Relevant bibliographies by topics / Tag Network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Tag Network'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Tag Network"

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Hu, Jinyu, Zhiwei Gao, and Weisen Pan. "Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation." Journal of Applied Mathematics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/248084.

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Multiangle social network recommendation algorithms (MSN) and a new assessment method, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithm from resource point (UBR), user-based algorithm from tag point (UBT), resource-based algorithm from tag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels.
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Shimizu, Hiroshi. "Ring Network with VLAN Tag." IEEJ Transactions on Electronics, Information and Systems 125, no. 10 (2005): 1602–7. http://dx.doi.org/10.1541/ieejeiss.125.1602.

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Yu, Xiaolei, Yujun Zhou, Zhenlu Liu, and Zhimin Zhao. "An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO." Transactions of the Institute of Measurement and Control 41, no. 12 (February 6, 2019): 3331–39. http://dx.doi.org/10.1177/0142331218823864.

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In this paper, a multi-tag optimization method based on image analysis and particle swarm optimization (PSO) neural network is proposed to verify the effect of radio frequency identification (RFID) multi-tag distribution on the performance of the system. A RFID tag detection system is proposed with two charge coupled device (CCD). This system can automatically focus on the tag according to its position, so it can obtain the image information more accurately by template matching and edge detection method. Therefore, the spatial structure of multi-tag and the corresponding reading distance can be obtained for training. Because of its excellent performance in multi-objective optimization, the PSO neural network is used to train and predict multi-tag distribution at the maximum reading distance. Compared with other neural networks, PSO is more accurate and its uptime is shorter for RFID multi-tag analysis.
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Türker, İlker, and Eyüb Ekmel Sulak. "A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links." International Journal of Modern Physics B 32, no. 04 (February 2018): 1850029. http://dx.doi.org/10.1142/s0217979218500297.

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Complex network studies, as an interdisciplinary framework, span a large variety of subjects including social media. In social networks, several mechanisms generate miscellaneous structures like friendship networks, mention networks, tag networks, etc. Focusing on tag networks (namely, hashtags in twitter), we made a two-layer analysis of tag networks from a massive dataset of Twitter entries. The first layer is constructed by converting the co-occurrences of these tags in a single entry (tweet) into links, while the second layer is constructed converting the semantic relations of the tags into links. We observed that the universal properties of the real networks like small-world property, clustering and power-law distributions in various network parameters are also evident in the multilayer network of hashtags. Moreover, we outlined that co-occurrences of hashtags in tweets are mostly coupled with semantic relations, whereas a small number of semantically unrelated, therefore random links reduce node separation and network diameter in the co-occurrence network layer. Together with the degree distributions, the power-law consistencies of degree difference, edge weight and cosine similarity distributions in both layers are also appealing forms of Zipf’s law evident in nature.
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Ping, Deng Li, Guo Bing, and Zheng Wen. "Web Service Clustering Approach Based on Network and Fused Document-Based and Tag-Based Topics Similarity." International Journal of Web Services Research 18, no. 3 (July 2021): 63–81. http://dx.doi.org/10.4018/ijwsr.2021070104.

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To produce a web services clustering with values that satisfy many requirements is a challenging focus. In this article, the authors proposed a new approach with two models, which are helpful to the service clustering problem. Firstly, a document-tag LDA model (DTag-LDA) is proposed that considers the tag information of web services, and the tag can describe the effective information of documents accurately. Based on the first model, this article further proposes an efficient document weight and tag weight-LDA model (DTw-LDA), which fused multi-modal data network. To further improve the clustering accuracy, the model constructs the network for describing text and tag respectively and then merges the two networks to generate web service network clustered. In addition, this article also designs experiments to verify that the used auxiliary information can help to extract more accurate semantics by conducting service classification. And the proposed method has obvious advantages in precision, recall, purity, and other performance.
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Ryoo, Jihoon, Jinghui Jian, Akshay Athalye, Samir R. Das, and Milutin Stanacevic. "Design and Evaluation of “BTTN”: A Backscattering Tag-to-Tag Network." IEEE Internet of Things Journal 5, no. 4 (August 2018): 2844–55. http://dx.doi.org/10.1109/jiot.2018.2840144.

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Shimizu, Hiroshi. "A ring network with VLAN tag." Electrical Engineering in Japan 166, no. 1 (January 15, 2009): 60–66. http://dx.doi.org/10.1002/eej.20422.

