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Humans excel at learning and recognizing objects, swiftly adapting to new concepts with just a few samples. However, current studies in computer vision on few-shot learning have not yet achieved human performance in integrating prior knowledge during the learning process. Humans utilize a hierarchical structure of object categories based on past experiences to facilitate learning and classification. Therefore, we propose a method named n-Hierarchy SEmantic Guided Attention (nHi-SEGA) that acquires abstract superclasses. This allows the model to associate with and pay attention to different levels of objects utilizing semantics and visual features embedded in the class hierarchy (e.g., house finch-bird-animal, goldfish-fish-animal, rose-flower-plant), resembling human cognition. We constructed an nHi-Tree using WordNet and Glove tools and devised two methods to extract hierarchical semantic features, which were then fused with visual features to improve sample feature prototypes. [ABSTRACT FROM AUTHOR]

Background/Objectives: Children with Autism Spectrum Disorder (ASD) face challenges in social communication due to difficulties in considering context, processing information, and interpreting social cues. This study aims to explore the neural processes related to pragmatic language communication in children with ASD and address the research question of how functional brain connectivity operates during complex pragmatic language tasks. Methods: We examined differences in brain functional connectivity between children with ASD and typically developing peers while they engaged in video recordings of spoken language tasks. We focused on two types of speech acts: semantic and pragmatic. Results: Our results showed differences between groups during the pragmatic and semantic language processing, indicating more idiosyncratic connectivity in children with ASD in the Left Somatomotor and Left Limbic networks, suggesting that these networks play a role in task-dependent functional connectivity. Additionally, these functional differences were mainly localized to the left hemisphere. [ABSTRACT FROM AUTHOR]

Social media is a platform for sharing various hashtags, news, and posts within the community. Increasing text content and information to read and understand is difficult because of the vast definitions. At present, every business must analyze data in social media. Social media platforms are where people worldwide discuss and share social commentary. Automated text summarization is crucial for condensing lengthy contents into concise ones using learning concepts. The unstructured data in social media are significant phrases with support from sources for analyzing sentiments and extracting the importance of the content. Previously, all data were analyzed as related content similarity matches. However, the main drawback is that data needs to be examined to rephrase or summarize the essential terms of extraction. Due to improper content extraction, the accuracy gets poor in precision level to increase the false content rate. To tackle these problems, in this paper presents a Machine Learning (ML) intelligence algorithm with Diverse Beam Search-Based Maximum Mutual Information (DBSMMI) and Ant Colony Optimization (ACO)-optimized Deep Belief Network Based Text Summarization (DBNTS). Initially, the COVID-19 Twitter dataset is preprocessed to remove noise and create a Pheromone value set based on the k-means semantic similarity algorithm. Our work analyzes and clusters the data according to their theme (area). Data analysis is the central concept and is performed using WordNet keyword matching and semantic matching of the words. Then, the similar word is clustered using a semantic similarity-based k-means clustering algorithm. Using DBSMMI to make identical content phrases maximum supports term sentence extraction. The maximum support clustered group is optimized for the respective theme using ant colony optimization with the DBNTS algorithm. The algorithm's efficiency can be tested with an existing classifier algorithms. The ACO semantic recommender system is implemented in this article to recommend relevant news to the Twitter user. The proposed simulation attains the 92.35% of accuracy, and 90.29% of precision performance. The proposed method efficiently improves classification accuracy, and precision performance compared to other methods. [ABSTRACT FROM AUTHOR]

Mapping and monitoring crops are the most complex and difficult tasks for experts processing and analyzing remote sensing (RS) images. Classifying crops using RS images is the most expensive task, and it requires intensive labor, especially in the sample collection phase. Fieldwork requires periodic visits to collect data about the crop's physiochemical characteristics and separating them using the known conventional machine learning algorithms and remote sensing images. As the problem becomes more complex because of the diversity of crop types and the increase in area size, sample collection becomes more complex and unreliable. To avoid these problems, a new segmentation model was created that does not require sample collection or high-resolution images and can successfully distinguish wheat from other crops. Moreover, UNet is a well-known Convolutional Neural Network (CNN), and the semantic method was adjusted to become more powerful, faster, and use fewer resources. The new model was named Fast-UNet and was used to improve the segmentation of wheat crops. Fast-UNet was compared to UNet and Google's newly developed semantic segmentation model, DeepLabV3+. The new model was faster than the compared models, and it had the highest average accuracy compared to UNet and DeepLabV3+, with values of 93.45, 93.05, and 92.56 respectively. Finally, new datasets of time series NDVI images and ground truth data were created. These datasets, and the newly developed model, were made available publicly on the Web. [ABSTRACT FROM AUTHOR]

