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This paper describes a method and technology for processing natural language texts and extracting data from the text that correspond to the semantics of an ontological model. The proposed method is distinguished by the use of a Large Language Model algorithm for text analysis. The extracted data are stored in an intermediate format, after which individuals and properties that reflect the specified semantics are programmatically created in the ontology. The proposed technology is implemented using the example of an ontological model that describes the geographical configuration and administrative–territorial division of Kazakhstan. The proposed method and technology can be applied in any subject areas for which ontological models have been developed. The results of the study can significantly improve the efficiency of using knowledge bases based on semantic networks by converting texts in natural languages into semantically linked data. [ABSTRACT FROM AUTHOR]

Semantic communication and spectrum sharing are pivotal technologies in addressing the perennial challenge of scarce spectrum resources for the sixth-generation (6G) communication networks. Notably, scant attention has been devoted to investigating semantic resource allocation within spectrum sharing semantic communication networks, thereby constraining the full exploitation of spectrum efficiency. To mitigate interference issues between primary users and secondary users while augmenting legitimate signal strength, the introduction of Intelligent Reflective Surfaces (IRS) emerges as a salient solution. In this study, we delve into the intricacies of resource allocation for IRS-enhanced semantic spectrum sharing networks. Our focal point is the maximization of semantic spectral efficiency (S-SE) for the secondary semantic network while upholding the minimum quality of service standards for the primary semantic network. This entails the joint optimization of parameters such as semantic symbol allocation, subchannel allocation, reflective coefficients of IRS elements, and beamforming adjustment of secondary base station. Recognizing computational intricacies and interdependence of variables in the non-convex optimization problem formulated, we present a judicious approach: a hybrid intelligent resource allocation approach leveraging dueling double-deep Q networks coupled with the twin-delayed deep deterministic policy. Simulation results unequivocally affirm the efficacy of our proposed resource allocation approach, showcasing its superior performance relative to baseline schemes. Our approach markedly enhances the S-SE of the secondary network, thereby establishing its prowess in advancing the frontiers of semantic spectrum sharing (S-SE). [ABSTRACT FROM AUTHOR]

This article aims to evaluate how teachers, in training and in-service, define the concepts of randomness, probability, chance and variability, fundamental terms in the teaching of statistics. To this end, a printed recording protocol of natural semantic networks was applied to 16 teachers in training and 58 in-service teachers, selected through non-probabilistic sampling. The results provide evidence that the concept with the lowest conceptual density in both groups is variability. Likewise, a greater presence of similar words was observed between randomness, chance and probability, despite being different concepts. Another finding is the association of the inducing concepts to words that are used within the basic lexicon in Chilean Spanish, whose dictionary is not specialized in statistics. It is concluded about the scarce use of technical language by the participants, which would eventually affect the teaching of statistics. [ABSTRACT FROM AUTHOR]

Traditional methods of weakly supervised semantic segmentation (WSsegmentation) for point cloud scenes have several limitations including limited precision and difficulty in handling complex scenes due to imprecise labels or partial annotations. To address these issues, we perform view-based adversarial training on the original point cloud scene samples through view resampling and Gaussian noise perturbation to reduce overfitting. Combining a self-attention mechanism with multi-layer perceptrons and point cloud segmentation strategy, we can perform dimensionality enhancement and dimensionality reduction operations to better capture the local features of point cloud data. Finally, we can obtain the semantic segmentation results of point cloud scene by fusing local and global semantic features. In the design of the network loss function, we combine the Siamese loss and the smoothness loss and the cross-entropy loss to improve the ability and fidelity of semantic networks. Specifically, the Siamese loss is used to compute the distance between different augmented point cloud data in their feature embedding space and the smoothness loss is used to penalize the discontinuity of semantic information between adjacent regions. The proposed weakly supervised segmentation network achieves an overall segmentation accuracy close to fully supervised segmentation methods and outperforms most of the existing weakly supervised segmentation methods by 5% to 10% in scene segmentation in terms of mIoU on S3DIS, ShapeNet, and PartNet datasets. Extensive experiments demonstrate the robustness, effectiveness, and generalization of the proposed point cloud segmentation network. [Display omitted] • We propose effective data augmentation methods, including view resampling and adversarial training, to tackle partial annotations. • To fully integrate the global and local features of point cloud, we employ high-dimensional feature self-attention mechanism and multi-layer perceptrons to fuse multi-level features. • We present a novel point cloud segmentation framework, which can achieve the higher segmentation accuracy than traditional segmentation networks for different point cloud scenes. • Extensive experiments demonstrate the robustness, effectiveness, and generalization of the proposed weakly supervised point cloud semantic segmentation network. [ABSTRACT FROM AUTHOR]

