Network Of Tags Research Articles

Enhanced Agricultural Vehicle Positioning through Ultra-Wideband-Assisted Global Navigation Satellite Systems and Bayesian Integration Techniques

This paper introduces a cooperative positioning algorithm for agricultural vehicles, which uses the relative distance of the workshop to improve the performance of the Global Navigation Satellite Systems (GNSS), to improve the positioning accuracy and stability. Firstly, the extended Kalman filter (EKF) fuses the vehicle motion state data with GNSS observation data to improve the independent GNSS positioning accuracy. Subsequently, vehicle state and observation models are formulated using Bayesian theory, incorporating GNSS/UWB data with UWB tag network ranging and with GNSS positioning data among agricultural vehicles and Inter-Vehicular Ranges (IVRs). This integration addresses the significant drift issue in GNSS elevation positioning by employing a high-dimensional decoupling algorithm, standardizing the discrete elevation data, and improving the data’s continuity and predictability. A particle filter is used to refine the vehicle’s position estimation further. Finally, experiments are carried out to verify the robustness of the proposed algorithm under different working conditions.

Streamlined jet tagging network assisted by jet prong structure

Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive and require substantial data for training. In this paper, we introduce a new jet classification network using an MLP mixer, where two subsequent MLP operations serve to transform particle and feature tokens over the jet constituents. The transformed particles are combined with subjet information using multi-head cross-attention so that the network is invariant under the permutation of the jet constituents. We utilize two clustering algorithms to identify subjets: the standard sequential recombination algorithms with fixed radius parameters and a new IRC-safe, density-based algorithm of dynamic radii based on HDBSCAN. The proposed network demonstrates comparable classification performance to state-of-the-art models while boosting computational efficiency drastically. Finally, we evaluate the network performance using various interpretable methods, including centred kernel alignment and attention maps, to highlight network efficacy in collider analysis tasks.

Social Media App for Connecting Similar Interests People Using flutter

Understanding interest similarity in Online Social Networks (OSNs) is crucial for various applications. This study addresses the challenge of determining interest similarity on platforms like Facebook, where users may not explicitly disclose their interests. Utilizing a substantial dataset of 479,048 users and 5,263,351 user-generated interests, the research focuses on movies, music, and TV shows. Findings reveal homophily in interest similarity, demonstrating that individuals tend to share more similar tastes when they have comparable demographic information or are connected as friends. A practical prediction model is proposed, facilitating the selection of users with high-interest similarities and enhancing decision-making for OSN applications. Additionally, the paper introduces a novel method using a tag network to connect users with similar interests, outperforming traditional methods by providing a more efficient means of connecting like-minded individuals in social networks. Key Word:Face image synthesis, Generative adversarial network, Face Recognition.

From Posts to Personas: Classifying Users in the Social Media Landscape

Social media is a digital environment where users openly share their opinions and engage in debates and discussions on various topics. Social media has amassed an enormous quantity of accessible data due to its constantly expanding and highly active user base. This data is a valuable resource for researching a variety of topics. An example of such a research problem is identifying user similarities by analyzing their data. This article explores combining comments, textual posts, and likes for comments to create keywords or tags and group the users. These tags are extracted from user data and utilized in constructing a tag network. The tag network facilitates the formation of communities consisting of users with similar interests. User grouping is achieved based on the tags extracted from the posts. The proposed methods employ TF-IDF (term frequency-inverse document frequency) and TextRank algorithms to extract the tags. Kernel diffusion determines the similarities between tags in the tag network. Finally, an aggregation-hierarchical clustering algorithm is employed to group social media users based on these tags.

