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It is crucial to observe how international policy initiatives are implemented in each country's environmental policies to ensure global environmental conservation and sustainable development. This study aims to comprehensively analyze the trend of environmental policy in Korea over time. To this aim, we use semantic network analysis to explore the main keywords and to identify the characteristics of the key themes in each period based on the national environmental comprehensive plan. Our results show that the value of economic growth and technological development are regarded as priorities in Korean environmental policy because the economy and technology are closely related to the environment. Similarly, when it comes to the fundamental concept of environmental policy, the concept of "green growth" is applied in preference to the concept of "sustainability." The results contribute to expanding the approach of existing policy research by providing the potential for semantic network analysis based on policy documents. [ABSTRACT FROM AUTHOR]

Social media platforms such as Twitter provide opportunities for governments to connect to foreign publics and influence global public opinion. In the current study, we used social and semantic network analysis to investigate China's digital public diplomacy campaign during COVID-19. Our results show that Chinese state-affiliated media and diplomatic accounts created hashtag frames and targeted stakeholders to challenge the United States or to cooperate with other countries and international organizations, especially the World Health Organization. Telling China's stories was the central theme of the digital campaign. From the perspective of social media platform affordance, we addressed the lack of attention paid to hashtag framing and stakeholder targeting in the public diplomacy literature. [ABSTRACT FROM AUTHOR]

This article examines the semantic development of significant keywords in Taiwanese cultural discourse from a corpus-based inductive perspective. It contextualises these keywords ideologically and socially via extended network analysis and innovatively also via sociological analysis. The research investigated lexical patterns in what we refer to as the Taiwan Early Post-war Corpus, which consists of culturally oriented articles from 1945 to 1949. It treated shared ideologically loaded keywords as indicators of different ideologies which in turn identify the social groups who propagate them. Through this process we discovered three ideologically inclined semantic fields, which emphasise connections to the dominant cultural discourses of the central and local governments, as well as one that emphasises Taiwanese subjectivity. To understand their contemporaneous dynamics, the study examined networks associated with the authors and periodicals through shared keywords. Using the Taiwan Biographical Ontology, the study complemented ideological patterns with positional analyses of authors' social involvements, which allowed us to treat them as proxies for various types of social, cultural, economic and political capital. We could thus characterise the habitus of each cluster and its position on an ideological map, thereby creating multi-layered networks of concepts, ideologies and social patterns. [ABSTRACT FROM AUTHOR]

China's state-run media are the mouthpiece of the government. During public health crises such as the COVID-19 pandemic, they are responsible for disseminating essential information to the public on behalf of the government. This study examined the Sina Weibo posts published by three leading state-run media entities (CCTV, People's Daily, and Xinhua News Agency) during the first wave of the COVID-19 outbreak. Semantic networks were extracted from posts during each stage of the outbreak, and clusters of nodes representing communication themes were identified, including investigations of the coronavirus, governmental policies and response efforts, case updates, prevention and control, and medical treatment. These themes indicate the use of information and bolstering strategies to maintain and increase government legitimacy. The findings can inform future risk and crisis communication during public health crises. [ABSTRACT FROM AUTHOR]

Despite their drawback, multiple-choice questions are an enduring feature in instruction because they can be answered more rapidly than open response questions and they are easily scored. However, it can be difficult to generate good incorrect choices (called "distractors"). We designed an algorithm to generate distractors from a semantic network for four types of multiple choice questions in biology. By recruiting 200 participants from Amazon Mechanical Turk, the machine-generated distractors were compared to human-generated distractors in terms of question difficulty, question discrimination and distractor usefulness. The machine-generated and human-generated distractors performed very closely on all the three measures, suggesting that generating distractors from a semantic network for simple multiple choice questions is a viable method. [ABSTRACT FROM AUTHOR]

