Tagging Behavior Research Articles

Improving the Hydrodynamic Performance of Underwater Tags for Blue Shark Monitoring

The use of tag devices in marine environments has become indispensable in attaining a better understanding of marine life and contributing to conservation efforts. However, the successful deployment and operation of underwater tags both depend significantly on their hydrodynamic characteristics, particularly their resistance to motion and stability in various environmental conditions. Herein, a comprehensive study on the hydrodynamic characteristics and optimization of an underwater tag designed for monitoring blue sharks is presented. Firstly, a validation process is conducted by comparing the computational fluid dynamics (CFD) results with the experimental data from Myring’s study, focusing on the resistance characteristics of the tag’s body and the impact of various operational conditions. Subsequently, the validated CFD model is applied to assess the hydrodynamic performance of the tag under different flow conditions, velocities, and angles of attack. Through iterative simulations, including mesh independence studies and boundary condition adjustments, the study identifies key parameters influencing the tag’s resistance and stability. Furthermore, the paper proposes and implements design modifications, including the incorporation of stabilizing fins, aimed at minimizing resistance and improving the tag’s equilibrium position. The effectiveness of these design enhancements is demonstrated through a comparative analysis of resistance and pitching moments for both preliminary and optimized tag configurations. Overall, the study provides valuable insights into the hydrodynamic behavior of underwater tags and offers practical recommendations for optimizing their design to minimize interference with the movement of tagged marine animals.

A Study of DNN-Based Media Literacy and Distance Education Management System in the Context of Participatory Culture.

Through an in-depth study and analysis of the integration of media literacy with distance education management using the DNN algorithm in the context of participatory culture, and the design of a distance education management system for application in actual teaching, the word embedding model is used to embed the ratings and tags, respectively; then, the self-encoder is used to extract textual features for item tags, while DNN is used to extract features for user tagging behaviors; finally, the fully connected neural network is used to predict ratings for the fused user and item features. The experimental results show that the optimized recommendation model effectively reduces the user rating prediction error while alleviating the cold start problem. A blockchain-based learning platform is designed. The purpose is to solve the contradiction between centralized storage and social trust, and to transfer the management of educational certification resources from centralized control management to distributed control management. This platform can record in stages a variety of verification information and course certificate information collected by students in the process of learning courses, create course certificates containing digital summaries of learning information, and store them on the chain to ensure the integrity and traceability of data in the learning process. This paper implements a blockchain-based online education system, which ensures that system data will not be maliciously queried and dynamically changed by management.

An IoT-Aware Smart System Exploiting the Electromagnetic Behavior of UHF-RFID Tags to Improve Worker Safety in Outdoor Environments

Recently, different solutions leveraging Internet of Things (IoT) technologies have been adopted to avoid accidents in agricultural working environments. As an example, heavy vehicles, e.g., tractors or excavators, have been upgraded with remote controls. Nonetheless, the community continues to encourage discussions on safety issues. In this framework, a localization system installed on remote-controlled farm machines (RCFM) can help in preventing fatal accidents and reduce collision risks. This paper presents an innovative system that exploits passive UHF-RFID technology supported by commercial BLE Beacons for monitoring and preventing accidents that may occur when ground-workers in RCFM collaborate in outdoor agricultural working areas. To this aim, a modular architecture is proposed to locate workers, obstacles and machines and guarantees the security of RCFM movements by using specific notifications for ground-workers prompt interventions. Its main characteristics are presented with its main positioning features based on passive UHF-RFID technology. An experimental campaign discusses its performance and determines the best configuration of the UHF-RFID tags installed on workers and obstacles. Finally, system validation demonstrates the reliability of the main components and the usefulness of the proposed architecture for worker safety.

Variation in Release Factor Abundance Is Not Needed to Explain Trends in Bacterial Stop Codon Usage.