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LAN, JAMES K., WELL Y. CHOU, and CHIUYUAN CHEN. "EFFICIENT ROUTING ALGORITHMS FOR GENERALIZED SHUFFLE-EXCHANGE NETWORKS." Discrete Mathematics, Algorithms and Applications 01, no. 02 (June 2009): 267–81. http://dx.doi.org/10.1142/s179383090900021x.

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The shuffle-exchange network has been proposed as a popular architecture for multistage interconnection networks. In 1991, Padmanabhan introduced the generalized shuffle-exchange network (GSEN) and proposed an efficient routing algorithm. Later, Chen et al. further enhanced the GSEN with bidirectional links and proposed the bidirectional GSEN (BGSEN). A BGSEN consists of the forward and the backward network. Based on the idea of inversely using the control tag generated by Padmanabhan's algorithm, Chen et al. proposed a routing algorithm for the backward network. Recently, Chen and Lou also proposed a routing algorithm for the backward network. It should be noted, however, that Padmanabhan's algorithm is actually an explicit formula for computing the control tag for routing and takes only O(1) time. Neither the algorithm of Chen et al. nor the algorithm of Chen and Lou provides an explicit formula for computing the control tag for routing and both algorithms take at least Ω(n) time, where n + 1 is the number of stages in the BGSEN. This paper attempts to propose an explicit formula for computing the control tag for routing in the backward network. We will demonstrate how this formula greatly simplifies the computation process and how it leads to efficient routing algorithms. In particular, an O(1)-time one-to-one routing algorithm and an efficient routing-table construction algorithm have been proposed.
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Lee, Hyuek-Jae, Soo-Young Lee, Sang-Yung Shin, and Bo-Yun Koh. "TAG: A Neural Network Model for Large-Scale Optical Implementation." Neural Computation 3, no. 1 (February 1991): 135–43. http://dx.doi.org/10.1162/neco.1991.3.1.135.

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TAG (Training by Adaptive Gain) is a new adaptive learning algorithm developed for optical implementation of large-scale artificial neural networks. For fully interconnected single-layer neural networks with N input and M output neurons TAG contains two different types of interconnections, i.e., M N global fixed interconnections and N + M adaptive gain controls. For two-dimensional input patterns the former may be achieved by multifacet holograms, and the latter by spatial light modulators (SLMs). For the same number of input and output neurons TAG requires much less adaptive elements, and provides a possibility for large-scale optical implementation at some sacrifice in performance as compared to the perceptron. The training algorithm is based on gradient descent and error backpropagation, and is easily extensible to multilayer architecture. Computer simulation demonstrates reasonable performance of TAG compared to perceptron performance. An electrooptical implementation of TAG is also proposed.
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RYU, WOOSEOK, JOONHO KWON, and BONGHEE HONG. "A SIMULATION NETWORK MODEL TO EVALUATE RFID MIDDLEWARES." International Journal of Software Engineering and Knowledge Engineering 21, no. 06 (September 2011): 779–801. http://dx.doi.org/10.1142/s0218194011005517.

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High-performance radio-frequency identification (RFID) is a challenging issue for large-scale enterprises. As a key component of an RFID system, RFID middleware is an important factor to measure the performance of the system. To evaluate the feasibility of an RFID middleware, the performance of the RFID middleware should be carefully evaluated in various RFID-enabled business environments. However, the construction of an RFID testbed requires a lot of time, money, and human resources because it involves numerous tagged items and a large number of deployed readers. We must provide a meaningful input tag stream representing various business activities, rather than random data. This paper presents a novel simulation model for the virtual construction of RFID testbeds. To ensure the semantic validity of the input tag stream, the proposed RFID simulation network (RSN) extends Petri nets by including sets of functions that represent unique characteristics of RFID environments such as the uncertainty of communications and tag movement patterns. By configuring appropriate functions, the RSN automatically generates an input tag stream that matches the distribution of real data. We demonstrate that the RSN model correctly reflects data from real-world environments by comparing input tag streams from real RFID equipment and from the RSN model.
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Dissertations / Theses on the topic "Tag Network"

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Ye, Qianyun. "2.4-GHz Wireless Network Based Multi-Tag Access System." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188491.