This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers' judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods. [ABSTRACT FROM AUTHOR]

Semantic network is a network of concepts connected by semantic relations. It contains two forms of binary semantic network and multiplex semantic network. The associated fact prediction is a link prediction task that aims to infer the implicitly connected facts by mining the high-level representation of the network. Previous methods for associated fact prediction put much emphasis on the topological feature of network but not utilize the information of semantic expression. This paper proposes a Semantic Network Encoder (SemNE), which learns a feature mapping function from the binary semantic networks and can be applied to the multiplex semantic networks in a pre-training manner. SemNE is a two-stage framework that contains an embedding encoder and a prediction decoder. It jointly models the semantic information and network topology to enrich the network representation. A word self-organization method based on the factual boundary is proposed to unify the topological feature and the semantic feature representations. Experimental results on binary semantic networks show that SemNE achieves the state-of-the-art results in associated fact prediction and experimental results on multiplex semantic networks show that SemNE is scalable and can effectively improve the performance of existing models. [ABSTRACT FROM AUTHOR]

Recent years have seen a significant and rapid rise in the development and deployment of many urban digital twins worldwide. Urban digital twins provide a central platform for incorporating data and knowledge from different sources and fields, and can thus be used for many urban-related processes such as urban planning, analysis, monitoring and visualization. One of the key requirements of digital twins in general and urban digital twins in particular is the continuous, bidirectional data flow between the physical entity and its digital counterpart. Consequently, this requires the ability to both detect and understand changes between different temporal versions of the physical and digital entities. In the context of smart cities and semantic 3D city models however, only a few studies have addressed this so far. This is due to the facts that: (1) Semantic 3D city models (mostly encoded in CityGML) contain multifaceted information modelled in a complex inheritance hierarchy, which complicates their change detection process; (2) As cities constantly evolve over time, matching their datasets often results in a large number of changes, which must be further analysed and processed to produce meaningful information; (3) Individual changes in the datasets are multi-layered as well as often correlated and cannot be fully understood without considering their context in the city model; and (4) Different types of changes are perceived and interpreted differently by different stakeholders, meaning that the outcome of the change detection and interpretation process must ultimately serve the human factor. Therefore, to address these challenges, this research proposes a Path-tracing Semantic Network (PSN) to interpret detected changes in semantic 3D city models. The framework represents the multidimensional nature of changes together with stakeholders in a semantic network, where their interrelations can be analysed explicitly using graph-based path-tracing methods. [ABSTRACT FROM AUTHOR]

To accurately and formally represent the historical trajectory and present the current situation of land use/land cover (LULC), numerous types of classification standards for LULC have been developed by different nations, institutes, organizations, etc.; however, these land cover classification systems and legends generate polysemy and ambiguity in integration and sharing. The approaches for dealing with semantic heterogeneity have been developed in terms of semantic similarity. Generally speaking, these approaches lack domain ontologies, which might be a significant barrier to implementing these approaches in terms of semantic similarity assessment. In this paper, we propose an ontological approach to assess the similarity of the domain of LULC classification systems and standards. We develop domain ontologies to explicitly define the descriptions and codes of different LULC classification systems and standards as semantic information, and formally organize this semantic information as rules for logical reasoning. Then, we utilize a Bayes algorithm to create a conditional probabilistic model for computing the semantic similarity of terms in two separate LULC land cover classification systems. The experiment shows that semantic similarity can be effectively measured by integrating a probabilistic model based on the content of ontology. [ABSTRACT FROM AUTHOR]

According to the online database landmarkmap, up to an estimated 50% or more of the world’s habitable land is held by indigenous peoples and communities. While legal and procedural provisions are being made for bureaucratically managing the many different types of tenure relations in this domain, there continues to be a lack of tools and expertise needed to quickly and accurately document customary and indigenous land rights. Software and hardware tools that have been designed for documenting land tenure through communities continue to assume a parcel-based model of land as well as categories of land relations (RRR) largely dimensionally similar to statutory land rights categories. The SmartSkeMa approach to land tenure documentation combines sketching by hand with aerial imagery and an ontology-based model of local rules regulating land tenure relations to produce a system specifically designed to allow accurate documentation of land tenure from a local perspective. In addition, the SmartSkeMa adaptor which is an OWL-DL based set of rules for translating local land related concepts to the LADM concepts provides a more high-level view of the data collected (i.e., what does this concept relate to within the national LADM profile?) In this paper we present the core functionalities of SmartSkeMa using examples from Kenya and Ethiopia. Based on an expert survey and focus groups held in Kenya, we also analyze how the approach fairs on the Fit-for-Purpose Land Administration tools scale. The results indicate that the approach could be beneficial in scaling up mapping of community and customary lands as well as help reduce conflict through its participatory nature. [ABSTRACT FROM AUTHOR]