The months-long anti-extradition bill movement in Hong Kong has gained worldwide attention. Grounded in the network agenda-setting (NAS) model, this study utilizes a machine-learning approach to analyze the related coverage of mainstream newspapers in Hong Kong, Mainland China, the U.S. and the U.K. (N = 2118), as well as discussions on Twitter (N = 152,509). Network visualizations showed that each media utilized a unique approach to highlight and connect the substantive and affective attributes. Time-series analyses revealed an overall reciprocal whilst asymmetrical association between the newspapers and Twitter, in which the latter exhibited a stronger influence on the former, particularly in terms of substantive attribute agendas. Yet, Twitter's impact shrank in terms of the affective attribute agendas and the NAS models. Newspapers, though exerted rather limited impact on Twitter, maintained a certain extent of independence in setting their affective attribute agendas and NAS models. This study enriches the NAS literature through combining substantive and affective attributes in semantic networks. [ABSTRACT FROM AUTHOR]

Despite their mastery over commercially popular textbooks, many EFL learners feel communicatively incompetent in real communicative interactions outside the classroom context. This data-driven study aims at exploring the roots of this educational ill by interviewing nine experienced English language teachers teaching in private language schools of Mashhad, the capital city of Khorasan province, Iran. An initial round of data collection and analysis yielded transfer value as the missing link in ELT materials, which accounts for learners' communicative incompetence. Iterative data collection and analysis and the constant comparative technique presented a thick description of what transfer value is, related it to its underlying causes and predicted its implications for teaching, learning and materials development. Furthermore, importing the transcription of the participants' perspectives and the emerged codes and categories into MAXQDA yielded: (1) a semantic network which shows that the final conceptualisation of teachers' perspectives enjoys descriptive, explanatory and predictive adequacy; (2) a code relation browser that clearly shows the degree of overlap or concurrence of the codes; and (3) a set of charts that illustrate the degree to which the emerged categories are grounded in the participants' perspectives. The findings have clear implications for materials developers and practitioners. [ABSTRACT FROM AUTHOR]

The relative ineffectiveness of semantics-based multimedia indexing systems on the Web is caused by the semantic knowledge incompleteness and semantic heterogeneity problems. Nevertheless, the need to search multimedia documents with precision on the Web is persistently growing; pressing the demand for effective and efficient indexing strategies. In this article, we present an ontology-based multimedia indexing approach that cooperatively identifies the semantic and taxonomic relations that exist between annotation words that surround multimedia documents on Webpages. In this context, multiple ontologies are jointly employed for indexing each document. We construct inverted indexes in the form of semantic networks where nodes of each network are identified and added based on a majority-voting technique, while edges represent the semantic and taxonomic relations that hold between those nodes. To alleviate the heterogeneity between the resulting networks, we employ ontology merging algorithms to integrate them into consistent networks. We also utilise concept relatedness measures to enrich the networks with semantically-relevant entities that are not recognised by the used ontologies. To validate our proposal, we have developed a prototype system based on the proposed techniques. The produced results using real-world datasets demonstrate an improvement of the effectiveness against state-of-the-art baseline metrics. [ABSTRACT FROM AUTHOR]

A virtual enterprise (VE) involves the coordination of collaborative partners in the supply chain network. To seek the market opportunity, the VE initiator and the VE partners have to negotiate with each other to achieve mutual gains as well as ultimate customer satisfaction. Messages sent and received by the counterparts during the negotiation process are considered as negotiation knowledge. However, it is difficult for different companies involved in the VE to recognise the heterogeneous knowledge on their own, especially in an agent-based negotiation system. Thus, semantic interoperability is an important issue in a VE. The objective of this research is to propose an ontology-based knowledge representation approach to provide a semantic interoperable environment to realise automatic negotiations in the VE. The contribution of this article is twofold. On the one hand, an ontology-based knowledge representation scheme is established for VE partners' ontology construction, matching and integration. On the other hand, two agent interaction protocols are developed for the multi-agent system (MAS) to retrieve semantic correspondences and conduct bid negotiations. [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]

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]

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]