This study aimed to derive educational contents from the verses on individual responsibility mentioned in the Qur’an, and from this primary goal emerge the following sub-goals: deducing educational principles from the verses on individual responsibility, explaining the educational values deduced from the verses on individual responsibility and highlighting contemporary educational applications deduced from the verses on individual responsibility, which reached ten verses. The study relied on the descriptive approach—the deductive analytical method in extracting educational contents from the specific verses. The study results indicated that the Holy Qur’an, including its verses on individual responsibility, includes many educational contents of principles and values, which we must work to derive to be applied in the educational field. The study clarified the principles of individual responsibility that should be developed in young people, the most important of which are justice, self-control, bearing responsibility, self-struggle, and decision-making. The study also resulted in values derived from the verses of individual responsibility, the most important of which are fear of Allāh, warning against injustice to the soul, spending, purifying the soul, gratitude, good deeds, and modesty. The study highlighted the educational importance of each principle and value mentioned in the noble verses and that each value significantly impacts human life. The study also presented some contemporary educational applications that can be exploited and applied in developing individual responsibility through the family and school. [ABSTRACT FROM AUTHOR]

Copyright of Online Journal of Art & Design is the property of Online Journal of Art & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In generative syntax, two major types of proposals – syntax-oriented and semantics-oriented proposals – have been used to examine adverbs and adverbials. Despite these proposals explaining the remarkable properties of adverb positioning, this class of words is heterogeneous and problematic in terms of their displacement. This study adopts scopal theories and proposes a partial semantic analysis using phases to describe adverbial positioning in Modern Standard Arabic. We argue that the semantic scope and the modification domain are the main determinants of adverbial position, which can best be analysed in terms of phases. In this regard, adverbials are classified into lower and higher adverbials. Lower adverbials have a narrow semantic scope and modification domain. They include subject-oriented and verb-oriented modifiers and freely adjoin the vP layer. Higher adverbials have a wider scope and modification domain. They include discourse-oriented modifiers and tend to attach to the Complementiser Phrase layer. In terms of their hierarchical order, discourse-oriented adverbials are higher than subject-oriented and verb-oriented modifiers in the articulated clause structure. This conclusion suggests that scopal theories along with language-specific rules may provide a unified generalization and straightforward account of the cross-linguistic distribution of adverbs and adverbials.

In order to decode the difference between audiovisual bimodal and unimodal responses of the human brain in semantic context, this study designed a related task paradigm and applied a new generation magnetoencephalogram combined with the machine learning model to analyze the collected signals from three perspectives: behavioral response, event-related field (ERF) and single-trial detection. Results show that the unimodal semantic response was mainly concentrated in the occipital cortex, while the bimodal semantic response was mainly concentrated in the parietal cortex. At the same time, respondents' response rate and the detection accuracy of single-trial in bimodal mode were significantly higher than that in unimodal mode. Moreover, the support vector machine (SVM) showed the best classification performance among the four machine learning models, with an average classification accuracy of 75.16% for within-subject classification and 80.56% for between-subject classification. This research concludes that the combination of optically pumped magnetometer-magnetoencephalography (OPM-MEG) and machine learning model provides an efficient approach to decode the difference between audiovisual bimodal and unimodal responses of the human brain in semantic context.

The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions.

