of VV Reduplication in Mandarin Chinese: Retrospections and Development

A Review on Semantic and Syntactic Constraints of VV Reduplication in Mandarin Chinese: Retrospections and Development

Attention-Enhanced Fusion of Structural and Functional MRI for Analyzing HIV-Associated Asymptomatic Neurocognitive Impairment.

Asymptomatic neurocognitive impairment (ANI) is a predominant form of cognitive impairment among individuals infected with human immunodeficiency virus (HIV). The current diagnostic criteria for ANI primarily rely on subjective clinical assessments, possibly leading to different interpretations among clinicians. Some recent studies leverage structural or functional MRI containing objective biomarkers for ANI analysis, offering clinicians companion diagnostic tools. However, they mainly utilize a single imaging modality, neglecting complementary information provided by structural and functional MRI. To this end, we propose an attention-enhanced structural and functional MRI fusion (ASFF) framework for HIV-associated ANI analysis. Specifically, the ASFF first extracts data-driven and human-engineered features from structural MRI, and also captures functional MRI features via a graph isomorphism network and Transformer. A mutual cross-attention fusion module is then designed to model the underlying relationship between structural and functional MRI. Additionally, a semantic inter-modality constraint is introduced to encourage consistency of multimodal features, facilitating effective feature fusion. Experimental results on 137 subjects from an HIV-associated ANI dataset with T1-weighted MRI and resting-state functional MRI show the effectiveness of our ASFF in ANI identification. Furthermore, our method can identify both modality-shared and modality-specific brain regions, which may advance our understanding of the structural and functional pathology underlying ANI.

CMBEE: A constraint-based multi-task learning framework for biomedical event extraction

Objective:Event extraction plays a crucial role in natural language processing. However, in the biomedical domain, the presence of nested events adds complexity to event extraction compared to single events, and these events usually have strong semantic relationships and constraints. Previous approaches ignored the binding connections between these complex nested events. This study aims to develop a unified framework based on event constraint information that jointly extract biomedical event triggers and arguments and enhance the performance of nested biomedical event extraction. Material and Methods:We propose a multi-task learning framework based on constraint information called CMBEE for the task of biomedical event extraction. The N-tuple form of event patterns is used to represent the constrained information, which is integrated into role detection and event type classification tasks. The framework use attention mechanism and gating mechanism to explore the fusion of multiple tuple information, as well as local and global constrained information fusion methods to dig further into the connections between events. Results:Experimental results demonstrate that our proposed method achieves the highest F1 score on a multilevel event extraction biomedical (MLEE) corpus and performs favorably on the biomedical natural language processing shared task 2013 Genia event corpus (GE 13). Conclusions:The experimental results indicate that modeling event patterns and constraints for multi-event extraction tasks is effective for complex biomedical event extraction. The fusion strategy proposed in this study, which incorporates different constraint information, helps to better express semantic information.

Semi-supervised feature selection with soft label learning

With the rapid increase of high-dimensional data mixed with labelled and unlabelled samples, the semi-supervised feature selection technique has received much attention in recent years. However, most existing approaches ignore the fuzziness of the data. Moreover, many feature selection methods need to measure the relationships among all samples, which is inefficient and difficult to be applied to large-scale data. To address the problems mentioned above, we propose an effective semi-supervised feature selection with the soft label learning (SFS-SLL) method in this paper. Specifically, we first learn initial soft labels based on the local distance between samples and clustering centers using an efficient fuzzy C-means clustering. We propose a supervised semantic constraint to exploit labelled and unlabelled data using manual labels as the soft label learning guidance. Then, we propose a simple yet effective sparse regression model which integrates soft label learning and feature selection into a unified framework. Finally, we derive an effective optimization strategy based on the alternating direction method of multipliers (ADMM) to iteratively solve the formulated problem. Experiment results on several benchmark datasets show a performance improvement on feature selection accuracy and efficiency over compared methods.