Semantically aligning the heterogeneous geospatial datasets (GDs) produced by different organizations demands efficient similarity matching methods. However, the strategies employed to align the schema (concept and property) and instances are usually not reusable, and the effects of unbalanced information tend to be neglected in GD alignment. To solve this problem, a holistic approach is presented in this paper to integrally align the geospatial entities (concepts, properties and instances) simultaneously. Spatial, lexical, structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting. The presented approach is validated with real geographical semantic webs, Geonames and OpenStreetMap. Compared with the well-known extensional-based aligning system, the presented approach not only considers more information involved in GD alignment, but also avoids the artificial parameter setting in metric aggregation. It reduces the dependency on specific information, and makes the alignment more robust under the unbalanced distribution of various information. [ABSTRACT FROM AUTHOR]

There is little research on treating symptoms of sexual orientation-obsessive-compulsive disorder (SO-OCD). Semantic networks represent a new cognitive approach for understanding cognitive mechanisms of SO-OCD. Specifically, we tested whether the self-help cognitive technique of association splitting (AS) developed from this approach would be efficacious in reducing SO-OCD symptoms and thought suppression. One hundred and twenty heterosexual undergraduates (82 females, 38 males) were randomly assigned to either the AS or waitlist control group. At baseline and four weeks later, participants completed items assessing SO-OCD symptoms, measures of sexual obsessions and thought suppression, and an association task in which they generated associations to different cue words. Generated associations were coded based on SO-OCD relevance and emotional valence. Results indicated reductions in SO-OCD-relevant associations across levels of emotional valence and SO-OCD-irrelevant negative associations, and increases in SO-OCD-irrelevant positive and neutral associations, only in the AS group. Furthermore, there were reductions in SO-OCD symptoms, sexual obsessions, and thought suppression only in the AS group. Importantly, these findings were obtained with overall large effect sizes. AS appears to be an efficacious self-help technique in reducing SO-OCD symptoms, sexual obsessions, and thought suppression. Clinical implications, limitations, and suggestions for future research are discussed. [ABSTRACT FROM AUTHOR]

In this letter, we propose a deep convolutional encoder-decoder model for remote sensing images semantic pixel labelling. Specifically, the encoder network is employed to extract the high-level semantic feature of hyperspectral images and the decoder network is employed to map the low resolution feature maps to full input resolution feature maps for pixel-wise labelling. Different from traditional convolutional layers we use a ‘dilated convolution’ which effectively enlarge the receptive field of filters in order to incorporate more context information. Also the fully connected conditional random field (CRF) is integrated into the model so that the network can be trained end-to-end. CRF can effectively improve the localization performance. Experiments on the Vaihingen and Potsdam dataset demonstrate that our model can make promising performance. [ABSTRACT FROM PUBLISHER]

News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics assemantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can ‘read’ a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 US cities spanning a 10-year period. We perform exploratory visualizations of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research. [ABSTRACT FROM PUBLISHER]

With increasing recognition of user experience (UX), it became important to identify UX issues from customers' opinions expressed in a form of text that is unstructured qualitative data. Since reliability of qualitative data has little been investigated, it requires much time and effort for researchers to define meanings and values from the qualitative data. Therefore, this article proposes a novel approach to measuring the stability of textual data by adopting semantic network analysis, as shown by our analysis on in-depth interview transcripts and online customer review data. Among the semantic networks generated from text, subnetworks were sampled from the original networks until the representativeness of each sample size was determined. Then, similarities between the subnetworks and the original network were calculated by applying a correlation analysis to determine whether stability occurs. The results indicated that variables of word frequency and network-level statistics affect stability of the network. The suggested method will be able to assist in identifying UX issues. [ABSTRACT FROM AUTHOR]

In this note, we give an example to answer affirmatively Ge-Lin’s question on the completion of partial metric spaces [3, Question 1]. We also study some related results on the completion of a partial metric space. [ABSTRACT FROM PUBLISHER]