The dominant video captioning methods employ the attentional encoder–decoder architecture, where the decoder is an autoregressive structure that generates sentences from left‐to‐right. However, these methods generally suffer from the exposure bias issue and neglect the guidance of future output contexts obtained from the right‐to‐left decoding. Here, the authors propose a new symmetric bidirectional decoder for video captioning. The authors first integrate the self‐attentive multi‐head attention and bidirectional gated recurrent unit for capturing the long‐term semantic dependencies in videos. The authors then apply one single decoder to generate accurate descriptions from left‐to‐right and right‐to‐left simultaneously. The decoder in each decoding direction performs two cross‐attentive multi‐head attention modules to consider both the past hidden states from the same decoding direction and the future hidden states from the reverse decoding direction at each time step. A symmetric semantic‐guided gated attention module is specially devised to adaptively suppress the irrelevant or misleading contents in the past or future output contexts and retain the useful ones for avoiding under‐description. Experimental evaluations on two widely applied benchmark datasets: Microsoft research video to text and Microsoft video description corpus, demonstrate that the authors' proposed method obtains substantially state‐of‐the‐art performance, which validates the superiority of the bidirectional decoder. [ABSTRACT FROM AUTHOR]

Source code suggestion is the utmost helpful feature in the integrated development environments that helps to quicken software development by suggesting the next possible source code tokens. The source code contains useful semantic information but is ignored or not utilised to its full potential by existing approaches. To improve the performance of source code suggestion, the authors propose a deep semantic net (DeepSN) that makes use of semantic information of the source code. First, DeepSN uses an enhanced hierarchical convolutional neural network combined with code‐embedding to automatically extract the top‐notch features of the source code and to learn useful semantic information. Next, the source code's long and short‐term context dependencies are captured by using long short‐term memory. We extensively evaluated the proposed approach with three baselines on ten real‐world projects and the results are suggesting that the proposed approach surpasses state‐of‐the‐art approaches. On average, DeepSN achieves 7.6% higher accuracy than the best baseline. [ABSTRACT FROM AUTHOR]

The semantic network structure is a core aspect of the mental lexicon and is, therefore, a key to understanding language development processes. This study investigated the structure of the semantic network of adolescents with intellectual disability (ID) and children with typical development (TD) using network analysis. The semantic networks of the participants (nID = 66; nTD = 49) were estimated from the semantic verbal fluency task with the pathfinder method. The groups were matched on the number of produced words. The average shortest path length (ASPL), the clustering coefficient (CC), and the network's modularity (Q) of the two groups were compared. A significantly smaller ASPL and Q and a significantly higher CC were found for the adolescents with ID in comparison with the children with TD. Reasons for this might be differences in the language environment and differences in cognitive skills. The quality and quantity of the language input might differ for adolescents with ID due to differences in school curricula and because persons with ID tend to engage in different out-of-school activities compared to TD peers. Future studies should investigate the influence of different language environments on the language development of persons with ID. [ABSTRACT FROM AUTHOR]

The “Growing Up Asian American” (GUAA) tag on YouTube conveys the marginalized experiences of Asian American women and offers narratives not shown in the mainstream media as a networked counterpublic. By exploring YouTube videos using this tag, I analyzed counterpublic discourses formed by nonstar Asian American YouTubers and how these discourses were shaped by the culture and medium of YouTube. Using mixed methods, including semantic network analysis and discursive textual analysis, I explored the unique traits of the GUAA Asian American networked counterpublic on YouTube. Although the discourses formed around the tag are considered relatively soft, an issue compounded by the nature of microcelebrity culture, the GUAA tag has also allowed Asian Americans to narrate their own personal and affective stories. The audiovisual nature of YouTube allows Asian Americans to talk with their bodies and voices to express their rich, heterogeneous, and intersectional identity, which cannot be reduced to one monolithic media portrayal. [ABSTRACT FROM AUTHOR]

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency--inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a wordlevel CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior. [ABSTRACT FROM AUTHOR]

To better solve scale variance problem, deep multi-scale methods usually detect objects of different scales by different in-network layers. However, the semantic levels of features from different layers are usually inconsistent. In this paper, we propose a multi-branch and high-level semantic network by gradually splitting a base network into multiple different branches. As a result, the different branches have same depth and the output features of different branches have similarly high-level semantics. Due to the difference of receptive fields, the different branches are suitable to detect objects of different scales. Meanwhile, the multi-branch network does not introduce additional parameters by sharing the convolutional weights of different branches. To further improve detection performance, skip-layer connections are used to add context to the branch of relatively small receptive field, and dilated convolution is incorporated to enlarge the resolutions of output feature maps. When they are embedded into Faster RCNN architecture, the weighted scores of proposal generation network and proposal classification network are further proposed. Experiments on three pedestrian datasets (i.e., the KITTI dataset, the Caltech dataset, and the Citypersons dataset), one face dataset (i.e., the WIDER FACE dataset), and two general object datasets (i.e., the COCO benchmark and the PASCAL VOC dataset) demonstrate the effectiveness and generality of proposed method. On these datasets, our method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process. [ABSTRACT FROM AUTHOR]

Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task that involves searching natural images through the use of free-hand sketches under the zero-shot scenario. Most previous methods project the sketch and image features into a low-dimensional common space for efficient retrieval, and meantime align the projected features to their semantic features (e.g., category-level word vectors) in order to transfer knowledge from seen to unseen classes. However, the projection and alignment are always coupled; as a result, there is a lack of explicit alignment that consequently leads to unsatisfactory zero-shot retrieval performance. To address this issue, we propose a novel progressive cross-modal semantic network. More specifically, it first explicitly aligns the sketch and image features to semantic features, then projects the aligned features to a common space for subsequent retrieval. We further employ cross-reconstruction loss to encourage the aligned features to capture complete knowledge about the two modalities, along with multi-modal Euclidean loss that guarantees similarity between the retrieval features from a sketch-image pair. Extensive experiments conducted on two popular large-scale datasets demonstrate that our proposed approach outperforms state-of-the-art competitors to a remarkable extent: by more than 3% on the Sketchy dataset and about 6% on the TU-Berlin dataset in terms of retrieval accuracy. [ABSTRACT FROM AUTHOR]

Are bilinguals more creative than monolinguals? Some prior research suggests bilinguals are more creative because the knowledge representations for their second language are similarly structured to those of highly creative people. However, there is contrasting research showing that the knowledge representations of bilinguals' second language are actually structured like those of less creative people. Finally, there is growing skepticism about there being differences between bilinguals and monolinguals on non-language tasks (e.g., the bilingual advantage for executive control). We tested whether bilinguals tested in their second language are more or less creative than both monolinguals and bilinguals tested in their first language. Participants also took a repeated semantic fluency test that we used to estimate individual semantic networks for each participant. We analyzed our results with Bayesian statistics and found support for the null hypothesis that bilingualism offers no advantage for creativity. Further, using best practices for estimating semantic networks, we found support for the hypothesis that there is no association between an individual's semantic network and their creativity. This is in contrast with published research, and suggests that some of those findings may have been the result of idiosyncrasies, outdated methods for estimating semantic networks, or statistical noise. Our results call into question reported relations between bilingualism and creativity, as well as semantic network structure as an explanatory mechanism for individual differences in creativity. [ABSTRACT FROM AUTHOR]

Ontologies represent the standard way to model the knowledge about specific domains. This holds also for the legal domain where several ontologies have been put forward to model specific kinds of legal knowledge. Both for standard users and for law scholars, it is often difficult to have an overall view on the existing alternatives, their main features and their interlinking with the other ontologies. To answer this need, in this paper, we address an analysis of the state-of-the-art in legal ontologies and we characterise them along with some distinctive features. This paper aims to guide generic users and law experts in selecting the legal ontology that better fits their needs and in understanding its specificity so that proper extensions to the selected model could be investigated. [ABSTRACT FROM AUTHOR]

Crowd counting is a challenging task dealing with the variation of an object scale and a crowd density. Existing works have emphasized on skip connections by integrating shallower layers with deeper layers, where each layer extracts features in a different object scale and crowd density. However, only high-level features are emphasized while ignoring low-level features. This paper proposes an estimation network by passing high-level features to shallow layers and emphasizing its low-level feature. Since an estimation network is a hierarchical network, a high-level feature is also emphasized by an improved low-level feature. Our estimation network consists of two identical networks for extracting a high-level feature and estimating the final result. To preserve semantic information, dilated convolution is employed without resizing the feature map. Our method was tested in three datasets for counting humans and vehicles in a crowd image. The counting performance is evaluated by mean absolute error and root mean squared error indicating the accuracy and robustness of an estimation network, respectively. The experimental result shows that our network outperforms other related works in a high crowd density and is effective for reducing over-counting error in the overall case. [ABSTRACT FROM AUTHOR]