A Study of Innovative Entrepreneurial Behavior of College Students under Algorithmic Recommendation

Abstract In this paper, for a large amount of text content such as college students’ innovation and entrepreneurship education practice, considering the problem of directionality, based on the idea of a recommendation algorithm, we use word similarity and TextRank algorithm to extract the keywords of innovation and entrepreneurship behaviors as the first-knowledge labels of the textual resources, and introduce complex labeling network based on this. On the premise of semantic-based TextRank keywords and tag network, the fusion-gated graph attention group recommendation algorithm is used to construct college students’ innovation and entrepreneurship practice platform, and the simulation analysis of college students’ innovation and entrepreneurship practice platform is carried out. The results show that the survival rate of virtual startups can be calculated as 5.37% by using the college students’ innovation and entrepreneurship practice platform, which can accurately analyze that college students’ innovation and entrepreneurship behaviors are stronger, but the survival rate of the enterprises founded by college students for the first time needs to be improved. This study can provide theoretical knowledge guidance for the study of college students’ innovative entrepreneurial behavior and has a facilitating effect on the development of innovation and entrepreneurship education in colleges and universities.

Field–Road Operation Classification of Agricultural Machine GNSS Trajectories Using Spatio-Temporal Neural Network

The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%.

Methodology for development of single cell dendritic spine (SCDS) synaptic tagging and capture model using Virtual Cell (VCell)

Single cell dendritic spine modelling methodology has been adopted to explain structural plasticity and respective change in the neuronal volume previously. However, the single cell dendrite methodology has not been employed previously to explain one of the important aspects of memory allocation i.e., Synaptic tagging and Capture (STC) hypothesis. It is difficult to relate the physical properties of STC pathways to structural changes and synaptic strength. We create a mathematical model based on earlier reported synaptic tagging networks. We built the model using Virtual Cell (VCell) software and used it to interpret experimental data and investigate the behavior and characteristics of known Synaptic tagging candidates.•We investigate processes associated with synaptic tagging candidates and compare them to the assumptions based on the STC hypothesis.•We assess the behavior of several reported synaptic tagging candidates against the requirements outlined in the synaptic tagging hypothesis.

Patterns of interest change in stack overflow

Stack Overflow is currently the largest programming related question and answer community, containing multiple programming areas. The change of user’s interest is the micro-representation of the intersection of macro-knowledge and has been widely studied in scientific fields, such as literature data sets. However, there is still very little research for the general public, such as the question and answer community. Therefore, we analyze the interest changes of 2,307,720 users in Stack Overflow in this work. Specifically, we classify the tag network in the community, vectorize the topic of questions to quantify the user’s interest change patterns. Results show that the change pattern of user interest has the characteristic of a power-law distribution, which is different from the exponential distribution of scientists’ interest change, but they are all affected by three features, heterogeneity, recency and proximity. Furthermore, the relationship between users’ reputations and interest changes is negatively correlated, suggesting the importance of concentration, i.e., those who focus on specific areas are more likely to gain a higher reputation. In general, our work is a supplement to the public interest changes in science, and it can also help community managers better design recommendation algorithms and promote the healthy development of communities.

DNA walker-powered ratiometric SERS cytosensor of circulating tumor cells with single-cell sensitivity

Identification and detection of extreme rare circulating tumor cells (CTCs) in peripheral blood can precisely monitor cancer recurrence and metastasis, however, how to ultra-sensitively and reliably detect CTCs is a big challenge. In this work, a ratiometric surface-enhanced Raman spectroscopy (SERS)-based strategy for ultra-sensitively and nondestructively detecting CTCs was proposed via CTCs-triggered DNA walker-assisted assembly of plasmonic nanostructure networks consisting of Walker probes and SERS tags. The Walker probes were prepared by modifying Fe3O4@SiO2@Au nanoparticles (GMNPs) with ROX-labeled EpCAM aptamer-blocked Zn2+-specific DNAzyme and hairpin-structured single-stranded DNAs H1, and the SERS tags were constructed by co-labelling hairpin-structured single-stranded DNAs H2 and Raman molecules (DTNB) on Au NPs. The aptamers can recognize EpCAM-positive CTCs via the specific binding to EpCAM, so that the activity of DNAzymes is activated with the assistance of Zn2+ to launch the DNA walker to move around for the cleavage of H1 on GMNPs. The residual fragments of H1 on GMNPs can hybridize with H2 on SERS tags and result in the formation of Walker probe-SERS tag network nanostructures (Nw NSs) with rich SERS hot spots. The reliable SERS detection of CTCs is achieved by the stable ratiometric SERS signals of DTNB and ROX generated from the Nw NSs, and a good linear relation between ratiometric SERS signal and MCF-7 cells concentration was obtained with the detection limit low to 1 cell/mL. The recovery rate of MCF-7 cells in peripheral blood is in the range of 94.0%–104.5%, which indicates a good application prospect of the novel ratiometric SERS cytosensor in the clinic detection of EpCAM-positive CTCs.