In bacteria stop codons are recognized by one of two class I release factors (RF1) recognizing TAG, RF2 recognizing TGA, and TAA being recognized by both. Variation across bacteria in the relative abundance of RF1 and RF2 is thus hypothesized to select for different TGA/TAG usage. This has been supported by correlations between TAG:TGA ratios and RF1:RF2 ratios across multiple bacterial species, potentially also explaining why TAG usage is approximately constant despite extensive variation in GC content. It is, however, possible that stop codon trends are determined by other forces and that RF ratios adapt to stop codon usage, rather than vice versa. Here, we determine which direction of the causal arrow is the more parsimonious. Our results support the notion that RF1/RF2 ratios become adapted to stop codon usage as the same trends, notably the anomalous TAG behavior, are seen in contexts where RF1:RF2 ratios cannot be, or are unlikely to be, causative, that is, at 3′untranslated sites never used for translation termination, in intragenomic analyses, and across archaeal species (that possess only one RF1). We conclude that specifics of RF biology are unlikely to fully explain TGA/TAG relative usage. We discuss why the causal relationships for the evolution of synonymous stop codon usage might be different from those affecting synonymous sense codon usage, noting that transitions between TGA and TAG require two-point mutations one of which is likely to be deleterious.

Epidemic Intelligence for the Crowd, by the Crowd

Tracking Twitter for public health has shown great potential. However, most recent work has been focused on correlating Twitter messages to influenza rates, a disease that exhibits a marked seasonal pattern. In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capacity? In May 2011, Germany reported an outbreak of Enterohemorrhagic Escherichia coli (EHEC). It was one of the largest described outbreaks of EHEC worldwide and the largest in Germany. In this work, we study the crowd's behavior in Twitter during the outbreak. In particular, we report how tracking Twitter helped to detect key user messages that triggered signal detection alarms before MedISys and other well established early warning systems. We also introduce a personalized learning to rank approach that exploits the relationships discovered by: (i) latent semantic topics computed using Latent Dirichlet Allocation (LDA), and (ii) observing the social tagging behavior in Twitter, to rank tweets for epidemic intelligence. Our results provide the grounds for new public health research based on social media.

Extensive fault emulation on RFID tags for fault tolerance and security evaluation

Radio frequency identification (RFID) is widespread and still necessary in many important applications. However, and in various significant cases, the use of this technology faces multiple security issues that must be addressed. This is mainly related to the use of RFID tags (transponders) which are electronic components communicating wirelessly, and hence they are vulnerable to multiple attacks through several means. Thus, it is challenging to evaluate the hardness of such devices. To tackle this problem, an extensive fault analysis is performed on an ultra-high frequency (UHF) tag architecture. Tens of millions of single-bit upsets (SBUs) and multiple-bit upsets (MBUs) faults have been emulated randomly on this tag architecture using an FPGA (field programmable gate arrays)-based emulation platform. The emulated faults have been classified into five groups according to faults effect on the tag behavior. Then, we propose a classification of emulated fault in function of MBU size and an error analysis with the aim of distinguishing between major and minor errors. Thus, experimentation results allowed a thorough evaluation of the tag robustness against MBUs. The proposed approach stands for an efficient means for studying tag architectures at the design level and evaluating their robustness and vulnerability to fault attacks and disturbances of harsh environments.

The effect of multilingual suggested tags on cross-language information tagging behaviour

Purpose Suggested tag was considered as one of the critical factors affecting a user’s tagging behaviour. However, compared to the findings on the suggested tags for the monolingual environment, it still lacks focused studies on the tag suggestions for cross-language information. Therefore, this paper aims to concern with annotation behaviour and psychological cognition in the cross-language environment when suggested tags are provided. Design/methodology/approach A cross-language tagging experiment was conducted to explore the impact of suggested tags on the tagging results and process. The descriptive statistics of tags, the sources and semantic relations of tags, as well as the user’s psychological cognition were all measured in the test. Findings The experimental results demonstrated that the multilingual suggested tags could bring some costs to a user’s tagging perception. Furthermore, the language factor of suggested tags led to different paths of tagging imitation (reflected by longer semantic mapping and imitation at the visual level) and different cognitive processes (topic extraction and inference process). Originality/value To the best of the authors’ knowledge, this study is one of the first to emphasize the effect of suggested tags during multilingual tagging. The findings will enrich the theories of user-information interaction in the cross-language environment and, in turn, provide practical implications for tag-based information system design.