The purpose of this study is to investigate Iran’s eLearning research status in comparison with the world. We propose a method based on a text mining approach for extracting knowledge from Iranian published articles and generating the corresponding semantic network automatically. eLearning concepts are extracted from papers published in 6 years’ proceedings of ICeLeT, an International Conference on eLearning and eTeaching, in Iran. After extracting the domain-specific concepts, each pair of concepts get the possibility to be linked together based on co-occurrence in the articles. A weight is assigned to each edge according to the pointwise mutual information value of the pair of concepts. To identify gaps between the latest local and global research, the obtained semantic network is compared with another semantic network extracted from 6 years’ proceedings of ICALT, an International Conference on Advanced Learning Technologies. By applying a hybrid clustering algorithm on two networks based on the combination of label propagation and Markov clustering, and identifying the differences between node memberships and hubs, strengths and weaknesses of each network are demonstrated. [ABSTRACT FROM PUBLISHER]

The purpose of this study is to determine the psychological meaning of coauthorship for a group of scientists, based on the assumption that the meaning of a concept is related to experience on “how a person behaves in a situation, depending on what the situation signifies to him”. The semantic meaning provides for an interpretation of action in beliefs, goals and intentions, following the idea that semantic meaning is a basis for inferring intentions to perform action. We used the Natural Semantic Networks Method. We interviewed 162 scientists in the physical and natural sciences. Results show a cluster of the semantic meaning of coauthorship in which collaboration, teamwork, work, common interest, discussion and others, are retrieved from memory and represented as a natural semantic network of the concept of coauthorship. Collaboration and teamwork are the two concepts that have the highest semantic value and this holds true for various disciplines although teamwork is the most elicited word associated to coauthorship. We propose a model in which coauthorship represents the result of the experience, attitudes and behavior of a group process concerning collaboration and teamwork in science.ABSTRACT FROM PUBLISHERCopyright of Social Epistemology is the property of Routledge 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. [ABSTRACT FROM AUTHOR]

Truly universal helper robots capable of coping with unknown, unstructured environments must be capable of spatial reasoning, i.e. establishing geometric relations between objects and locations, expressing those in terms understandable by humans. It is therefore desirable that spatial and semantic environment representations are tightly interlinked. 3D robotic mapping and the generation of consistent metric representations of space are highly useful for navigation and exploration, but they do not capture symbol-level information about the environment. This is, however, essential for reasoning, and enables interaction via natural language, which is arguably the most common and natural communication channel used and understood by humans. This article presents a review of research in three major fields relevant for this discussion of spatial reasoning and interaction. Firstly, dialogue systems are an integral part of modern approaches to situated human–robot interaction. Secondly, interactive robots must be equipped with environment representations and reasoning methods that are suitable for both navigation and task fulfillment, as well as for interaction with human partners. Thirdly, at the interface between these domains are systems that ground language in systemic environment representation and which allow the integration of information from natural language descriptions into robotic maps. For each of these areas, important approaches are outlined and relations between the fields are highlighted, and challenging applications as well as open problems are discussed. [ABSTRACT FROM AUTHOR]

Human exposure to and contamination by environmental toxic compounds generates discourses and practices that merit greater attention. In this article, we assess internal chemical contamination and the risk of toxic effects as an experience related to the production of meaning in everyday life. Drawing on the analysis of semantic networks of narratives from semi-structured interviews conducted with 43 informants in Catalonia, Spain, we consider participants’ perceptions of the health risks of toxic compounds, including social discourses on exposure, toxicity, and internal chemical contamination, and on responsibilities, consequences, and proposed strategies for controlling toxic compounds. Informants’ narratives on the relationships between nature and nurture suggest that they no longer perceive rigid boundaries separating the human body from the external environment and its chemical pollutants. [ABSTRACT FROM PUBLISHER]

This study explored how Asian communication scholarship has evolved by using semantic network analysis on keywords in titles of publications inAsian Journal of Communication(AJC) from 1990 to 2015. It also compared those results with the results of a similar analysis ofJournalism & Mass Communication Quarterly(JMCQ) to find both Western and unique aspects ofAJC.AJCscholarship has shown its Asian uniqueness by exploring social and cultural comparisons focusing on a few East Asian countries. It has also followed Western communication scholarship by investigating effects of political communication and news media, as well as advertising using television in the United States, similar toJMCQ.AJCcontributes to mainstream mass communication scholarship while keeping its own Asian perspectives. [ABSTRACT FROM PUBLISHER]