Understanding the Dynamics of Knowledge Building Process in Online Knowledge‐Sharing Platform: A Structural Analysis of Zhihu Tag Network

Through structural analysis of 8‐year tag networks from online knowledge‐sharing platforms, this study finds that, with the scale of tag networks growing quickly, the growth trend of number edges indicates that tag network follows densification law. The clustering coefficient and the average shortest path of the network show that the rapid growth of network size does not bring about the compartmentalization of the knowledge network, and the degree distribution of tag networks shows a truncated power‐law distribution. According to the structural characteristics of the tag network, this study proposes a tag network model based on the BA model. Based on the preference attachment, the triadic closure mechanism is employed to construct the edges between the old nodes, which revises the limitation that the BA model only connects edges between old and new nodes. The results show that the simulation model matches the actual tag network structure well. The generation mechanism of the tag network model provides a reference for understanding the knowledge construction process of the online knowledge‐sharing platform to a certain extent.

Application of cascade binary pointer tagging in joint entity and relation extraction of Chinese medical text.

Extracting relational triples from unstructured medical texts can provide a basis for the construction of large-scale medical knowledge graphs. The cascade binary pointer tagging network (CBPTN) shows excellent performance in the joint entity and relation extraction, so we try to explore its effectiveness in the joint entity and relation extraction of Chinese medical texts. In this paper, we propose two models based on the CBPTN: CBPTN with conditional layer normalization (Cas-CLN) and biaffine transformation-based CBPTN with multi-head selection (BTCAMS). Cas-CLN uses the CBPTN to decode the head entity and relation-tail entity successively and utilizes conditional layer normalization to enhance the connection between the two steps. BTCAMS detects all possible entities in a sentence by using the CBPTN and then determines the relation between each entity pair through biaffine transformation. We test the performance of the two models on two Chinese medical datasets: CMeIE and CEMRDS. The experimental results prove the effectiveness of the two models. Compared with the baseline CasREL, the F1 value of Cas-CLN and BTCAMS on the test data of CMeIE improved by 1.01 and 2.13%; on the test data of CEMRDS, the F1 value improved by 1.99 and 0.68%.

Factors Affecting Self-Localization in UHF RFID Tag Networks

Accuracy of mobile objects self-localization in radio frequency identification (RFID) tag networks depends on many environmental and design factors. This paper analyzes effect of such factors on estimates of the mobile object location. As an estimator, we use the extended finite impulse response (EFIR) filter. It is shown that accuracy of self-localization in the ultra high frequency (UHF) RFID tag networks can be increased by the factor of several times if to optimize design of the tag and network environment and obtain the optimal angle of arrival and viewing angle. Many other factors are also considered.

Patterns in Interactive Tagging Networks

How do users behave if they can tag each other in social networks?In this paper, we answer this question by studying the interactive tagging network constructed by Twitter lists. Twitter lists can be regarded as the tagging process; a user (i.e., tagger) creates a list with a name (i.e., tag) and adds other users (i.e., tagged users) into the list. This tagging network is by nature different from the resource tagging networks (e.g., Flickr and Delicious) because users on this network can tag each other. We address the following research questions: (RQ1) What is the common patterns and the difference between the interactive tagging network and the resource tagging networks? (RQ2) Do users tag each other on the interactive tagging network? And if so, to what extent? (RQ3) What is the difference between the two types of relationships on Twitter: who-tags-whom and who-follows-whom? By quantitatively studying million-scale networks, we found the pervasive patterns across the different tagging networks, and the interactive patterns within the interactive tagging network. This study sheds light on the underlying characteristics of the interactive tagging network, which is relevant to the social scientists and the system designers of the tagging systems.