Abstract Humans excel at learning and recognizing objects, swiftly adapting to new concepts with just a few samples. However, current studies in computer vision on few-shot learning have not yet achieved human performance in integrating prior knowledge during the learning process. Humans utilize a hierarchical structure of object categories based on past experiences to facilitate learning and classification. Therefore, we propose a method named n-Hierarchy SEmantic Guided Attention (nHi-SEGA) that acquires abstract superclasses. This allows the model to associate with and pay attention to different levels of objects utilizing semantics and visual features embedded in the class hierarchy (e.g., house finch-bird-animal, goldfish-fish-animal, rose-flower-plant), resembling human cognition. We constructed an nHi-Tree using WordNet and Glove tools and devised two methods to extract hierarchical semantic features, which were then fused with visual features to improve sample feature prototypes.

Deep learning (DL)-based approaches are notable for their ability to establish feature associations without relying on physical constraints, unlike traditional strategies that are complex and dependent on expert experience. However, three main challenges hinder the versatility of semantic segmentation models. First, the targets in these images are dense and exist at varying spatial scales, which imposes higher demands on the model for accurate segmentation across scales. Second, the segmentation of small targets in the images is often overlooked, leading to a compromise between fine segmentation and model efficiency. Lastly, the data-intensive nature of remote sensing images and the resource-intensive operations of large-scale networks impose significant communication and computation burdens on edge devices, which may not have sufficient resources to handle them effectively. To address these challenges, this paper proposes a lightweight semantic segmentation method for remote sensing images to achieve high-precision segmentation for multi-scale targets while maintaining low computational complexity. The main components include: (1) embedding the inverted residual block structure to minimize the number of model parameters and computational costs; (2) introducing the parallel irregular space pyramid pooling module to efficiently aggregate multi-scale contextual information for fine-grained recognition of small targets; and (3) embedding transfer learning into the encoder-decoder structure to speed up the convergence rate and improve multi-scale feature fusion capability, thereby reducing semantic information loss. The proposed lightweight method has been extensively tested on real-world high-resolution remote sensing datasets. It achieved PA, MPA, MIoU, and FWIoU scores of 87.90%, 75.76%, 66.29%, and 78.81% on the Vaihingen dataset; 87.03%, 85.31%, 74.85%, and 77.54% on the Potsdam dataset; and 95.37%, 83.33%, 75.70%, and 91.31% on the Aeroscapes dataset. Compared to other popular semantic segmentation models, the proposed method achieved the highest values in all four evaluation indicators, demonstrating its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Introduction: Vocabulary knowledge achievement is crucial for effective language learning. However, there is a gap in vocabulary knowledge achievement, particularly at the Seto High School in Ethiopia. This study addresses this gap by focusing on Grade 11 students and investigating the effect of vocabulary learning strategies on students' vocabulary knowledge achievement and motivation. Methods: A quasi-experimental design was employed, involving two natural classes of Grade 11 students, with 30 students in the experimental group and 30 in the control group, selected through convenience sampling. A mixedmethod research design was also used to gather comprehensive data. The data collected included pretest and posttest assessments of vocabulary knowledge achievement, a vocabulary learning strategies (VLS) questionnaire, and semi structured interviews. The analysis of the data utilized statistical methods such as means, standard deviations, independent t - tests, and correlations to evaluate the effects of vocabulary learning strategies on students' vocabulary knowledge achievement and motivation. Results: High reliability was observed for both the VLS questionnaire and the tests. Pretest results revealed no significant (p > 0.05) difference in vocabulary knowledge achievement between the experimental and control groups. Posttest results demonstrated a statistically significant (p < 0.05) improvement in vocabulary achievement in the experimental group compared to the control group. Correlation analysis revealed a positive and significant (p < 0.05) association between VLS use and vocabulary knowledge achievement. Survey results and qualitative data showed that students predominantly relied on dictionary-based and I keep vocabulary notebook vocabulary learning strategies before training, with a noticeable shift toward increased use of guessing, keyword, and semantic mapping strategies after training. These findings underscore the effectiveness of VLS training in enhancing vocabulary knowledge among Grade 11 students. Finally, some recommendations are proposed. Conclusion: The study concluded that training in vocabulary learning strategies had a significant impact on students' vocabulary knowledge achievement. [ABSTRACT FROM AUTHOR]

Copyright of Al-Adab / Al-ādāb is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions. [ABSTRACT FROM AUTHOR]