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data. [ABSTRACT FROM PUBLISHER]

Social media rumors are improvised and expressive forms of public opinion that especially arise under uncertain sociopolitical situations. This study utilizes early rumor studies as theoretical framework for textual analysis of Twitter public opinion. A content and semantic network analysis of Twitter messages spread during Korean saber rattling in 2013 was conducted for understanding public opinion in an uncertain context. The results show that, while non-rumor narratives focus on policy-level responses to the threat situation in a similar manner to institutionalized opinion polling, rumors are less concerned with official responses, instead reflective of hegemonic tensions between anti-leftwing political sentiments and the counteractive accounts. Some rumors reveal the public's coping with fears in the form of humor, guesswork, or wishes. Online rumor analysis helps understand how the society's collective memories interact with the current situational uncertainty in shaping public opinions and sentiments. [ABSTRACT FROM AUTHOR]

‘Sharing’ as it relates to the online environment is underconceptualized, and yet has been proposed as a means for understanding how individuals negotiate everyday privacy. To explore this possibility, we gather reader comments to online news accounts, as these offer an opportunity for observing everyday discourse. Using semantic network analysis, we map related concepts, and use these as a basis for revisiting the concept of ‘sharing' as it pertains to the digital sphere. We argue that while ‘sharing' continues to encompass traditional notions of communality and distribution, as practiced in digital spaces, it also takes on an added dimension of subjectivity. Consonant with Foucault's (1988) ‘technologies of the self’, sharing online becomes a reflexive mechanism to know and care for oneself. By considering ‘sharing' in this light, we aim to further the conversation of counter-posing sharing to privacy, especially when envisioned as a boundary management process. [ABSTRACT FROM PUBLISHER]

More than fifty years after the first edition of Thompson's seminalMotif-Indexof Folk Literature, we present an online search engine tailored to fully disclose the index digitally. This search engine, called MOMFER, greatly enhances the searchability of theMotif-Indexand provides exciting new ways to explore the collection. This is enabled by our use of modern techniques from both natural language processing and information retrieval. The key feature of the search tool is the way in which it allows users to search theMotif-Indexfor semantic concepts, such as ‘mythical animals’, ‘mortality’, or ‘emotions’. This paper will explain the motivations for creating the search tool, explicate the production process, and show in a number of case studies how the search tool can be used to explore the index in innovative ways. [ABSTRACT FROM PUBLISHER]

In this article, we present a novel approach utilizing Decisional DNA to help the Internet of Things capture decisional events and reuse them for decision making in future operations. The Decisional DNA is a domain-independent, standard and flexible knowledge representation structure that allows its domains to acquire, store, and share experiential knowledge and formal decision events in an explicit way. We apply this approach to our current work—SmartBike, a sensor-equipped bicycle built under the concept of Internet of Things. By using Decisional DNA and machine learning algorithms, the SmartBike is able to distinguish its user's patterns based on past riding data. The presented conceptual approach demonstrates how Decisional DNA can be applied to the Internet of Things and bring to them intelligence required by forthcoming semantic networks. [ABSTRACT FROM PUBLISHER]

Prior neuropsychological evidence suggests that semantic and phonological components of short-term memory (STM) are functionally and neurologically distinct. The current paper examines proactive interference (PI) from semantic and phonological information in two STM-impaired patients, DS (semantic STM deficit) and AK (phonological STM deficit). In Experiment 1 probe recognition tasks with open and closed sets of stimuli were used. Phonological PI was assessed using nonword items, and semantic and phonological PI was assessed using words. In Experiment 2 phonological and semantic PI was elicited by an item recognition probe test with stimuli that bore phonological and semantic relations to the probes. The data suggested heightened phonological PI for the semantic STM patient, and exaggerated effects of semantic PI in the phonological STM case. The findings are consistent with an account of extremely rapid decay of activated type-specific representations in cases of severely impaired phonological and semantic STM. [ABSTRACT FROM PUBLISHER]

