Semantic Disparity Research Articles

SAR images change detection using proposed lamina attention-based noise-resistant UNet advance network (LANRUNet++)

In the past few years, change detection techniques using Convolutional Neural Networks (CNNs) and conventional methods have garnered considerable attention in satellite image analysis. However, both categories of existing approaches have their respective shortcomings. For instance, methods based on CNNs tend to overlook the interactions amongst multilayer convolutions, while traditional methods may contain errors in the initial classification stage that limit the optimization of network. To address these limitations, we propose a new attentionbased, noise-resistant advanced UNet model named as Lamina Attention-Based Noise-Resistant UNet Advance Network (LANRUNet++)for detecting changes in satellite imagery. UNet++ is a unique neural network structure composed of an encoder and decoder, that are linked by a sequence of dense convolutional blocks nested within each other. A core principle underlying the proposed LANRUNet++ is to effectively bridge the semantic disparity between the encoder and decoder feature maps prior to their fusion. We present a lamina attention mechanism within UNet++, which dynamically assigns weights to features extracted from various convolutional layers. This facilitates improved incorporation of spatial context information. Moreover, we added a loss function that is robust to noise and can effectively minimize the influence of inaccurate annotations. As a result, our model becomes less susceptible to errors in initial classification outcomes. Furthermore, we added a Pixel to Density Ratio Parameter that further reduces noisy labels, improving the robustness of the model.We conducted a comprehensive performance assessment of LANRUNet++ on SAR images and conducted a comparative analysis against a range of cutting-edge techniques currently available. The results of our experiments demonstrate that our proposed approach surpasses other existing methods in its ability to detect changes, achieving a higher level of accuracy with various metrics.

FALSE FRIENDS OF TRANSLATORS. UNDERSTANDING ENGLISH-GERMAN TRANSLATIONS

The translation of false friends between English and German languages presents a significant challenge due to semantic disparities and lexical ambiguities of these languages. In this article the analysis of the different types of false friends is given, and the semantic and stylistic meanings of them is examined in depth. It also provides the list of different types of English-German false friends.

CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation

The Transformer architecture has gained widespread acceptance in image segmentation. However, it sacrifices local feature details and necessitates extensive data for training, posing challenges to its integration into computer-aided medical image segmentation. To address the above challenges, we introduce CCFNet, a collaborative cross-fusion network, which continuously fuses a CNN and Transformer interactively to exploit context dependencies. In particular, when integrating CNN features into Transformer, the correlations between local and global tokens are adaptively fused through collaborative self-attention fusion to minimize the semantic disparity between these two types of features. When integrating Transformer features into the CNN, it uses the spatial feature injector to reduce the spatial information gap between features due to the asymmetry of the extracted features. In addition, CCFNet implements the parallel operation of Transformer and the CNN and independently encodes hierarchical global and local representations when effectively aggregating different features, which can preserve global representations and local features. The experimental findings from two public medical image segmentation datasets reveal that our approach exhibits competitive performance in comparison to current state-of-the-art methods.

Visual Clue Guidance and Consistency Matching Framework for Multimodal Named Entity Recognition

The goal of multimodal named entity recognition (MNER) is to detect entity spans in given image–text pairs and classify them into corresponding entity types. Despite the success of existing works that leverage cross-modal attention mechanisms to integrate textual and visual representations, we observe three key issues. Firstly, models are prone to misguidance when fusing unrelated text and images. Secondly, most existing visual features are not enhanced or filtered. Finally, due to the independent encoding strategies employed for text and images, a noticeable semantic gap exists between them. To address these challenges, we propose a framework called visual clue guidance and consistency matching (GMF). To tackle the first issue, we introduce a visual clue guidance (VCG) module designed to hierarchically extract visual information from multiple scales. This information is utilized as an injectable visual clue guidance sequence to steer text representations for error-insensitive prediction decisions. Furthermore, by incorporating a cross-scale attention (CSA) module, we successfully mitigate interference across scales, enhancing the image’s capability to capture details. To address the third issue of semantic disparity between text and images, we employ a consistency matching (CM) module based on the idea of multimodal contrastive learning, facilitating the collaborative learning of multimodal data. To validate the effectiveness of our proposed framework, we conducted comprehensive experimental studies, including extensive comparative experiments, ablation studies, and case studies, on two widely used benchmark datasets, demonstrating the efficacy of the framework.