Semantic memory encompasses knowledge of specific objects and their diverse associations, but the mechanisms that allow us to retrieve aspects of knowledge required for a given task are poorly understood. The dual hub theory suggests that separate semantic stores represent knowledge of (i) taxonomic categories (in the anterior temporal lobes, ATL) and (ii) thematic associations (in angular gyrus, AG or posterior middle temporal gyrus, pMTG). Alternatively, the controlled semantic cognition (CSC) framework suggests that semantic processing emerges from the flexible interaction of heteromodal semantic representations in ATL with a semantic control network, which includes pMTG as well as prefrontal regions. According to this view, ATL supports patterns of coherent auto-associative retrieval, while semantic control sites respond when ongoing conceptual activation needs to be altered to suit the task or context. These theories make different predictions about the nature of functional dissociations within the semantic network. We review evidence for these claims across multiple methods. First, we show ATL is sensitive to the strength of thematic associations as well as taxonomic relations. Next, we document functional dissociations between AG and pMTG: rather than these regions acting as comparable thematic hubs, AG is allied to the default mode network and supports more 'automatic' retrieval, while pMTG responds when control demands are high. However, the semantic control network, including pMTG, also shows a greater response to events/actions and verbs, supporting the claims of both theories. We propose that tasks tapping event semantics often require greater shaping of conceptual retrieval than comparison tasks, because these elements of our knowledge are inherently flexible, with relevant features depending on the context. In this way, the CSC account might be able to account for findings that suggest both a process and a content distinction within the semantic network. This article is part of the theme issue 'Towards mechanistic models of meaning composition'. [ABSTRACT FROM AUTHOR]

Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently. [ABSTRACT FROM AUTHOR]

Examines the property of semantic correctness of programs written in a shared-memory parallel language. Requirements in parallel programming; Importance of sequential consistency of a multiprocess device to parallel programming; Theorem of semantic equivalence.

Previous research demonstrates that rhetorical figures differentially affect the extent of ad processing. Specifically, tropes (a type of figure) deviate more from expected language use than schemes, with the greater deviation yielding more extensive ad processing. We extend previous research in two ways by focusing on the incongruity differences that exist between schemes and tropes. Study 1 uses syndicated data (Starch readership scores) to test how figures combine to affect the extent of processing. Results show that when figures leverage unique mechanisms (i.e., schemes and tropes), their combination yields incremental processing gains. Alternatively, when figures leverage redundant mechanisms (e.g., multiple tropes), their combination yields no incremental processing. Study 2 is an experiment that tests how figures separate in affecting the focus of ad processing. Results show that schemes generate a generalized focus on the entire ad, including both ad-stylistic and message-related aspects, while tropes generate a more selective focus on message-related aspects. [ABSTRACT FROM AUTHOR]

Very large-scale conversation (VLSC) involves the exchange of thousands of electronic mail (e-mail) messages among hundreds or thousands of people. Usenet newsgroups are good examples (but not the only examples) of online sites where VLSCs take place. To facilitate understanding of the social and semantic structure of VLSCs, two tools from the social sciences--social networks and semantic networks--have been extended for the purposes of interface design. As interface devices, social and semantic networks need to be flexible, layered representations that are useful as a means for summarizing, exploring, and cross-indexing the large volumes of messages that constitute the archives of VLSCs. This paper discusses the design criteria necessary for transforming these social scientific representations into interface devices. The discussion is illustrated with the description of the Conversation Map system, an implemented system for browsing and navigating VLSCs. [ABSTRACT FROM AUTHOR]

Images obtained from an illuminated scene often have their original colour contaminated. Colour constancy is a study considering how to restore them. Substantial progress on colour constancy has been made in recent years due to the development of a convolutional neural network (CNN). In a CNN structure, high-level features contain semantic information while low-level features show local details. If both are taken into account, they would help achieve a more accurate illuminant estimation. However, previous works paid little attention to the latter for lack of frameworks, which can combine those two kinds of features together. Inspired by the pyramid model, a top-down network that successively propagates high-level information to low-level layers is proposed. This network, named top-down semantic aggregation for colour constancy (TDCC), takes full advantage of the multi-scale representations with strong semantics. As a result, objects with intrinsic colours are captured and a better estimation is obtained. Experiments on three benchmark datasets demonstrate that TDCC significantly outperforms stateof- the-art colour constancy methods. [ABSTRACT FROM AUTHOR]

This paper suggests a method for refining a massive amount of collective intelligence data and visualizing it with a multilevel sentiment network in order to understand the relevant information in an intuitive and semantic way. This semantic interpretation method minimizes network learning in the system as a fixed network topology only exists as a guideline to help users understand. Furthermore, it does not need to discover every single node to understand the characteristics of each clustering within the network. After extracting and analyzing the sentiment words from the movie review data, we designed a movie network based on the similarities between the words. The network formed in this way will appear as a multilevel sentiment network visualization after the following three steps: (1) design a heatmap visualization to effectively discover the main emotions on each movie review; (2) create a two-dimensional multidimensional scaling (MDS) map of semantic word data to facilitate semantic understanding of network and then fix the movie network topology on the map; (3) create an asterism graphic with emotions to allow users to easily interpret node groups with similar sentiment words. The research also presents a virtual scenario about how our network visualization can be used as a movie recommendation system. We next evaluated our progress to determine whether it would improve user cognition for multilevel analysis experience compared to the existing network system. Results showed that our method provided improved user experience in terms of cognition. Thus, it is appropriate as an alternative method for semantic understanding. [ABSTRACT FROM AUTHOR]