Temporal evolution of tagging subnetwork features and motif under different activity levels – take the Q&A community Zhihu as an example

Purpose This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu. Design/methodology/approach A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif. Findings This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak. Research limitations/implications It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks. Practical implications This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. Social implications Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control. Originality/value In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.

Passive BLE Sensing for Indoor Pattern Recognition and Tracking

Crowd-centric sensing using smart phones enables a diverse range of applications evolving from large outdoor environments (e.g., smart cities) to small-scale indoor environments (e.g., smart homes, smart buildings). Tracking users’ patterns in indoor environments is a valuable and challenging aspect that is not yet fully addressed. Active indoor localization systems are generally energy-inefficient and cannot be applied to crowd monitoring applications. This paper focuses on the development of a passive and energy-efficient indoor tracking and pattern recognition technique on top of a managed Bluetooth Low-Energy (BLE) network. Particularly, our system model is based on a passive monitoring of a network of BLE tags, which continuously broadcast their unique identifiers, and the current timestamp. Multiple protocols were implemented to extract moving objects’ locations in indoors. The trajectory building process consists of different phases: 1) data sampling, 2) outlier detection and removal, 3) location estimation with a weighted centroid approach, 4) spatio-temporal map matching, and finally 5) trajectory smoothing. A series of experiments was conducted to demonstrate the efficiency and accuracy of the proposed approach, with respect to active triggering approaches and BLE-based localization systems.

RTJTN: Relational Triplet Joint Tagging Network for Joint Entity and Relation Extraction.

Extracting entities and relations from unstructured sentences is one of the most concerned tasks in the field of natural language processing. However, most existing works process entity and relation information in a certain order and suffer from the error iteration. In this paper, we introduce a relational triplet joint tagging network (RTJTN), which is divided into joint entities and relations tagging layer and relational triplet judgment layer. In the joint tagging layer, instead of extracting entity and relation separately, we propose a tagging method that allows the model to simultaneously extract entities and relations in unstructured sentences to prevent the error iteration; and, in order to solve the relation overlapping problem, we propose a relational triplet judgment network to judge the correct triples among the group of triples with the same relation in a sentence. In the experiment, we evaluate our network on the English public dataset NYT and the Chinese public datasets DuIE 2.0 and CMED. The F1 score of our model is improved by 1.1, 6.0, and 5.1 compared to the best baseline model on NYT, DuIE 2.0, and CMED datasets, respectively. In-depth analysis of the model's performance on overlapping problems and sentence complexity problems shows that our model has different gains in all cases.

On the Design of Multi-Hop Tag-to-Tag Routing Protocol for Large-Scale Networks of Passive Tags

Recently, tag-to-tag (T2T) backscattering technique in a passive RFID system has received broad attention due to its superiority for large-scale network applications. If used to implement a Network of Tags, use of T2T communication allows inherent communication parallelism, thus supporting orders of magnitude larger capacity than centralized RFID reader-based systems. To unleash the potential of T2T communication, turbo backscattering operation enables the implementation of a multi-hop network of tags, which supports larger network coverage of a Network of Tags. However, due to asymmetric communication links and interferences among tags' transmissions in such a T2T backscattering based network, the routing protocol design has become one of the main technical challenges, especially for large-scale networks. Furthermore, the computation time of T2T routing protocols increases exponentially with the number of tags, greatly limiting the practicability of such large-scale backscattering networks. In this paper, we present the design of a Network of Tags model to address these challenges, and we propose novel routing protocols for three distinct types of tags with different hardware capabilities. To address the issue of computational processing time of the routing protocol for large-scale T2T networks, we propose a new scheme with linear time complexity. We evaluate and compare the performance of the proposed protocols, as well as investigate the impact of network parameters on the performance.