Learning transferable cross-modality representations for few-shot hyperspectral and LiDAR collaborative classification

Hyperspectral image (HSI) classification, incorporating both spatial and spectral information, is a crucial topic in earth observation and land cover analysis. However, ground objects with similar spectral attributes are still the challenges for finer classifications. Recently, deep learning-based multimodality fusion provides promising solutions by exploiting LiDAR data with its geometric information to fuse with spectral attributes. However, the labor-intensive and time-consuming multimodality data annotation limits the performance of supervised deep learning technologies. How to address the semantic disparity between the LiDAR data and HSIs, and learning transferable representations for cross-scene classifications are still challenging. In this paper, we propose a multimodal fusion relational network with meta-learning (MFRN-ML) to solve these challenges. Specifically, the MFRN-ML incorporates the multimodal learning and few-shot learning (FSL) into a three-stage task-based learning framework to learn the transferable cross-modality representations for few-shot HSI and LiDAR collaborative classification. First, a multimodal fusion relational network, composed of a cross-modality feature fusion module and a relation learning module, is proposed to address the challenge of limited annotations in multimodal learning in a data-adaptive way. Then, a three-stage task-based learning framework can train the network to learn transferable representations with few labeled samples for cross-scene classification. We perform experiments on four multimodal datasets collected by different sensors. Compared with existing supervised, semi-supervised, and meta-learning methods, MFRN-ML attains state-of-the-art performances in few-shot tasks. Particularly, our method shows promising generalization ability on unseen categories across different domains.

Cross Language Information Retrieval based on Automatic Query Translation for Marathi Documents

Abstract: The current research article explores the realm of Cross Language Information Retrieval (CLIR) and its significance in the digital age. It addresses the challenges faced in CLIR, including lexical and semantic disparities, the scarcity of parallel corpora, cultural nuances, and more. The article discusses innovative solutions encompassing Machine Translation, Query Expansion, Cross-Lingual Word Embeddings, and Multilingual Information Retrieval Models to enhance CLIR's effectiveness. Furthermore, it sheds light on Information Retrieval Models, such as the Boolean Model, Vector Space Model (VSM), and Probabilistic Models, explaining their principles and applications. The study also presents experimental results highlighting the limitations of monolingual IR models and the effectiveness of crosslingual techniques, such as translation and query expansion, in improving CLIR, making it a valuable tool for accessing information across languages.

An Efficient Information Extraction Mechanism with Page Ranking and a Classification Strategy based on Similarity Learning of Web Text Documents

Users have recently had more access to information thanks to the growth of the www information system. In these situations, search engines have developed into an essential tool for consumers to find information in a big space. The difficulty of handling this wealth of knowledge grows more difficult every day. Although search engines are crucial for information gathering, many of the results they offer are not required by the user because they are ranked according on user string matches. As a result, there were semantic disparities between the terms used in the user inquiry and the importance of catch phrases in the results. The problem of grouping relevant information into categories of related topics hasn't been solved. A Ranking Based Similarity Learning Approach and SVM based classification frame work of web text to estimate the semantic comparison between words to improve extraction of information is proposed in the work. The results of the experiment suggest improvisation in order to obtain better results by retrieving more relevant results.

Semantic Disparity between Amr (Command) and Nahy (Negative Command) in the Noble Quran: An Interpretive:Contextual Study

Semantic Disparity between Amr (Command) and Nahy (Negative Command) in the Noble Quran: An Interpretive:Contextual Study

Enhancing Traceability Link Recovery with Fine-Grained Query Expansion Analysis

Requirement traceability links are an essential part of requirement management software and are a basic prerequisite for software artifact changes. The manual establishment of requirement traceability links is time-consuming. When faced with large projects, requirement managers spend a lot of time in establishing relationships from numerous requirements and codes. However, existing techniques for automatic requirement traceability link recovery are limited by the semantic disparity between natural language and programming language, resulting in many methods being less accurate. In this paper, we propose a fine-grained requirement-code traceability link recovery approach based on query expansion, which analyzes the semantic similarity between requirements and codes from a fine-grained perspective, and uses a query expansion technique to establish valid links that deviate from the query, so as to further improve the accuracy of traceability link recovery. Experiments showed that the approach proposed in this paper outperforms state-of-the-art unsupervised traceability link recovery methods, not only specifying the obvious advantages of fine-grained structure analysis for word embedding-based traceability link recovery, but also improving the accuracy of establishing requirement traceability links. The experimental results demonstrate the superiority of our approach.