The open service network for marine environmental data (NETMAR) project uses semantic web technologies in its pilot system which aims to allow users to search, download and integrate satellite, in situ and model data from open ocean and coastal areas. The semantic web is an extension of the fundamental ideas of the World Wide Web, building a web of data through annotation of metadata and data with hyperlinked resources. Within the framework of the NETMAR project, an interconnected semantic web resource was developed to aid in data and web service discovery and to validate Open Geospatial Consortium Web Processing Service orchestration. A second semantic resource was developed to support interoperability of coastal web atlases across jurisdictional boundaries. This paper outlines the approach taken to producing the resource registry used within the NETMAR project and demonstrates the use of these semantic resources to support user interactions with systems. Such interconnected semantic resources allow the increased ability to share and disseminate data through the facilitation of interoperability between data providers. The formal representation of geospatial knowledge to advance geospatial interoperability is a growing research area. Tools and methods such as those outlined in this paper have the potential to support these efforts. [ABSTRACT FROM AUTHOR]

In this article, we propose a method to find corresponding object-set pairs between image and map polygon object data sets by means of latent semantic analysis. Latent semantic analysis assigns each polygon object of both data sets to feature vectors in a continuous geometric space in which the similarities between the vectors are proportional to the priorities to constitute a corresponding object-set pair. Thus, object clusters can be obtained by applying an agglomerative hierarchical clustering to the feature vectors. These object clusters are separated into object-set pairs according to the data sets to which the objects belong and are evaluated with a geometric matching criterion to find corresponding object-set pairs. We applied the proposed method to the segmentation result of a composite image with six normalized difference vegetation index (NDVI) images and a forest inventory map. The proposed method was compared to a graph-embedding-based method. The results showed that the proposed method found more corresponding object-set pairs with a similar accuracy in terms of shape similarities and shared information of found pairs. [ABSTRACT FROM AUTHOR]

This paper presents work on retrofitting of high-tech systems (HTS) in land-based aquaculture system for improving production efficiency. The objective is to develop architecture for an automated system for aquaculture farming. We call this development an automated expert system architecture (AESA). The article has been organized into four parts. First, it reviews the background and business opportunity on aquaculture, which includes fish and seaweed farming. We have proposed a six-layer model for the development of a fully contained total automation system along with approaches and methods. The developed software platform was tested with a laboratory based demonstration rig. [ABSTRACT FROM PUBLISHER]

Tests of divergent thinking represent the most commonly used assessment of creative potential. Typically they are scored for total ideational output (fluency), ideational originality, and, sometimes, ideational flexibility. That scoring system provides little information about the underlying process and about the associations among ideas. It also does not really capture the key principle of divergent thinking, namely that ideas may be found when cognition explores new (divergent) directions. The investigation reported here used 3 independent semantic networks, each computerized and previously validated, to quantify the distance between responses (ideas) to several tests of divergent thinking. These sources were WordNet (WN), Word Associations Network (WAN), and IdeaFisher (IF). Statistical analyses indicated that remote and close associations can be reliably measured when different sources of associative strength are used. Inter-item reliability (alpha coefficients) of what these networks had identified as remote associations were higher than those from close associations. Inter-item reliability values were higher in the WAN and IF, which provided shorter lists than the WN. Therefore, longer associative lists did not necessarily produce better indices of remote and close associations. Also, scores from a measure of creative attitudes and values were significantly correlated with remote, but not with close, associations across all 3 networks. This finding is very important because it shows that people with a higher tendency of creative attitudes and values, as measured by divergent thinking tests, are more likely to make remote associations rather than close associations. Limitations and future directions are discussed. [ABSTRACT FROM AUTHOR]

Troyer and colleagues [Troyer, A. K., Moscovitch, M., & Winocur, G. (1997). Clustering and switching as two components of verbal fluency: evidence from younger and older healthy adults.Neuropsychology, 11(1), 138–146] developed a seminal method to measure clustering and switching behaviors during verbal fluency (VF) productions. We sought to expand the reach of their system by modifying the scoring rules. Compared to the Troyer system, our modifications yield comparable estimates of interrater reliability and similar patterns of correlation with demographic characteristics for both clustering and switching in healthy adults. However, two objective measures of word relatedness (interword interval timing and latent semantic analysis) confirm that our revisions capture additional information about the organization of entries in the lexical network. [ABSTRACT FROM PUBLISHER]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd 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.)