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Machine-to-Machine technology has been intensively researched recently which is believed to take the role of leading ICT industry development. Wireless Sensor Networks provide solution to integrate numerous numbers of machines who require features include low power, low cost, and flexible, which can be fulfilled by applying Zigbee technique. This thesis devotes an effort into Wireless Sensor Network development that a Multi- Tag System operating on 2.4-GHz wireless network is developed. A theoretical study about ZigBee protocol and its bottom layers IEEE 802.15.4 standard is provided to lay a foundation of the design work. The thesis also includes a practical usage of low cost TI CC2530 Systom-on-Chip, together with the illustration of software development inside the chip. The design work provides GUI Platform for users to register themselves into the network and central monitoring platform to track all users within the area. Both GUI platforms are developed based on VB IDE. The Multi-Tag Access System is suitable for attendee control functionality in big-scale conference, events, and lecture, which is also a prototype expecting more functionality to be added in the future.
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Wei, Wei. "Utilizing Social Bookmarking Tag Space for Web Content Discovery: A Social Network Analysis Approach." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/195123.

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Social bookmarking has gained popularity since the advent of Web 2.0. Keywords known as tags are created to annotate web content, and the resulting tag space composed of the tags, the resources, and the users arises as a new platform for web content discovery. Useful and interesting web resources can be located through searching and browsing based on tags, as well as following the user-user connections formed in the social bookmarking community. However, the effectiveness of tag-based search is limited due to the lack of explicitly represented semantics in the tag space. In addition, social connections between users are underused for web content discovery because of the inadequate social functions. In this research, we propose a comprehensive framework to reorganize the flat tag space into a hierarchical faceted model. We also studied the structure and properties of various networks emerging from the tag space for the purpose of more efficient web content discovery.The major research approach used in this research is social network analysis (SNA), together with methodologies employed in design science research. The contribution of our research includes: (i) a faceted model to categorize social bookmarking tags; (ii) a relationship ontology to represent the semantics of relationships between tags; (iii) heuristics to reorganize the flat tag space into a hierarchical faceted model using analysis of tag-tag co-occurrence networks; (iv) an implemented prototype system as proof-of-concept to validate the feasibility of the reorganization approach; (v) a set of evaluations of the social functions of the current networking features of social bookmarking and a series of recommendations as to how to improve the social functions to facilitate web content discovery.
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Hijazi, Issa, and Pontus Pettersson. "Animal ID Tag Recognition with Convolutional and Recurrent Neural Network : Identifying digits from a number sequence with RCNN." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17031.

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Major advances in machine learning have made image recognition applications, with Artificial Neural Network, blossom over the recent years. The aim of this thesis was to find a solution to recognize digits from a number sequence on an ID tag, used to identify farm animals, with the help of image recognition. A Recurrent Convolutional Neural Network solution called PPNet was proposed and tested on a data set called Animal Identification Tags. A transfer learning method was also used to test if it could help PPNet generalize and better recognize digits. PPNet was then compared against Microsoft Azures own image recognition API, to determine how PPNet compares to a general solution. PPNet, while not performing as good, still managed to achieve competitive results to the Azure API.
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Esfahani, Alireza. "Security schemes against pollution attacks in network coding over wireless networks." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/21773.

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Doutoramento em Telecomunicações
Resumo em português não disponivel
The topic of this thesis is how to achieve e cient security against pollution attacks by exploiting the structure of network coding. There has recently been growing interest in using network coding techniques to increase the robustness and throughput of data networks, and reduce the delay in wireless networks, where a network coding-based scheme takes advantage of the additive nature of wireless signals by allowing two nodes to transmit simultaneously to the relay node. However, Network Coding (NC)-enabled wireless networks are susceptible to a severe security threat, known as data pollution attack, where a malicious node injects into the network polluted (i.e., corrupted) packets that prevent the destination nodes from decoding correctly. Due to recoding at the intermediate nodes, according to the core principle of NC, the polluted packets propagate quickly into other packets and corrupt bunches of legitimate packets leading to network resource waste. Hence, a lot of research e ort has been devoted to schemes against data pollution attacks. Homomorphic Message Authentication Code (MAC)-based schemes are a promising solution against data pollution attacks. However, most of them are susceptible to a new type of pollution attack, called tag pollution attack, where an adversary node randomly modi es tags appended to the end of the transmitted packets. Therefore, in this thesis, we rst propose a homomorphic message authentication code-based scheme, providing resistance against data pollution attacks and tag pollution attacks in XOR NC-enabled wireless networks. Moreover, we propose four homomorphic message authentication code-based schemes which provide resistance against data and tag pollution attacks in Random Linear Network Coding (RLNC). Our results show that our proposed schemes are more e cient compared to other competitive tag pollution immune schemes in terms of complexity, communication overhead and key storage overhead.
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Jeevarathnam, Nanda Gopal. "Grid-Based RFID Indoor Localization Using Tag Read Count and Received Signal Strength Measurements." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7039.