Solidification behavior of acylglycerols in fatty acid methyl esters and effects on the cold flow properties of biodiesel

AbstractThe formation of precipitates in biodiesel (comprising fatty acid methyl esters [FAMEs] obtained from plant oils) can lead to the clogging of fuel filters. Such precipitates are often caused by the solidification of acylglycerols (monoacylglycerols [MAGs], diacylglycerols [DAGs], and triacylglycerols [TAGs]) that have higher melting points than FAMEs. Based on our prior study on the solidification behavior of MAG/FAME binary mixtures, the present work investigated the behavior of various DAGs and TAGs combined with FAMEs. Differential scanning calorimetry was used to clarify the effects of acylglycerols on the cold‐flow properties of biodiesel. When DAGs and TAGs were added to FAMEs, the liquidus temperatures (above which the mixtures were completely liquid) increased steeply even at low concentrations. This same behavior was observed previously in trials with MAGs, indicating that all acylglycerols readily precipitate in combination with FAMEs. However, thermodynamic analyses established that the reasons for such precipitation were different for different compounds. MAGs precipitates because they contain two hydroxyl groups and therefore have a low affinity for FAMEs. In contrast, TAGs precipitates as a result of their high enthalpies of fusion (which in turn are caused by high molecular weights), while both factors affect the precipitation of DAGs. A non‐solid‐solution thermodynamic model that assumes a eutectic system was found to accurately predict the liquidus temperatures of binary and multicomponent mixtures containing various acylglycerols with FAMEs.

Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study

BackgroundThe use of remote data capture for monitoring blood glucose and supporting digital apps is becoming the norm in diabetes care. One common goal of such apps is to increase user awareness and engagement with their day-to-day health-related behaviors (digital engagement) in order to improve diabetes outcomes. However, we lack a deep understanding of the complicated association between digital engagement and diabetes outcomes.ObjectiveThis study investigated the association between digital engagement (operationalized as tagging of behaviors alongside glucose measurements) and the monthly average blood glucose level in persons with type 2 diabetes during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first 6 months, blood glucose levels will drop faster and further in patients with increased digital engagement and that difference in outcomes will persist for the remainder of the year. Finally, we hypothesize that disaggregated between- and within-person variabilities in digital engagement will predict individual-level changes in blood glucose levels.MethodsThis retrospective real-world analysis followed 998 people with type 2 diabetes who regularly tracked their blood glucose levels with the Dario digital therapeutics platform for chronic diseases. Subjects included “nontaggers” (users who rarely or never used app features to notice and track mealtime, food, exercise, mood, and location, n=585) and “taggers” (users who used these features, n=413) representing increased digital engagement. Within- and between-person variabilities in tagging behavior were disaggregated to reveal the association between tagging behavior and blood glucose levels. The associations between an individual’s tagging behavior in a given month and the monthly average blood glucose level in the following month were analyzed for quasicausal effects. A generalized mixed piecewise statistical framework was applied throughout.ResultsAnalysis revealed significant improvement in the monthly average blood glucose level during the first 6 months (t=−10.01, P<.001), which was maintained during the following 6 months (t=−1.54, P=.12). Moreover, taggers demonstrated a significantly steeper improvement in the initial period relative to nontaggers (t=2.15, P=.03). Additional findings included a within-user quasicausal nonlinear link between tagging behavior and glucose control improvement with a 1-month lag. More specifically, increased tagging behavior in any given month resulted in a 43% improvement in glucose levels in the next month up to a person-specific average in tagging intensity (t=−11.02, P<.001). Above that within-person mean level of digital engagement, glucose levels remained stable but did not show additional improvement with increased tagging (t=0.82, P=.41). When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels (t=1.30, P=.20).ConclusionsThis study sheds light on the source of the association between user engagement with a diabetes tracking app and the clinical condition, highlighting the importance of within-person changes versus between-person differences. Our findings underscore the need for and provide a basis for a personalized approach to digital health.

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item''s neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

Tagging Items Automatically Based on Both Content Information and Browsing Behaviors