The semantic knowledge stored in our brains can be accessed from different stimulus modalities. For example, a picture of a cat and the word "cat" both engage similar conceptual representations. While existing research has found evidence for modality-independent representations, their content remains unknown. Modality-independent representations could be semantic, or they might also contain perceptual features. We developed a novel approach combining word/picture cross-condition decoding with neural network classifiers that learned latent modality-independent representations from MEG data (25 human participants, 15 females, 10 males). We then compared these representations to models representing semantic, sensory, and orthographic features. Results show that modality-independent representations correlate both with semantic and visual representations. There was no evidence that these results were due to picture-specific visual features or orthographic features automatically activated by the stimuli presented in the experiment. These findings support the notion that modality-independent concepts contain both perceptual and semantic representations. [ABSTRACT FROM AUTHOR]

Purpose: Processing real-world sounds requires acoustic and higher-order semantic information. We tested the theory that individuals with autism spectrum disorder (ASD) show enhanced processing of acoustic features and impaired processing of semantic information. Methods: We used a change deafness task that required detection of speech and non-speech auditory objects being replaced and a speech-in-noise task using spoken sentences that must be comprehended in the presence of background speech to examine the extent to which 7–15 year old children with ASD (n = 27) rely on acoustic and semantic information, compared to age-matched (n = 27) and IQ-matched (n = 27) groups of typically developing (TD) children. Within a larger group of 7–15 year old TD children (n = 105) we correlated IQ, ASD symptoms, and the use of acoustic and semantic information. Results: Children with ASD performed worse overall at the change deafness task relative to the age-matched TD controls, but they did not differ from IQ-matched controls. All groups utilized acoustic and semantic information similarly and displayed an attentional bias towards changes that involved the human voice. Similarly, for the speech-in-noise task, age-matched–but not IQ-matched–TD controls performed better overall than the ASD group. However, all groups used semantic context to a similar degree. Among TD children, neither IQ nor the presence of ASD symptoms predict the use of acoustic or semantic information. Conclusion: Children with and without ASD used acoustic and semantic information similarly during auditory change deafness and speech-in-noise tasks. [ABSTRACT FROM AUTHOR]

Topic modeling is a powerful technique to discover hidden topics and patterns within a collection of documents without prior knowledge. Traditional topic modeling and clustering-based techniques encounter challenges in capturing contextual semantic information. This study introduces an innovative end-to-end semantic-driven topic modeling technique for the topic extraction process, utilizing advanced word and document embeddings combined with a powerful clustering algorithm. This semantic-driven approach represents a significant advancement in topic modeling methodologies. It leverages contextual semantic information to extract coherent and meaningful topics. Specifically, our model generates document embeddings using pre-trained transformer-based language models, reduces the dimensions of the embeddings, clusters the embeddings based on semantic similarity, and generates coherent topics for each cluster. Compared to ChatGPT and traditional topic modeling algorithms, our model provides more coherent and meaningful topics. [ABSTRACT FROM AUTHOR]

Copyright of Larq Journal for Philosophy, Linguistics & Social Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of RILEX Revista Sobre Investigaciones Léxicas is the property of Editorial de la Universidad de Jaen and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Brain Topography is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The exponential expansion of information has made text feature extraction based on simple semantic information insufficient for the multidimensional recognition of textual data. In this study, we construct a text semantic structure graph based on various perspectives and introduce weight coefficients and node clustering coefficients of co-occurrence granularity to enhance the link prediction model, in order to comprehensively capture the structural information of the text. Firstly, we jointly build the semantic structure graph based on three proposed perspectives (i.e., scene semantics, text weight, and graph structure), and propose a candidate keyword set in conjunction with an information probability retrieval model. Subsequently, we propose weight coefficients of co-occurrence granularity and node clustering coefficients to improve the link prediction model based on the semantic structure graph, enabling a more comprehensive acquisition of textual structural information. Experimental results demonstrate that our research method can reveal potential correlations and obtain more complete semantic structure information, while the WPAA evaluation index validates the effectiveness of our model. [ABSTRACT FROM AUTHOR]