Introduction. Truth and falsity play a dominant role in contemporary work on information, misinformation, and disinformation. Discussions of these notions typically include remarks about their truth-values, or even explicit arguments in favour of their having fixed truth-values. Method. I use Chalmers’s framework for identifying and potentially solving verbal disputes to analyse the discussions regarding information and truth. Analysis. Philosophical analyses of the discussions of information and its relation to truth, as they play out between Luciano Floridi and Don Fallis (among others), are carried out in the paper. Results. I find that these discussions are in fact a verbal dispute. Further, the dominant focus on truth and falsity in the work on information, misinformation, and disinformation, within philosophy of information, gives rise to a Floridian dilemma: a dilemma in which Floridi’s definition of semantic information, as inherently truthful, turns out to encompass certain varieties of misinformation and disinformation while excluding others. Conclusion. I recommend that information must be defined as semantic content in general without reference to truth, i.e. as truth-neutral, such that all varieties of misinformation and disinformation can be kinds of information. [ABSTRACT FROM AUTHOR]

A defining characteristic of Alzheimer's disease is difficulty in retrieving semantic memories, or memories encoding facts and knowledge. While it has been suggested that this impairment is caused by a degradation of the semantic store, the precise ways in which the semantic store is degraded are not well understood. Using a longitudinal corpus of semantic fluency data (listing of items in a category), we derive semantic network representations of patients with Alzheimer's disease and of healthy controls. We contrast our network-based approach with analyzing fluency data with the standard method of counting the total number of items and perseverations in fluency data. We find that the networks of Alzheimer's patients are more connected and that those connections are more randomly distributed than the connections in networks of healthy individuals. These results suggest that the semantic memory impairment of Alzheimer's patients can be modeled through the inclusion of spurious associations between unrelated concepts in the semantic store. We also find that information from our network analysis of fluency data improves prediction of patient diagnosis compared to traditional measures of the semantic fluency task. [ABSTRACT FROM AUTHOR]

Declarative memory enables cognitive agents to effectively store and retrieve factual memory in real-time. Increasing the capacity of a real-time agent's declarative memory increases an agent's ability to interact intelligently with its environment but requires a scalable retrieval system. This work represents an extension of the Accelerated Declarative Memory (ADM) system, referred to as Hardware Accelerated Declarative Memory (HADM), to execute retrievals on a GPU. HADM also presents improvements over ADM's CPU execution and considers critical behavior for indefinitely running declarative memories. The negative effects of a constant maximum associative strength are considered, and mitigating solutions are proposed. HADM utilizes a GPU to process the entire semantic network in parallel during retrievals, yielding significantly faster declarative retrievals. The resulting GPU-accelerated retrievals show an average speedup of approximately 70 times over the previous Service Oriented Architecture Declarative Memory (soaDM) implementation and an average speedup of approximately 5 times over ADM. HADM is the first GPU-accelerated declarative memory system in existence. [ABSTRACT FROM AUTHOR]

Objective: Positive symptoms, such as delusion and hallucination, commonly include negative emotional content in schizophrenia. We investigated the neural basis implicated during the processing of strong negative emotional words in patients with schizophrenia. Methods: In our study, 35 patients with schizophrenia and 19 healthy controls were recruited, and the participants were asked to passively view the words that contained swearing and neutral content during functional magnetic resonance imaging. Results: Patients with schizophrenia, compared to healthy controls, showed hypoactivation to the swear and neutral words stimuli in the left inferior frontal gyrus, left middle frontal gyrus, and left angular/supramarginal gyrus. More specifically, patients with remitted schizophrenia were found to have greater activation to the stimuli in the left middle/inferior frontal gyrus than patients with active schizophrenia. Furthermore, in the analysis of regions of interests, the left inferior and middle frontal gyrus activity was related to the severity of positive symptoms, including delusion and suspiciousness. Conclusion: Our results suggest that patients with schizophrenia have difficulty in semantic processing and inhibitory control of swear words, and these abnormalities may be connected with the severity of positive symptoms. [ABSTRACT FROM AUTHOR]