Communication network research is increasingly being used across the communication discipline. However, most social network research is limited in its generalizability because it focuses on either a single network case or ego-centric network data. In order to generate knowledge across network studies, a mechanism is needed to synthesize. This chapter presents a taxonomy of communication relations, differentiating between communication flow, affinity, representational, and semantic networks. Then, it demonstrates the utility of the taxonomy for synthesizing network research by reviewing 139 studies in communication journals, focusing on the antecedents, outcomes, and processes of each type of network. [ABSTRACT FROM AUTHOR]

This article examines the variability of produsage (the hybridization of production and consumption) within popular social media by introducing semantic network analysis of information communication on Twitter. Utilizing user data (n[2,254,806]), the study examines the dynamics of produsage (a) as a function of user activity and (b) whether produsage levels vary based on either ‘soft’ or ‘hard’ news information. The findings extend upon past socio-ethnographic studies of social media produsage and demonstrate that produsage is significantly low for low-activity users (the majority) but increases dramatically with user activity becoming the dominant communication modality for users with 5,000–25,000 status messages. The volume of soft news information is over double that of hard news information, while produsage networks vary only slightly based on the type of content distributed with ‘hard’ news information tending toward production bias and ‘soft’ news information toward consumption bias. [ABSTRACT FROM PUBLISHER]

In public discourse, meaning is constantly renegotiated. Frames and other semantic structures are co-constructed in the public debate based on the contributions of many discourse participants. Over time, they incorporate new information and interpretations. As a result, time-dependent changes occur both on the level of manifest contributions and on the level of latent structures organizing discourse into meaningful frames. This article introduces a technique capable of analyzing the changing patterns of meaning in a genuinely dynamic fashion. It applies Evolutionary Factor Analysis (EFA), a recently developed technique for treating high-dimensional data with time-changing latent structure. Using EFA, we uncover evolving patterns on different levels of abstraction within our data, which represent discourse as a detailed semantic network. We investigate specific dynamics expected within dynamic discourse (e.g., emergence, evolution, consolidation, crisis) and analyze the time-changing structure and content of meaning. The methodological innovation presented in this paper allows a detailed analysis of micro-level changes organized by latent higher-level structures: It can be transferred to a variety of social phenomena organized by structures that evolve over time (e.g., public opinion, social interaction). Rendering their dynamic behavior accessible to statistical analysis, it offers new theoretical insights into their mechanics and underlying structure. [ABSTRACT FROM AUTHOR]

Each of the FR family of models has been represented in Resource Description Framework (RDF), the basis of the Semantic Web. This has involved analysis of the entity-relationship diagrams and text of the models to identify and create the RDF classes, properties, definitions, and scope notes required. The work has shown that it is possible to seamlessly connect the models within a semantic framework, specifically in the treatment of names, identifiers, and subjects, and link the RDF elements to those in related namespaces. [ABSTRACT FROM PUBLISHER]

Different versions of the Web Coverage Service (WCS) schemas of the Open Geospatial Consortium (OGC) reflect semantic conflict. When applying the extended FRAG-BASE schema-matching approach (a schema-matching method based on COMA++, including an improved schema decomposition algorithm and schema fragments identification algorithm, which enable COMA++-based support to OGC Web Service schema matching), the average recall of WCS schema matching is only 72%, average precision is only 82% and average overall is only 57%. To improve the quality of multi-version WCS retrieval, we propose a schema-matching method that measures node semantic similarity (NSS). The proposed method is based on WordNet, conjunctive normal form and a vector space model. A hybrid algorithm based on label meanings and annotations is designed to calculate the similarity between label concepts. We translate the semantic relationships between nodes into a propositional formula and verify the validity of this formula to confirm the semantic relationships. The algorithm first computes the label and node concepts and then calculates the conceptual relationship between the labels. Finally, the conceptual relationship between nodes is computed. We then use the NSS method in experiments on different versions of WCS. Results show that the average recall of WCS schema matching is greater than 83%; average precision reaches 92%; and average overall is 67%. [ABSTRACT FROM AUTHOR]