SemiPSCN: Polarization Semantic Constraint Network for Semi-Supervised Segmentation in Large-Scale and Complex-Valued PolSAR Images

SemiPSCN: Polarization Semantic Constraint Network for Semi-Supervised Segmentation in Large-Scale and Complex-Valued PolSAR Images

A Maritime Target Location Method Using Geometric Semantic Constraints Based on Spaceborne GNSS-R

A Maritime Target Location Method Using Geometric Semantic Constraints Based on Spaceborne GNSS-R

Downhole Track Detection via Multi-dimensional Conditional Generative Adversarial Nets

Frequent mine disasters cause a large number of casualties and property losses. Autonomous driving is a fundamental measure for solving this problem, and track detection is one of the key technologies for computer vision to achieve downhole automatic driving. The track detection result based on the existing models lacks the semantic detail of the tracks and relies too much on visual postprocessing technology. Therefore, this paper proposes a track detection model based on the multi-dimensional conditional generative adversarial network. First, the generator is decomposed into global and local parts using a multi-granularity structure. Second, a multi-scale shared convolution structure is introduced into the discriminator to further guide the generator. In addition, this paper proposes a penalty mechanism based on Monte Carlo search to enhance the semantic constraints in the image generation process. Compared with the state-of-the-art semantic segmentation algorithms, extensive experiments on the downhole scene dataset demonstrate proposed model achieved the best results in terms of pixel accuracy, intersection-over-union (IOU) and the track detection accuracy. This paper provides a new idea for track line detection. In the future, the model can also be applied to other segmentation problems as well. Code and data will be shared.

English adjectives of similarity: A corpus study of their determiner/modifier status

This paper investigates the determiner/modifier distinction issue. As reference grammars tend to define determiners and modifiers very vaguely especially in neighbouring cases there is a need for more formal rule together with obligatory semantic constraint. The left-hand periphery of English noun phrases is of strict order: determination zone precedes the modification zone (from left to right), so determiners go before the adjectival string. As the determination zone precedes the modification one it is assumed that only modifiers can be preceded by adjectives. The adjectives of similarity are tested in this way. The corpus-based analysis results in fixing possible adjectives accompanying the adjectives of similarity on their left. It is claimed that no adjective of similarity can be considered as a determiner.

DOE: a dynamic object elimination scheme based on geometric and semantic constraints

In this paper, we propose a dynamic object elimination algorithm that combines semantic and geometric constraints to address the problem of visual SLAM being easily affected by dynamic feature points in dynamic environments. This issue leads to the degradation of localisation accuracy and robustness. Firstly, we employ a lightweight YOLO-Tiny network to enhance both detection accuracy and system speed. Secondly, we integrate the YOLO-Tiny network into the ORB-SLAM3 system to extract semantic information from the images and initiate the elimination of dynamic feature points. Subsequently, we augment this approach by incorporating geometric constraints between neighbouring frames to further eliminate dynamic feature points. Then, the former is supplemented by combining the geometric constraints between neighbouring frames to further eliminate dynamic feature points. Experiments on the TUM dataset demonstrate that the algorithm in this paper can improve the Relative Pose Error (RPE) by up to 95.12% and the Absolute Trajectory Error (ATE) by up to 99.01% in high dynamic sequences compared to ORB-SLAM3. The effectiveness of dynamic feature point elimination is evident, leading to significantly improved localisation accuracy.

Unleashing the full potential of hyperspectral imaging: Decoupled image and frequency-domain spatial–spectral framework

Hyperspectral image classification (HSIC) is a rapidly developing field that utilizes deep learning methods. However, the reliance on convolutional neural networks (CNNs) for spectral–spatial feature extraction presents certain limitations. Specifically, the use of the fixed-position convolutional kernels in CNNs hinders their ability to capture fully the spectral information around the spatial central pixel, thereby overlooking critical differences between features. To address this issue, a decoupled image- and frequency-domain spectral–spatial framework for HSIC was developed in this study. This method incorporates image- and frequency-domain-based multiscale learnable convolutional attention to refine the differentiating features of the different feature distributions. Additionally, a novel frequency-domain information enhancement module was designed to extract the structural shape and texture details under the semantic constraints of the frequency phase, complementing the image domain to improve the extracted feature maps. Furthermore, a simple and efficient hierarchical feature representation module was introduced to extract both local and global information effectively from the fused features. The experimental results obtained using three open datasets and a practical hyperspectral image of the Gaofen-5 satellite demonstrate that the proposed method outperforms other state-of-the-art HSIC methods.