POOL—A Semantic Model for Approximate Reasoning—is based on the theory of linguistic fuzzy relations (LFR) and linguistic similarity relations (LSR). The proposed model combines the advantages of linguistic representation and numerical computation. Hence, it can facilitate knowledge representation and approximate reasoning for object/concept relation modeling especially in an environment containing imprecision, ambiguity, and/or uncertainty, which is often the case faced by knowledge engineers in building decision support systems. [ABSTRACT FROM AUTHOR]

The concept of an electronic marketplace is introduced whereby consumers may browse through a structured database giving product offerings and local supply outlets. We consider the nature of software to facilitate the shopping process developing a concept of similarity between generic products using a semantic net structure. The search is guided by specified user preferences. A prototype implementation, using Prolog, is described. [ABSTRACT FROM AUTHOR]

Highlights • Extending the natural language to the design intent representation for collaborative product design. • Supporting designers to share and exchange the design intents in knowledge-based collaborative CAD. • Combining collaborative design, ontology, semantic network and natural language generation together. • The ability in recognizing and repairing features, fixing design history and picking out features. Abstract Modern manufacturing firms are more inclining to promote the product quality, save costs and reduce times of product design by both collaborative designing and model reuse. If CAD components constructed collaboratively have information representing their developers' design intents embedded in the model, people's understanding over the product should be improved and the product model should be best reused. Until now, capturing, recording and presenting design intents still remains a challenge. It has been shown by empirical studies that textual summarisations can lead to improved decision making. In this paper, we propose an approach to generation the natural language description about design intents of collaboratively developed product. The approach brings together techniques from different areas of collaborative designing, ontology and semantic network, and natural language generation. The language generation process is guided by an information model we established to give a structured description about design intents of collaboratively products. In order to record information related to the design intents, we build a common CAD model ontology and then generate a semantic network to describe dependencies, component structures and design history which are components of the design intent information model. The techniques of natural language generation, namely discourse planning and sentence planning, are adopted for the eventual linguistic generation of design intents. Finally, we use several case studies to prove the advantages of natural language in helping people better understanding the design intents. [ABSTRACT FROM AUTHOR]

The literature on technology management has increasingly focused on the sociocognitive elements of the industry life cycle. One of these elements, category labels (words, in most cases) and its role in shaping market understandings, has recently become of interest to scholars. As industries evolve, stakeholders generate a plethora of category labels. However, we know relatively little about why some category labels are used repeatedly, whereas others are abandoned. Drawing on semantic networks theory, we argue that the familiarity and creativity of category labels drive their adoption. We hypothesize that low levels of familiarity hinder comprehension, but too much familiarity increases the cost of obviousness. Likewise, low levels of creativity do not trigger curiosity, whereas too much creativity spurs dissonance. We use two methods to address these hypotheses. First, we study the early smartphone industry, finding support for an inverted U-shaped relationship between both the familiarity and creativity of category labels and their adoption, even after controlling for alternative explanations, such as technology and design characteristics. Second, we find consistent results through two online experiments that broaden the scope of our study and address potential endogeneity concerns in our field data. Our paper expands the literature on the evolution of technology industries by showing that familiarity and creativity are distinct dimensions that influence the sociocognitive dynamics of an emerging industry. We also contribute to the categorization literature by theorizing about the contestation that occurs among category labels and providing empirical evidence of the factors that affect their adoption. [ABSTRACT FROM AUTHOR]

It is crucial to represent the semantic information of a document in sentiment classification. Various semantic information representation models have been proposed, however existing approaches have their setbacks. Notable weaknesses among these are: (1) tradition VSM methods, completely ignore the semantic information; (2) averaging word embedding methods, cannot depict the synthetical semantic meaning of the given document; (3) neural network methods, require complex structure and are notoriously difficult to be trained. To overcome these limitations, we introduce a simple but novel method which we call bag of meta-words (BoMW). In our method, the semantic information of the document is indicated by a meta-words vector in which every single meta-word element denotes particular semantic information. Especially, these meta-words are extracted from pre-trained word embeddings through two different but complemental models, naive interval meta-words (NIM) and feature combination meta-words (FCM). In general, our new model BoMW is as simple as traditional VSM model but it can capture the synthetical semantic meanings of the document. Numerous experiments on two benchmarks (IMDB dataset and Pang’s dataset) are carried out to verify the effectiveness of the proposed method, and the results show that the performance of our method can exceed the traditional VSM methods and methods using pre-trained word embedding. [ABSTRACT FROM AUTHOR]

Proxy signcryption means that the proxy signcrypter obtains the delegate authorization from the original signcrypter and then signcrypts the specified message on behalf of the original signcrypter. In this paper, we construct an identity-based proxy signcryption protocol (IBPSP) based on the universally composable (UC) framework. In the random oracle model, we prove that this protocol has the semantic security under the gap bilinear Diffie-Hellman and computational Diffie-Hellman assumptions. At the same time, an ideal functionality of the identity-based proxy signcryption protocol is defined in the UC security framework, and we also prove the equivalence between the universally composable identity-based proxy signcryption protocol and its IND-CCA2 and UF-CMA security. Analysis shows this IBPSP has both low computation complexity and semantic security together with UC security. [ABSTRACT FROM AUTHOR]