Tags have been adopted by many online services as a method to manage their online resources. Effective tagging benefits both users and firms. In real applications providing a user tagging mechanism, only a small portion of tags are usually provided by users. Therefore, an automatic tagging method, which can assign tags to different items automatically, is urgently needed. Previous works on automatic tagging focus on exploring the tagging behavior of users or the content information of items. In online service platforms, users frequently browse items related to their interests, which implies users’ judgment about the underlying features of items and is helpful for automatic tagging. Browsing-behavior records are much more plentiful compared with tagging behavior and easy to collect. However, existing studies about automatic tagging ignore this kind of information. To properly integrate both browsing behaviors and content information for automatic tagging, we propose a novel probabilistic graphical model and develop a new algorithm for the model parameter inference. We conduct thorough experiments on a real-world data set to evaluate and analyze the performance of our proposed method. The experimental results demonstrate that our approach achieves better performance than state-of-the-art automatic tagging methods. Summary of Contribution. In this paper, we study how to automatically assign tags to items in an e-commerce background. Our study is about how to perform item tagging for e-commerce and other online service providers so that consumers can easily find what they need and firms can manage their resources effectively. Specifically, we study if consumer browsing behavior can be utilized to perform the tagging task automatically, which can save efforts of both firms and consumers. Additionally, we transform the problem into how to find the most proper tags for items and propose a novel probabilistic graphical model to model the generation process of tags. Finally, we develop a variational inference algorithm to learn the model parameters, and the model shows superior performance over competing benchmark models. We believe this study contributes to machine learning techniques.

The effects of suggested tags and autocomplete features on social tagging behaviors

AbstractMany websites employ social tagging to allow users to label and classify information. These tagging user interfaces use a variety of features to support efficient and consistent tag creation, including suggested tags and autocomplete for tags. This study uses a custom‐built tagging interface in a controlled experiment to determine how these features affect social tagging behavior. The study finds that suggested tags do not have a significant effect on the number of tags, number of unique tags, number of typos, or time elapsed per tagged provided. However, autocomplete significantly increases the number and consistency of tags provided, significantly decreases the rate of typos, and significantly decreases the elapsed time per tag provided. These findings for the autocomplete feature align with the priorities and constraints of social tagging folksonomies that support retrieval and site navigation and suggest that autocomplete is an important aid for text entry in social tagging user interfaces.

AIRec: Attentive intersection model for tag-aware recommendation

Tag-aware recommender systems (TRS) utilize rich tagging information to better depict user portraits and item features. Recently, many efforts have been done to improve TRS with neural networks. However, existing methods construct user representations through either explicit tagging behaviors or implicit interacted items, which is inadequate to capture multi-aspect user preferences. Besides, there are still lacks of investigation about the intersection between user and item tags, which is crucial for better recommendation.In this paper, we propose AIRec, an attentive intersection model for TRS, to address the above issues. More precisely, we first project the sparse tag vectors into a latent space through multi-layer perceptron (MLP). Then, the user representations are constructed with a hierarchical attention network, where the item-level attention differentiates the contributions of interacted items and the preference-level attention discriminates the saliencies between explicit and implicit preferences. After that, the intersection between user and item tags is exploited to enhance the learning of conjunct features. Finally, the user and item representations are concatenated and fed to factorization machines (FM) for score prediction. We conduct extensive experiments on two real-world datasets, demonstrating significant improvements of AIRec over state-of-the-art methods for tag-aware top-n recommendation.

Hierarchical attention model for personalized tag recommendation

AbstractWith the development of Web‐based social networks, many personalized tag recommendation approaches based on multi‐information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user‐item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real‐world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.

Adversarial Learning for Personalized Tag Recommendation

We have recently seen great progress in image classification due to the success of deep convolutional neural networks and the availability of large-scale datasets. Most of the existing work focuses on single-label image classification. However, there are usually multiple tags associated with an image. The existing works on multi-label classification are mainly based on lab curated labels. Humans assign tags to their images differently, which is mainly based on their interests and personal tagging behavior. In this paper, we address the problem of personalized tag recommendation and propose an end-to-end deep network which can be trained on large-scale datasets. The user-preference is learned within the network in an unsupervised way where the network performs joint optimization for user-preference and visual encoding. A joint training of user-preference and visual encoding allows the network to efficiently integrate the visual preference with tagging behavior for a better user recommendation. In addition, we propose the use of adversarial learning, which enforces the network to predict tags resembling user-generated tags. We demonstrate the effectiveness of the proposed model on two different large-scale and publicly available datasets, YFCC100 M and NUS-WIDE. The proposed method achieves significantly better performance on both the datasets when compared to the baselines and other state-of-the-art methods. The code is publicly available at https://github.com/vyzuer/ALTReco.