Beyond top-k: knowledge reasoning for multi-answer temporal questions based on revalidation framework.

Answer sorting and filtering are two closely related steps for determining the answer to a question. Answer sorting is designed to produce an ordered list of scores based on Top-k and contextual criteria. Answer filtering optimizes the selection according to other criteria, such as the range of time constraints the user expects. However, the unclear number of answers and time constraints, as well as the high score of false positive results, indicate that the traditional sorting and selection methods cannot guarantee the quality of answers to multi-answer questions. Therefore, this study proposes MATQA, a component based on multi-answer temporal question reasoning, using a re-validation framework to convert the Top-k answer list output by the QA system into a clear number of answer combinations, and a new multi-answer based evaluation index is proposed for this output form. First, the highly correlated subgraph is selected by calculating the scores of the boot node and the related fact node. Second, the subgraph attention inference module is introduced to determine the initial answer with the highest probability. Finally, the alternative answers are clustered at the semantic level and the time constraint level. Meanwhile, the candidate answers with similar types and high scores but do not satisfy the semantic constraints or the time constraints are eliminated to ensure the number and accuracy of final answers. Experiments on the multi-answer TimeQuestions dataset demonstrate the effectiveness of the answer combinations output by MATQA.

Binomial expressions as a window on the phonological system of Turkish

The principles governing word order in binomial expressions have been investigated in a few languages, revealing the relevance of several interacting semantic and phonological constraints, some of them universal or near-universal while others appear to be more language-specific. This paper presents the results of a preliminary quantitative analysis of some constraints influencing word order in Turkish irreversible binomials. We found that semantic constraints referring to the semantic prominence and iconic order of the items of the binomial, a constraint placing loanwords in final position, suprasegmental phonological constraints referring to syllable count, presence of onsets and presence of codas, segmental phonological constraints referring to the sonority of consonants, vowel roundness and presence of an initial labial consonant, all influence word order in Turkish irreversible binomials. Furthermore, the evidence from the segmental constraints may also shed light on some aspects of the Turkish phonological system, i.e. the markedness of rounded vowels and labial consonants, the properties of the sonority scale in Turkish and the featural specification of Turkish /j/.

Linguistic Equivalence in Muslim-Idiom Translation

Muslim-idiom translations use a range of strategies to properly contextualise biblical messages and to engage their audience. Most of these are not new or unusual. The rendering of divine titles may be subject to various different kinds of legitimate equivalence, as well as semantic and social constraints, and these issues are part of biblical history. Similarly, euphemisms, fundamental to human interaction, have always been a part of biblical textual history and cannot be banned from modern translations. And the formulaic key expressions of Islam, closely paralleling those of the Bible, contribute valuably to the literary stock which can be used to achieve engagement. Concerns about these issues are ultimately a matter of trust in translators’ loyalty to the biblical source text.

Semantic constraints to represent common sense required in household actions for multimodal learning-from-observation robot

The learning-from-observation (LfO) paradigm allows a robot to learn how to perform actions by observing human actions. Previous research in top-down learning-from-observation has mainly focused on the industrial domain, which consists only of the real physical constraints between a manipulated tool and the robot’s working environment. To extend this paradigm to the household domain, which consists of imaginary constraints derived from human common sense, we introduce the idea of semantic constraints, which are represented similarly to the physical constraints by defining an imaginary contact with an imaginary environment. By studying the transitions between contact states under physical and semantic constraints, we derive a necessary and sufficient set of task representations that provides the upper bound of the possible task set. We then apply the task representations to analyze various actions in top-rated household YouTube videos and real home cooking recordings, classify frequently occurring constraint patterns into physical, semantic, and multi-step task groups, and determine a subset that covers standard household actions. Finally, we design and implement task models, corresponding to these task representations in the subset, with the necessary daemon functions to collect the necessary parameters to perform the corresponding household actions. Our results provide promising directions for incorporating common sense into the robot teaching literature.