A long-standing question concerns whether sensory input can reach semantic stages of processing in the absence of attention and awareness. Here, we examine whether the N400, an event related potential associated with semantic processing, can occur under conditions of inattentional blindness. By employing a novel three-phase inattentional blindness paradigm designed to maximise the opportunity for detecting an N400, we found no evidence for it when participants were inattentionally blind to the eliciting stimuli (related and unrelated word pairs). In contrast, participants noticed the same task-irrelevant word pairs when minimal attention was allocated to them, and a small N400 became evident. When the same stimuli were fully attended and relevant to the task, a robust N400 was observed. In addition to univariate ERP measures, multivariate decoding analyses were unable to classify related from unrelated word pairs when observers were inattentionally blind to the words, with decoding reaching above-chance levels only when the words were (at least minimally) attended. By comparison, decoding reached above-chance levels when contrasting word pairs with non-word stimuli, even when participants were inattentionally blind to these stimuli. Our results also replicated several previous studies by finding a “visual awareness negativity” (VAN) that distinguished task-irrelevant stimuli that participants noticed compared with those that were not perceived, and a P3b (or “late positivity”) that was evident only when the stimuli were task relevant. Together, our findings suggest that semantic processing might require at least a minimal amount of attention.

It is well known that nominalization represents a component part of a wider linguistic phenomenon, named conversion, and consists in passing of a word from a certain part of speech to that of the noun. Lately, in the act of speaking, there is an intensification of the nominalization process of the adjectives. The fact is caused by the frequent, regular use of the adjective in a close syntactic connection with the noun. Consequently, occurs the compression, the restriction of the syntactic group (noun + adjective), the adjective assuming the meaning of the whole combination. Simultaneously, from a semantic perspective, it becomes more independent, fulfilling in the sentence the syntactic function of subject or complement. The process is so active, so that it also requires a lexicographical re-approach.

Familiar music facilitates memory retrieval in adults with dementia. However, mechanisms behind this effect, and its generality, are unclear because of a lack of parallel work in healthy aging. Exposure to familiar music enhances spontaneous recall of memories directly cued by the music, but it is unknown whether such effects extend to deliberate recall more generally - e.g., to memories not directly linked to the music being played. It is also unclear whether familiar music boosts recall of specific episodes versus more generalised semantic memories, or whether effects are driven by domain-general mechanisms (e.g., improved mood). In a registered report study, we examined effects of familiar music on deliberate recall in healthy adults ages 65-80 years (N = 75) by presenting familiar music from earlier in life, unfamiliar music, and non-musical audio clips across three sessions. After each clip, we assessed free recall of remote memories for pre-selected events. Contrary to our hypotheses, we found no effects of music exposure on recall of prompted events, though familiar music evoked spontaneous memories most often. These results suggest that effects of familiar music on recall may be limited to memories specifically evoked in response to the music (Preprint and registered report protocol at https://osf.io/kjnwd/).

AbstractUnderstanding users’ satisfaction is fundamental for enhancing the effectiveness and usability of e-learning platforms. The existing approaches for analyzing users’ satisfaction leverage word embedding vectors to represent sentiment information, but they often fail to fully address the complex relationship between emotional and semantic information. Additionally, several emotional and semantic word embedding models are proposed, but they require sentiment information. In this study, we propose a novel multi-task deep neural model, called Sentiment-Emotion-Semantic Network (SES-Net), capable of learning sentiment, emotion, and semantic information simultaneously. The proposed model comprises three main sub-neural tasks: Bidirectional Long Short-Term Memory (BiLSTM) to capture sentiment, BiLSTM to extract semantics, and Convolutional Neural Networks (CNN) to learn emotional features. Experimental results reveal that, SES-Net outperforms the previous approaches by achieving an average F1-score of 90.59%. [ABSTRACT FROM AUTHOR]