User Tagging Behaviors in an OPAC: An Analysis of Seven Years of I-Share User Tags

User tagging services are under-used in cultural heritage institutions despite existing for over a decade. This study considers seven years of user tags from university and public institutions by comparing service usage between institution types and qualitatively analyzing a selection of tags from the University of Illinois at Urbana-Champaign. Researchers found that, overall, few users are tagging items in the online catalogs, but tags being created are largely descriptive in nature, indicating the potential to improve discoverability for under-described materials. With improved education on their use and purpose, tagging and annotation services can become invaluable resources for cultural heritage institutions.

User Tagging Behaviors in an OPAC: An Analysis of Seven Years of I-Share User Tags

User tagging services are under-used in cultural heritage institutions despite existing for over a decade. This study considers seven years of user tags from university and public institutions by comparing service usage between institution types and qualitatively analyzing a selection of tags from the University of Illinois at Urbana-Champaign. Researchers found that, overall, few users are tagging items in the online catalogs, but tags being created are largely descriptive in nature, indicating the potential to improve discoverability for under-described materials. With improved education on their use and purpose, tagging and annotation services can become invaluable resources for cultural heritage institutions.

Modelling and analysis of tagging networks in Stack Exchange communities

AbstractLarge Question-and-Answer (Q&amp;A) platforms support diverse knowledge curation on the Web. While researchers have studied user behaviour on such platforms in a variety of contexts, there is relatively little insight into important by-products of user behaviour that also encode knowledge. Here, we analyse and model the macroscopic structure of tags applied by users to annotate and catalogue questions, using a collection of 168 Stack Exchange websites that span a diversity of sizes and topics. We study the distribution of tag frequencies and also the structure of ‘co-tagging’ networks where nodes are tags and links connect tags that have been applied to the same question. We find striking similarity in tagging structure across Stack Exchange communities, even though each community evolves independently (albeit under similar guidelines). Our findings thus provide evidence that social tagging behaviour is largely driven by the Stack Exchange platform itself and not by the individual Stack Exchange communities. We also develop a simple generative model that creates random bipartite graphs of tags and questions. Our model accounts for the tag frequency distribution but does not explicitly account for co-tagging correlations. Even under these constraints, we demonstrate empirically and theoretically that our model can reproduce a number of the statistical properties that characterize co-tagging networks.

Quantifying gendered participation in OpenStreetMap: responding to theories of female (under) representation in crowdsourced mapping

This paper presents the results of an exploratory quantitative analysis of gendered contributions to the online mapping project OpenStreetMap (OSM), in which previous research has identified a strong male participation bias. On these grounds, theories of representation in volunteered geographic information (VGI) have argued that this kind of crowdsourced data fails to embody the geospatial interests of the wider community. The observed effects of the bias however, remain conspicuously absent from discourses of VGI and gender, which proceed with little sense of impact. This study addresses this void by analysing OSM contributions by gender and thus identifies differences in men’s and women’s mapping practices. An online survey uniquely captured the OSM IDs as well as the declared gender of 293 OSM users. Statistics relating to users’ editing and tagging behaviours openly accessible via the ‘how did you contribute to OSM’ wiki page were subsequently analysed. The results reveal that volumes of overall activity as well editing and tagging actions in OSM remain significantly dominated by men. They also indicate subtle but impactful differences in men’s and women’s preferences for modifying and creating data, as well as the tagging categories to which they contribute. Discourses of gender and ICT, gender relations in online VGI environments and competing motivational factors are implicated in these observations. As well as updating estimates of the gender participation bias in OSM, this paper aims to inform and stimulate subsequent discourses of gender and representation towards a new rationale for widening participation in VGI.

A Study on Large-scale Social Tagging Behavior of User in Online Social Network

Online social networks equally referred as Facebook, Twitter, Google+, LinkedIn and Foursquare have become majorly democratic all over the world. A social network represents a significant role in people’s daily life by building social relations among people. Nowadays people can access these social networks using both desktop PCs and new emerging mobile devices. With billions of users worldwide, Online Social Networks are a new venue of innovation with many challenging research problems. An understanding of user interactions in OSNs can provide important insights into questions of human social behavior and the pattern of social platforms and applications. In this paper, a study on user behavior in OSNs from several perspectives like connectivity and interaction among users, traffic activity, social tagging, mobile social behavior is considered. Also, this paper study malicious behaviors of OSN users and discuss several solutions to detect misbehaving users. This paper explores existing research highlights and provides various needs of significant research in these topics.