Connectionist language modelling typically has difficulty with syntactic systematicity, or the ability to generalise language learning to untrained sentences. This work develops an unsupervised connectionist model of infant grammar learning. Following the semantic boostrapping hypothesis, the network distils word category using a developmentally plausible infant-scale database of grounded sensorimotor conceptual representations, as well as a biologically plausible semantic co-occurrence activation function. The network then uses this knowledge to acquire an early benchmark clausal grammar using correlational learning, and further acquires separate conceptual and grammatical category representations. The network displays strongly systematic behaviour indicative of the general acquisition of the combinatorial systematicity present in the grounded infant-scale language stream, outperforms previous contemporary models that contain primarily noun and verb word categories, and successfully generalises broadly to novel untrained sensorimotor grounded sentences composed of unfamiliar nouns and verbs. Limitations as well as implications to later grammar learning are discussed. [ABSTRACT FROM AUTHOR]

Research suggests that people support underdogs. Three studies examined how laypeople conceptualize the underdog label. Study 1 used a free association method to create a semantic network map of the underdog construct. In Study 2, participants provided their own definitions and selected the entity that best exemplified an underdog. In the two studies, the underdog term was linked to both disadvantage and successful qualities. In Study 3, participants read fictional stories about competing entities unlikely to succeed. When targets were labeled as underdogs, participants predicted that they would perform significantly better than expectations. We suggest that, although dictionaries define underdogs as unlikely to prevail, a layperson's conceptions, shaped by inspirational archetypal stories of odds overcome, are more optimistic. [ABSTRACT FROM AUTHOR]

The study of information based on the approach of Shannon was detached from problems of meaning. Also, it did not allow analysis of the structural characteristics of information, nor describe the way structures carry information. An outline of a different theory of information, including its semantics, was earlier proposed by the author. This theory was using closure spaces to model information. In the present paper, structures (called syllogistics) underlying syllogistic reasoning as well as ethnoscientific classifications are identified together with the conditions for the lattice of closed subsets describing information to allow its existence. The structures can be used for logical analysis outside of language at the more general level of information, which in turn can be applied to the description of semantic relations in the context of information. [ABSTRACT FROM AUTHOR]

In the scientific literature and popular media, the five terms suicide bomber, suicide terrorist, Islamic martyr, martyr or volunteer are often used interchangeably. This study investigates the mental representations related to these five different terms when the terms are presented individually and when they are embedded in the context of two scenarios. Participants were 129 undergraduate students. It was hypothesized that: (1) the positive and negative valence of associations to the five target words would differ; (2) the perception of a suicide terrorist would be influenced by target word used and by the context of the act, such as the kind of victims; (3) the reasons given for why a person becomes a suicide bomber would reflect more often those discussed in scientific research and less often those commonly presented in the media; and (4) the reasons given would differ between target words and context. Results mostly confirmed the hypotheses. Although context information seems to outweigh the connotations of the individual terms, results caution researchers and media on the need to reflect on possible consequences resulting from the use of specific terms. [ABSTRACT FROM AUTHOR]

Foreign language (FL) education has been marked by a monolingual principle that has favoured 'intralingual' methodologies. Bakhtin's view of language interillumination - that languages throw light on each other - challenges such language teaching practices radically. Using conversation analysis methods, this article examines transcripts of interactional sequences from one eighth-grade French lesson for evidence of reciprocal lexical elucidation. Analysis suggests that participants accomplish interillumination by constructing frameworks of corresponding sets of French and Swedish lexical items which support interlingual exploration and participant (re)orientation to the words in relation to each other. Understanding foreign and familiar words is enhanced by bilingual countering which enables students to fit an unknown or unclear utterance meaningfully into their semantic networks. Patterns of interlingual contact and coordination, indeed bilingual interaction, serve sense-making processes and challenge the privileging or the subordination of any one language in language learning. [ABSTRACT FROM AUTHOR]