MFTSC: A Semantically Constrained Method for Urban Building Height Estimation Using Multiple Source Images

The use of remote sensing imagery has significantly enhanced the efficiency of building extraction; however, the precise estimation of building height remains a formidable challenge. In light of ongoing advancements in computer vision, numerous techniques leveraging convolutional neural networks and Transformers have been applied to remote sensing imagery, yielding promising outcomes. Nevertheless, most existing approaches directly estimate height without considering the intrinsic relationship between semantic building segmentation and building height estimation. In this study, we present a unified architectural framework that integrates the tasks of building semantic segmentation and building height estimation. We introduce a Transformer model that systematically merges multi-level features with semantic constraints and leverages shallow spatial detail feature cues in the encoder. Our approach excels in both height estimation and semantic segmentation tasks. Specifically, the coefficient of determination (R2) in the height estimation task attains a remarkable 0.9671, with a root mean square error (RMSE) of 1.1733 m. The mean intersection over union (mIoU) for building semantic segmentation reaches 0.7855. These findings underscore the efficacy of multi-task learning by integrating semantic segmentation with height estimation, thereby enhancing the precision of height estimation.

Multi-view semantic enhancement model for few-shot knowledge graph completion

In recent years, few-shot knowledge graph completion (FKGC) has gained popularity as a solution to the long-tail distribution problem of real-world knowledge graphs (KGs). The previous knowledge graph completion (KGC) models obtain triple representation merely relying on the structure view, ignoring the valuable semantic knowledge. In this paper, we propose a multi-view framework for few-shot relation learning to address the issue. Specifically, based the structural information of the graph obtained using the structure view, we add the text view and the commonsense view. The text view employs text descriptions of entities and relations to obtain a richer semantic representation. The commonsense view performs high-quality negative sampling based on complex relations. Moreover, commonsense semantic constraints are invoked to suppress the overfitting caused by the complexity of the relation matrix. Extensive experiments show that our model outperforms state-of-the-art FKGC methods on the frequently-used benchmark datasets FB15k237-One and NELL-One.

A Knowledge-Guided Fusion Visualisation Method of Digital Twin Scenes for Mountain Highways

Informatization is an important trend in the field of mountain highway management, and the digital twin is an effective way to promote mountain highway information management due to the complex and diverse terrain of mountainous areas, the high complexity of mountainous road scene modeling and low visualisation efficiency. It is challenging to construct the digital twin scenarios efficiently for mountain highways. To solve this problem, this article proposes a knowledge-guided fusion expression method for digital twin scenes of mountain highways. First, we explore the expression features and interrelationships of mountain highway scenes to establish the knowledge graph of mountain highway scenes. Second, by utilizing scene knowledge to construct spatial semantic constraint rules, we achieve efficient fusion modeling of basic geographic scenes and dynamic and static ancillary facilities, thereby reducing the complexity of scene modeling. Finally, a multi-level visualisation publishing scheme is established to improve the efficiency of scene visualisation. On this basis, a prototype system is developed, and case experimental analysis is conducted to validate the research. The results of the experiment indicate that the suggested method can accomplish the fusion modelling of mountain highway scenes through knowledge guidance and semantic constraints. Moreover, the construction time for the model fusion is less than 5.7 ms; meanwhile, the dynamic drawing efficiency of the scene is maintained above 60 FPS. Thus, the construction of twinned scenes can be achieved quickly and efficiently, the effect of replicating reality with virtuality is accomplished, and the informatisation management capacity of mountain highways is enhanced.