Modelling and analysis of tagging networks in Stack Exchange communities

AbstractLarge Question-and-Answer (Q&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.

Outcomes of the International Oceanographic Data and Information Exchange Ocean Biogeographic Information System OBIS-Event-Data Workshop on Animal Tagging and Tracking

The Ocean Biogeographic Information System (OBIS) began in 2000 as the repository for data from the Census of Marine Life. Since that time, OBIS has expanded its goals beyond simply hosting data to supporting more aspects of marine conservation (Pooter et al. 2017). In order to accomplish those goals, the OBIS secretariat in partnership with its European node (EurOBIS) hosted at the Flanders Marine Institute (VLIZ, Belgium), and the Intergovernmental Oceanographic Commission (IOC) Committee on International Oceanographic Data and Information Exchange (IODE, 23rd session, March 2015, Brugge) established a 2-year pilot project to address a particularly problematic issue that environmental data collected as part of marine biological research were being disassociated from the biological data. OBIS-Event-Data is the solution that was developed from that pilot project, which devised a method for keeping environmental data together with the biological data (Pooter et al. 2017). OBIS is seeking early adopters of the new data standard OBIS-Event-Data from among the marine biodiversity monitoring communities, to further validate the data standard, and develop data products and scientific applications to support the enhancement of Biological and Ecosystem Essential Ocean Variables (EOVs) in the framework of the Global Ocean Observing System (GOOS) and the Marine Biodiversity Observation Network of the Group on Earth Observations (GEO BON MBON). After the successful 2-year IODE pilot project OBIS-ENV-DATA, the IOC established a new 2-year IODE pilot project OBIS-Event-Data for Scientific Applications (2017-2019). The OBIS-Event-Data data standard, building on Darwin Core, provides a technical solution for combined biological and environmental data, and incorporates details about sampling methods and effort, including event hierarchy. It also implements standardization of parameters involved in biological, environmental, and sampling details using an international standard controlled vocabulary (British Oceanographic Data Centre Natural Environment Research Council). A workshop organized by IODE/OBIS in April brought together major animal tagging and tracking networks such as the Ocean Tracking Network (OTN), the Animal Telemetry Network (ATN), the Integrated Marine Observing System (IMOS), the European Tracking Network (ETN) and the Acoustic Tracking Array Platform (ATAP) to test the OBIS-Event-Data standard through the development of some data products and science applications. Additionally, this workshop contributes to the further maturation of the GOOS EOV on fish as well as the EOV on birds, mammals and turtles. We will present the outcomes as well as any lessons learned from this workshop on problems, solutions, and applications of using Darwin Core/OBIS-Event-Data for bio-logging data.

Image Analysis System for Optimal Geometric Distribution of RFID Tags Based on Flood Fill and DLT

A novel image analysis method for improving the reading performance of the radio frequency identification (RFID) system is proposed by arranging the geometric distribution of multiple tags reasonably based on flood fill and direct linear transformation (DLT) in this paper. An image collecting system is projected to collect the image of RFID tags, and an online detection system is designed to measure the reading distance. The proposed system has good performance of instantaneity and robustness, in which tags could be identified rapidly with the tray turning. The 2-D distribution images of tags are processed using the flood-fill technique. Furthermore, the 3-D distribution images of tags are analyzed based on DLT. Subsequently, a tag network is established, in which a tag is described as a node. Finally, support vector machine is introduced to predict the reading distance and corresponding distribution for a given number of tags after training the measured reading distance of RFID tags under various geometric distributions. The results show that the optimal geometric distribution of tags could be obtained by analyzing the images of tags and the predicted reading distance of tags. This method can improve the dynamic performance of multiple tags in the RFID system effectively.