Measuring semantic gap between user-generated content and product descriptions through compression comparison in e-commerce

The significance of user-generated content as a source for business intelligence and analytics has been on the rise since the inception of electronic commerce platforms and has been solidified in the wake of the pandemic due to the prominence of electronic commerce as a sales channel. The prevailing approach to harnessing unstructured data involves the utilization of Artificial Intelligence; however, there exist simpler alternatives capable of yielding valuable information. This article introduces a methodology grounded in information theory to quantify the semantic disparity between the consumer community and product descriptions.This disparity can result in potential misunderstandings in the dialogue among consumers, and incidental costs in the dialogue between consumers and vendors. One plausible explanation for this disparity is that the terminology employed by consumers may possess different meanings compared to that utilized by product description writers. Our methodology employs large corpora of consumer reviews and product descriptions to quantify this semantic disparity across multiple electronic commerce domains through the implementation of random word exchanges and compression. Furthermore, we utilize neural word embeddings to identify specific words exhibiting the greatest semantic drift between reviews and descriptions, thereby providing lexical examples of these gaps. Our findings indicate that lower levels of lexical-semantic gap are associated with better consumer satisfaction.

Action Nominal Constructions and Their Use in a Swedish COVID-19 Corpus

Swedish has two main types of action nominal constructions (ANCs), either compounding or phrasal (incorporating or nominal, in Koptjevskaja-Tamm 1993), which contain deverbal nouns in -ande or -(n)ing, along with dependent elements. This study investigates Swedish ANCs in use, based on a limited data set from a COVID-19 corpus. It adopts a lexeme-based approach, where deverbal nouns (simplex action nominals), whether including -ande or ‑(n)ing, are morphological constructs and contain a verb lexeme, specified for a list of arguments and an event structure. The study focusses on two questions: to what extent do the two suffixes occur in the compounding versus phrasal ANCs, and to what extent and by what means are the arguments of the verbal base expressed in the compounding versus phrasal ANCs. The data of 328 ANCs (type) show that compounding ANCs predominate over phrasal ANCs, whether combined with -ande or -(n)ing, and that -(n)ing is much more frequent than -ande. As for the expression of arguments, the compounding ANCs contain equally often a modifier (similar to NN-compounding) or an Arg2/internal argument, whereas the phrasal ANCs more often express the Arg2. The Arg1/external argument tends to be expressed as a preposed genitive in one out of ten cases in both compounding and phrasal ANCs. Within a lexeme-based account, we can speak of a gradual transition from morphological constructions, more typically primary compounds (noun-noun) and less typically deverbal compounds (such as compounding ANCs), over to syntactic constructions (such as phrasal ANCs). In conclusion, the study suggests that compounding ANCs are the preferred option in contemporary Swedish for both suffixes, with compounding ANCs, in particular those with -(n)ing, bordering on NN-compounding. Given that both phrasal and compounding ANCs with -ande or -(n)ing occur, albeit to different extents, competition between the morphological and syntactic patterns remains unresolved.

Derivation of Nominals Corresponding to Object Experiencer Verbs in roz- in Polish

This paper contributes to the discussion concerning the status of Kimian states as a relatively new ontological category in language. We argue here that a class of psychological nouns in Polish represents Kimian states and it is semantically and morpho-syntactically related to structures with passive participles, which also show features of Kimian states. The discussion has a bearing on the Aspect Preservation Hypothesis (Fábregas and Marín, “State Nouns”, “The Role”), since the data seem to constitute a problem for this Hypothesis if the nominals are analysed as derived from active verbal forms or as sharing structure with them. The derivation of roz- psychological nominals from either Object Experiencer Verbs, Subject Experiencer Verbs or bare roots presents, in our opinion, insurmountable problems, resulting from the distributional facts concerning cognate nominal and verbal forms, as well as morpho-syntactic and semantic disparity between active forms of psychological verbs (both with Subject Experiencer and Object Experiencer arguments) and cognate roz- nominals. The nominals show Kimian state properties and contain morphological elements shared with the stative passive structures headed by passive participles of psychological verbs. The stable distributional correspondence between the nominals and their cognate passive participles supports derivational links between these two classes of forms. At the same time no such correspondence is observed with Subject Experiencer Verbs. Our analysis proceeds along the lines of Fábregas and Marín’s (“The Role”) analysis of Slovenian data, but counter some derivational claims voiced by Rozwadowska and Iordăchioaia (“The Root”, “Categorization”). In the light of the proposed analysis, the derivation of roz- nominals ceases to be problematic for the Aspect Preservation Hypothesis.