The Qur'an is an unparalleled treasure as a linguistic and literary source. One of its literary aspects is the concepts within the Qur'an. Understanding the Qur'an necessitates accurately comprehending the words and expressions used in the verses. This requires identifying the root meaning of these words and the meanings they have acquired over time. This study examines and evaluates the important concept of “zīnat” (ornament/adornment), which adds richness to the Qur'an. The aim of this research is to identify the lexical meanings of the term "zīnat," the meanings it conveys, and its usage in the verses. To access the sources for this study, a review of printed materials was conducted. As the study focuses on the examination of the concept of zīnat in the Qur'an, the lexical meanings of this term were identified first. The primary focus of the study is the analysis of the term “zīnat” and other words used in similar meanings. After identifying the concept, its usage forms in the Qur'an were included. Considering the scope of the study, words related to zīnat in the Qur'an were included. However, not every concept with a semantic relationship to zīnat was included. Therefore, some words with similar meanings that do not appear in the Qur'an were not included in this research. [ABSTRACT FROM AUTHOR]

Background: Irregular word reading has been used to estimate premorbid intelligence in Alzheimer's disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The primary objective of this study is to ascertain whether irregular word reading serves as an indicator of cognitive and semantic decline in AD, potentially discouraging its use as a marker for premorbid intellectual abilities. Method: Six hundred eighty-one healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer's Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL), a region implicated in semantic memory. Results: EMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances. Conclusions: While the AmNART may be appropriate to measure premorbid intellectual abilities in cognitively unimpaired individuals, our results suggest that it captures current semantic decline in MCI and AD patients and may therefore underestimate premorbid intelligence. On the other hand, irregular word reading tests might be clinically useful to detect semantic impairments in individuals on the AD continuum. [ABSTRACT FROM AUTHOR]

The default mode network (DMN) lies towards the heteromodal end of the principal gradient of intrinsic connectivity, maximally separated from the sensory‐motor cortex. It supports memory‐based cognition, including the capacity to retrieve conceptual and evaluative information from sensory inputs, and to generate meaningful states internally; however, the functional organisation of DMN that can support these distinct modes of retrieval remains unclear. We used fMRI to examine whether activation within subsystems of DMN differed as a function of retrieval demands, or the type of association to be retrieved, or both. In a picture association task, participants retrieved semantic associations that were either contextual or emotional in nature. Participants were asked to avoid generating episodic associations. In the generate phase, these associations were retrieved from a novel picture, while in the switch phase, participants retrieved a new association for the same image. Semantic context and emotion trials were associated with dissociable DMN subnetworks, indicating that a key dimension of DMN organisation relates to the type of association being accessed. The frontotemporal and medial temporal DMN showed a preference for emotional and semantic contextual associations, respectively. Relative to the generate phase, the switch phase recruited clusters closer to the heteromodal apex of the principal gradient—a cortical hierarchy separating unimodal and heteromodal regions. There were no differences in this effect between association types. Instead, memory switching was associated with a distinct subnetwork associated with controlled internal cognition. These findings delineate distinct patterns of DMN recruitment for different kinds of associations yet common responses across tasks that reflect retrieval demands. [ABSTRACT FROM AUTHOR]

Although traditional vision-based SLAM (VSLAM) technologies have achieved impressive results, they are less satisfactory in challenging environments. Deep learning promotes the rapid development of computer vision and shows prominent advantages in image processing. It's a hot spot to combine deep learning with VSLAM, which is promising through the efforts of many researchers. Here, we introduce the combination of deep learning and traditional VSLAM algorithm, starting from the classical neural networks of deep learning. The achievements of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in depth estimation, pose estimation and closed-loop detection are summarized. The advantages of neural network in semantic information extraction are elaborated, and the future development of VSLAM is also prospected. [ABSTRACT FROM AUTHOR]

The Khotons, in the west of Mongolia, were originally Turkic people who spoke the Khoton language, until the 19th century, which is currently considered extinct. This study aimed to prove that the Khoton language belonged to the Turkic languages; and to standardize the Swadesh inventory in relation to the Khoton words. The Swadesh inventory of words was the primary source of this research, which was sampled to examine the basic characteristics like semantic (meaning) acoustic (sound-based), pronunciation and spellings. This study adopted a comparativehistorical research design with a qualitative approach, which involved an in-depth content analysis of the data. A data classification process was used to analyze the data by dividing them into categories (3-tiers) to enable retrieving, sorting and storing information. The Swadesh list of words also acted as the instrument of the study as this list was used to target the Khoton vocabulary and determine their equivalence. Such a data classification also helped to manipulate, track and analyze individual specimens in data. The findings of the study reveal that a majority of the linguistic combinations (lexemes) sampled for this study were found in the Khoton language, fully corresponding to the meaning of the English words in the Swadesh list. It was also evident that the basic vocabulary of the Khoton language had the elements of a Turkic language; and that there was an opportunity to reproduce the Khoton language. It is recommended that future studies should examine other versions of Swadesh inventory and compare them with the words in Turkic languages. [ABSTRACT FROM AUTHOR]