Currently, searchable encryption becomes the focus topic with the emerging cloud computing paradigm. The existing research schemes are mainly semantic extensions of multiple keywords. However, the semantic information carried by the keywords is limited and does not respond well to the content of the document. And when the original scheme constructs the conceptual graph, it ignores the context information of the topic sentence, which leads to errors in the semantic extension. In this paper, we define and construct semantic search encryption scheme for context-based conceptual graph (ESSEC). We make contextual contact with the central key attributes in the topic sentence and extend its semantic information, so as to improve the accuracy of the retrieval and semantic relevance. Finally, experiments based on real data show that the scheme is effective and feasible. [ABSTRACT FROM AUTHOR]

Purpose Connectivism has been proposed to explain the impact of new technologies on learning. According to this approach, learning may occur even outside the individual within an organization or a system. Learning objectives are not defined in advance and learning requires the ability to form connections and use networks to find the required knowledge. The connections by which individuals can learn are more important than what they currently know. The purpose of this paper is to investigate if a measure, rating the importance of concepts, can be derived from a network representation of the learning domain and if highly connected concepts – with high importance value – can describe whether information is explored in such ways as assumed by connectivism.Design/methodology/approach The authors empirically examined if the proposed measure can provide insight on the role of connections in learning and explain the reasons behind passing certain parts of a test using a linear regression model.Findings The results are twofold. First, an implementation of the information exploration principle of connectivism has been introduced, applying semantic technologies and the importance measure. Second, although no significant effects could be isolated, trends in performance improvement concerning highly important concepts were identified.Originality/value However, connectivism has been known since 2005, it is still lacking for successful implementations. The presented approach of a concept importance measure is a promising starting point by providing means of connected learning, enabling individuals to effectively improve their personal abilities to better fit job demand. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to zone identification which is based on considering features with high semantic richness. Out of the scenarios of selecting features for identifying a zone based on classifying the sentences in a text, we came to notice that in the scenario where specialized names belonging to a text's domain and mode of the verbs together with reduced versions of conventional features, including history, are taken into account, an accuracy rate of 61% (resp. 81%) is obtained which is higher than that belonging to Liakata's (resp. Fisas's) approach. Also, to have a genuine comparison, both Liakata's and Fisas's corpora are used in our experiments. Such accuracy is obtained at the place where less computational cost for extracting the features was decreased. In order to improve the accuracy of zone identification, a decision-level fusion process based on combining the results of separate classifiers, was considered. With regard to this, two fusion techniques of 'majority voting' and 'average of probabilities' were used. Experimentations show the fact that ensemble of 'Logistic Regression', 'Support Vector Machine' and 'Neural Network' as classifiers yields the best performance. Also 'majority voting' was shown to perform a bit better than 'average of probabilities'. [ABSTRACT FROM AUTHOR]

The heterogeneity of a network causes major challenges for link prediction in heterogeneous complex networks. To deal with this problem, supervised link prediction could be applied to integrate heterogeneous features extracted from different nodes/relations. However, supervised link prediction might be faced with highly imbalanced data issues which results in undesirable false prediction rate. In this paper, we propose a new kernel-based one-class link predictor in heterogeneous complex networks. Assuming a set of available meta-paths, a graph kernel is extracted based on each meta-path. Then, they are combined to form a single kernel function. Afterwards, one class support vector machine (OC-SVM) would be applied on the positive node pairs to train the link predictor. The proposed method has been compared with popular link predictors using DBLP network. The results show that the method outperforms other conventional link predictors in terms of prediction performances. [ABSTRACT FROM AUTHOR]

An important prerequisite for the simulation-based assessment of energy systems at urban scale is the availability of high-quality, well-formatted and semantically structured data. Unfortunately, best practices and state-of-the-art approaches for urban data modelling are hardly applied in the context of energy-related simulations, such that data management and data access often become tedious and cumbersome tasks. This paper presents the so-called Simulation Package, i.e., a data model extending the 3D City Database for CityGML, and its derived data access layer, both aiming to bridge this gap between semantic 3D city modelling and simulation in the context of urban energy systems. The feasibility of this approach is demonstrated with the help of a concrete example, where the proposed extension has been implemented and integrated into a simulation toolchain. The aim is that the availability of a common, shared data model and the proof-of-concept implementation will contribute and foster adoption and further improvement in the future. [ABSTRACT FROM AUTHOR]