Formula omitted]-net: Dual supervised medical image segmentation with multi-dimensional self-attention and diversely-connected multi-scale convolution

Although U-Net and its variants have achieved some great successes in medical image segmentation tasks, their segmentation performances for small objects are still unsatisfactory. Therefore, in this work, a new deep model, ω-Net, is proposed to achieve more accurate medical image segmentations. The advancements of ω-Net are mainly threefold: First, it incorporates an additional expansive path into U-Net to import an extra supervision signal and obtain a more effective and robust image segmentation by dual supervision. Then, a multi-dimensional self-attention mechanism is further developed to highlight salient features and suppress irrelevant ones consecutively in both spatial and channel dimensions. Finally, to reduce semantic disparity between the feature maps of the contracting and expansive paths, we further propose to integrate diversely-connected multi-scale convolution blocks into the skip connections, where several multi-scale convolutional operations are connected in both series and parallel. Extensive experimental results on three abdominal CT segmentation tasks show that (i) ω-Net greatly outperforms the state-of-the-art image segmentation methods in medical image segmentation tasks; (ii) the proposed three advancements are all effective and essential for ω-Net to achieve the superior performances; and (iii) the proposed multi-dimensional self-attention (resp., diversely-connected multi-scale convolution) is more effective than the state-of-the-art attention mechanisms (resp., multi-scale solutions) for medical image segmentations. The code will be released online after this paper is formally accepted.

Multi-Task Learning Using Gradient Balance and Clipping with an Application in Joint Disparity Estimation and Semantic Segmentation

In this paper, we propose a novel multi-task learning (MTL) strategy from the gradient optimization view which enables automatically learning the optimal gradient from different tasks. In contrast with current multi-task learning methods which rely on careful network architecture adjustment or elaborate loss functions optimization, the proposed gradient-based MTL is simple and flexible. Specifically, we introduce a multi-task stochastic gradient descent optimization (MTSGD) to learn task-specific and shared representation in the deep neural network. In MTSGD, we decompose the total gradient into multiple task-specific sub-gradients and find the optimal sub-gradient via gradient balance and clipping operations. In this way, the learned network can satisfy the performance of specific task optimization while maintaining the shared representation. We take the joint learning of semantic segmentation and disparity estimation tasks as the exemplar to verify the effectiveness of the proposed method. Extensive experimental results on a large-scale dataset show that our proposed algorithm is superior to the baseline methods by a large margin. Meanwhile, we perform a series of ablation studies to have a deep analysis of gradient descent for MTL.

Residual Block Based Nested U-Type Architecture for Multi-Modal Brain Tumor Image Segmentation.

Multi-modal magnetic resonance imaging (MRI) segmentation of brain tumors is a hot topic in brain tumor processing research in recent years, which can make full use of the feature information of different modalities in MRI images, so that tumors can be segmented more effectively. In this article, convolutional neural networks (CNN) is used as a tool to improve the efficiency and effectiveness of segmentation. Based on this, Dense-ResUNet, a multi-modal MRI image segmentation model for brain tumors is created. The Dense-ResUNet consists of a series of nested dense convolutional blocks and a U-Net shaped model with residual connections. The nested dense convolutional blocks can bridge the semantic disparity between the feature maps of the encoder and decoder before fusion and make full use of different levels of features. The residual blocks and skip connection can extract pixel information from the image and skip the link to solve the traditional deep traditional CNN network problem. The experiment results show that our Dense-ResUNet can effectively help to extract the brain tumor and has great clinical research and application value.

Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder.