The study aims to highlight the impact of Al-Farra''s linguistic opinions in the book "Al- Ibanah" by Al-Awatbi, by researching the linguistic levels - grammatical, morphological and semantic -. At the grammatical level, the study presents Al-Farra's opinions on nominatives, accusatives, prepositions, and letters, the impact of the grammatical evidence that Al-Awatbi derived from him, and the nature of Al-Awatbi's adherence to Al- Farra's opinions. At the morphological level, the study examines Al-Farra's opinions on: punctuation, assimilation, pausing, masculinity and femininity, and some issues in the morphological scale. As for the semantic level, the study discusses Al-Farra's opinions on the stability and change of meaning, and the significance of constructions. In the end, the study presents some of Al-Farra's opinions on metaphor, parsing, the intrusive, and languages, and Al-Awatbi's influence on them [ABSTRACT FROM AUTHOR]

Machine translation evaluation is difficult and challenging for natural languages because different languages behave differently for the same dataset. Lexical-based metrics have been poorly represented semantic relationships and impose strict identity matching. However, translation and assessment become difficult for target morphologically rich languages with relatively free word order. Most of the standard evaluation metrics consider word order but do not effectively consider sentence structure. In this paper, we propose a novel machine translation evaluation metric SemSyn which incorporates both semantic and syntactic similarity. We incorporate the term frequency-inverse document frequency with the earth mover's distance and word embedding to cover the semantic similarity. The part of speech and dependency parsing tags assist in covering syntactic similarity in the sentence structure. Part of speech and dependency parsing tags are extracted from universal dependencies and trained on the SpaCy library. Experimental results show that SemSyn has a higher correlation with human judgment than other evaluation metrics for morphologically rich language and other languages. [ABSTRACT FROM AUTHOR]

Introduction: Believing comprises multifaceted processes that integrate information from the outside world through meaning‐making processes with personal relevance. Methods: Qualitative Review of the current literature in social cognitive neuroscience. Results: Although believing develops rapidly outside an individual's conscious awareness, it results in the formation of beliefs that are stored in memory and play an important role in determining an individual's behavior. Primal beliefs reflect an individual's experience of objects and events, whereas conceptual beliefs are based on narratives that are held in social groups. Conceptual beliefs can be about autobiographical, political, religious, and other aspects of life and may be encouraged by participation in group rituals. We hypothesize that assertions of future gains and rewards that transcend but are inherent in these codices provide incentives to follow the norms and rules of social groups. Conclusion: The power of conceptual beliefs to provide cultural orientation is likely to fade when circumstances and evidence make it clear that what was asserted no longer applies. [ABSTRACT FROM AUTHOR]

Water, indispensable for life and central to ecosystems, human activities, and climate dynamics, requires rapid and accurate monitoring. This is vital for sustaining ecosystems, enhancing human welfare, and effectively managing land, water, and biodiversity on both the local and global level. In the rapidly evolving domain of remote sensing and deep learning, this study focuses on water body extraction and classification through the use of recent deep learning models of visual foundation models (VFMs). Specifically, the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) models have shown promise in semantic segmentation, dataset creation, change detection, and instance segmentation tasks. A novel two-step approach involving segmenting images via the Automatic Mask Generator method of the SAM and the zero-shot classification of segments using CLIP is proposed, and its effectiveness is tested on water body extraction problems. The proposed methodology was applied to both remote sensing imagery acquired from LANDSAT 8 OLI and very high-resolution aerial imagery. Results revealed that the proposed methodology accurately delineated water bodies across complex environmental conditions, achieving a mean intersection over union (IoU) of 94.41% and an F1 score of 96.97% for satellite imagery. Similarly, for the aerial imagery dataset, the proposed methodology achieved a mean IoU of 90.83% and an F1 score exceeding 94.56%. The high accuracy achieved in selecting segments predominantly classified as water highlights the effectiveness of the proposed model in intricate environmental image analysis. [ABSTRACT FROM AUTHOR]

IntroductionVocabulary knowledge achievement is crucial for effective language learning. However, there is a gap in vocabulary knowledge achievement, particularly at the Seto High School in Ethiopia. This study addresses this gap by focusing on Grade 11 students and investigating the effect of vocabulary learning strategies on students’ vocabulary knowledge achievement and motivation.MethodsA quasi-experimental design was employed, involving two natural classes of Grade 11 students, with 30 students in the experimental group and 30 in the control group, selected through convenience sampling. A mixed-method research design was also used to gather comprehensive data. The data collected included pretest and posttest assessments of vocabulary knowledge achievement, a vocabulary learning strategies (VLS) questionnaire, and semi structured interviews. The analysis of the data utilized statistical methods such as means, standard deviations, independent t - tests, and correlations to evaluate the effects of vocabulary learning strategies on students’ vocabulary knowledge achievement and motivation.ResultsHigh reliability was observed for both the VLS questionnaire and the tests. Pretest results revealed no significant (p > 0.05) difference in vocabulary knowledge achievement between the experimental and control groups. Posttest results demonstrated a statistically significant (p

Information about trees in forest is essential for the assessment of the quantity and the quality of forest ecosystem services. Recently, Deep Learning (DL) methods were regarded as a new cornerstone of algorithm development. Semantic annotations of 3D forest scenes are fundamental for DL algorithm developments. Its necessity has become more urgent as DL is data-driven and requires large amount of training and verification data. However, high-quality annotated forest datasets are still rare, as trees comprise of irregular structures and small components and pose significantly greater challenges even for manual recognition in comparison with artificial objects. This paper introduces a new open point cloud dataset ForestSemantic for forest semantic studies at both individual tree- and plot-levels. The dataset is based on TLS data with different forest conditions. Manual annotation was carried out to a level of detail 4, i.e., until all visible branches. Semantic information is provided at both plot- and tree-levels, as well as at both object- and point-level. Thus, the dataset supports both instance and semantic studies, such as objects detection and segmentation and classification at both tree- and plot-levels. In addition, the dataset also provides comprehensive structural tree traits as reference for further methodological development and verification. This dataset is expected to facilitate research in new dimensions and benchmarks of different systems and solutions. A few examples are demonstrated in this paper to unveil the potentials of the dataset for various applications. In future, it is also possible to simulate other types of point clouds by down-sampling and deforming, and to transfer the dataset for training and verification of other close-range sensing systems, as the dataset was generated using TLS point clouds that represent the highest spatial resolution and geometric accuracy in all close-range point clouds.

Image captioning is a hot topic that combines a multidiscipline task between computer vision and natural language processing. One of the tasks in the geological field is to make descriptions from the images of geological rocks. The task of a geologist is to write a content description of an image and display it as text that can be used in the future. Interpretation of the object is one of the objectives of the research, which is to traverse the image structures in depth. Shapes, colors, and structures are to be focused on to get the image’s features. The problem faced is how the separable neural network (SNN) and long short-term memory (LSTM) have an impact on the caption that can meet the geologist’s description. SNN is called Visual Attention (VaT), and LSTM is called Semantic Attention (SemAtt) as an architecture of image captioning. The result of the experiment confirms that the accuracy model for captioning gets BLEU- $1=0.908$ , BLEU- $2=0.877$ , BLEU- $3=0.750$ , and BLEU- $4=0.510$ . The evaluation score is compared to those of other evaluators, such as Meteor and RougeL, which get 0.670 and 0.623, respectively. The model confirms that it outperforms the baseline model. Referring to the evaluations, we concluded that the model was able to generate captioned geological rock images that met the geologist’s description. Precision and recall have supported the models in providing the predicted word that is suitable for the image features.