Anomaly detection has been applied in the various disease of medical practice, such as breast cancer, retinal, lung lesion, and skin disease. However, in real-world anomaly detection, there exist a large number of healthy samples, and but very few sick samples. To alleviate the problem of data imbalance in anomaly detection, this paper proposes an unsupervised learning method for deep anomaly detection based on an improved adversarial autoencoder, in which a module called chain of convolutional block (CCB) is employed instead of the conventional skip-connections used in adversarial autoencoder. Such CCB connections provide considerable advantages via direct connections, not only preserving both global and local information but also alleviating the problem of semantic disparity between the encoding features and the corresponding decoding features. The proposed method is thus able to capture the distribution of normal samples within both image space and latent vector space. By means of minimizing the reconstruction error within both spaces during training phase, higher reconstruction error during test phase is indicative of an anomaly. Our method is trained only on the healthy persons in order to learn the distribution of normal samples and can detect sick samples based on high deviation from the distribution of normality in an unsupervised way. Experimental results for multiple datasets from different fields demonstrate that the proposed method yields superior performance to state-of-the-art methods.

IRONI PEMILIHAN UMUM DI INDONESIA DALAM PUISI “KETIKA INDONESIA DIHORMATI DUNIA” KARYA TAUFIK ISMAIL

The events that occur in the environment are inseparable from the observations and works of writers. Likewise about general elections. Elections are routine events in a country that adopts a democratic system. Taufik Ismail, who is one of the most prolific poets in Indonesia, also noted this election event. This research takes one of Taufik Ismail's poems with the theme of the general election, namely When Indonesia is Honored by the World (KIDD). KIDD's poetry is examined by its ironic elements in shaping meaning. Characteristics and types of irony that is in it and how the irony builds the meaning desired by the poet. Characteristics of irony utterances found in KIDD are semantic disparities and the use of hyperbolic force. Semantic disparities are characterized by lexical contradictions, the juxtaposition of words containing conflicting components of meaning, and the use or insertion of words with contrasting tunings. The use of hyperbole is the most powerful element seen in KIDD. This seems to be a style that is typical of Taufik Ismail. Hyperbolic speech is characterized by a violation of the quantity and quality maxim. The study of the type of irony shows that the KIDD poem contains a type of verbal irony as part of the structure of the pronunciation of irony. The type also found in this study is the type of situational irony. These characteristics and types of irony are used to build criticism, satire, and ridicule towards the Indonesian people. Such criticism, satire, and derision are arranged in a comparison or contrast between the past and the present.

Cecimpedan: Semantic-cognitive Process on Balinese Children

Cecimpedan is a kind of riddle in Balinese language. Cecimpedan can be classified into two types, namely onomatope and associative cecimpedan. The classification is based on linear cognitive development with the linguistic competence of children. Onomatope cecimpedan is used by children who are in the preoperational cognitive developmental period whereas associative cecimpedan is used by children who are in formal operational period. The hierarchy of cognitive development motivates the difference of syntactic and semantic constructions within those two kinds of cecimpedan. The semantic process of onomatope cecimpedan involves the logic of inductive analogy that holds on the actual similarity between rhyme and sound resonance of a word. Rhyme and sound resonance are formed through abbreviation process by maintaining the final syllable of a word. It is in opposite with associative cecimpedan. This second type of cecimpedan was developed by using declarative analogy logic. It is constructed through lexical choices that can lead to semantic disparity. This semantic disparity becomes the core of associative cecimpedan. Therefore, each participant must be able to clarify the answer of associative cecimpedan logically and empirically.

Global Comparison Aggregation Services

Web aggregation has been available regionally for several years, but this service has not been offered globally. As an example, using multiple regional comparison aggregators, we analyze the global prices for a Sony camcorder, which differ by more than three times. We further explain that lack of global comparison aggregation services partially contribute to such huge price dispersion. We also discuss difficulties encountered in the manual integration of global web sources. Motivated by this example, we propose a context mediation architecture for global aggregation to address semantic disparities of global information sources. Global aggregation services can bring efficiency to the global market and can be useful for market research and other business uses.

Response class interference in STM

In this experiment, we investigated why the performance decline across a series of Brown-Peterson trials, using a constant short (30-sec) intertriai interval (ITI), is reversed by increasing the ITI on the subsequent trial. One interpretation is that the increased time serves as a marker that emphasizes the succeeding trial and its information, resulting in better recall. In the present experiment, we increased the ITI by presenting a word matrix to be read during the added time interval. For different groups the matrix was of the same semantic material as the memory triads, was quite similar, or was quite different. The amount of improvement on the next trial was a function of the degree of semantic disparity between the matrices and the memory triads. Mere presentation of the matrix was not sufficient to influence performance on the subsequent trial, but the semantic nature of the matrices did exert such an influence.