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Passive ultra-high frequency (UHF) radio frequency identification (RFID) systems have gained immense popularity in recent years for their wide-scale industrial applications in inventory tracking and management. In this study, we explore the potential of passive RFID systems for indoor localization by developing a grid-based experimental framework using two standard and easily measurable performance metrics: received signal strength indicator (RSSI) and tag read count (TRC). We create scenarios imitating real life challenges such as placing metal objects and other RFID tags in two different read fields (symmetric and asymmetric) to analyze their impacts on location accuracy. We study the prediction potential of RSSI and TRC both independently and collaboratively. In the end, we demonstrate that both signal metrics can be used for localization with sufficient accuracy whereas the best performance is obtained when both metrics are used together for prediction on an artificial neural network especially for more challenging scenarios. Experimental results show an average error of as low as 0.286 (where consecutive grid distance is defined as unity) which satisfies the grid-based localization benchmark of less than 0.5.
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Klaus, Jan Philipp. "Three Essays on the Effects of Executives' Informal Networks on Shareholder Value, Financial and Tax Reporting Outcomes." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1707258/.

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Prior literature suggests that CEOs capitalize on their position within the hierarchy of all business executives, resulting in various – both positive and negative – firm outcomes. Using a novel data set on golf outings to measure the quality of a CEO's informal (vs. formal) network, as measured by the CEO's network centrality, this study examines whether well-connected CEOs generate private gains through insider trades. Results suggest that, among golfing CEOs, CEOs with higher quality informal networks generate significantly higher insider trading profits on sales of their firms' stock, consistent with more famous, powerful, and influential CEOs possessing superior information. The paper continues by delineating a channel through which private information flow to network participants by documenting significantly different golf patterns of CEOs during the two weeks before material firm events become public while showing that CEOs generate noticeably higher insider trading profits from stock trades executed during the two weeks following these golf outings. This study highlights a setting in which shareholders are at risk of wealth transfer and illustrates the potential limitations of regulation concerning insider trading.
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Cranley, Nikki. "The Implications for Network Switch Design in a Networked FTI Data Acquisition System." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595675.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
Switches are a critical component in any networked FTI data acquisition system in order to allow the forwarding of data from the DAU to the target destination devices such as the network recorder, PCM gateways, or ground station. Commercial off the shelf switches cannot meet the harsh operating conditions of FTI. This paper describes a hardware implementation of a crossbar switching architecture that meets the reliability and performance requirements of FTI equipment. Moreover, by combining the crossbar architecture with filtering techniques, the switch can be configured to achieve sophisticated forwarding operations. By way of illustration, a Gigabit network tap application is used to demonstrate the fundamental concepts of switching, forwarding, crossbar architecture, and filtering.
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Guo, Yue. "Capability assessment of VAr support and demand response to transmission network using flexible tap changing techniques in distribution networks." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/capability-assessment-of-var-support-and-demand-response-to-transmission-network-using-flexible-tap-changing-techniques-in-distribution-networks(5a373163-d2c9-4e13-9720-0c4fa0456c3f).html.

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Due to the increasing integration of renewable energy generations, the overvoltage and overload issues in transmission networks have become more significant, and they may occur at various locations. To mitigate the overvoltage issues, traditional solutions which often consider the installation of reactive power compensators such as shunt reactors, SVC, STATCOM may not be cost-effective. To mitigate the overload issues, traditional methods using direct or price-based demand control will affect customers’ electrical experience in that they are inconvenienced greatly. This thesis discusses the flexible tap changing techniques that utilise existing parallel transformers in distribution networks to provide reactive power absorption and demand response services for transmission systems. Among them, the tap stagger technique operates parallel transformers in small different tap positions, i.e. staggered taps, to result in more reactive power absorption from upstream networks. In addition, the tap changing technique changes voltages in the range of statutory limits through the adjustment of tap positions in order to change network demands without directly affecting customers. The aggregated reactive power absorption or demand response from many pairs of parallel transformers in distribution networks could be sufficient to provide VAr or demand support to transmission networks. Network capability studies have been carried out in OpenDSS simulation software to investigate the VAr absorption capability by using tap staggering technique and the demand reduction capability by using tap changing technique. The studies are based on two UK HV distribution networks (132-33kV) with 11 and 28 primary substations (33/11 or 6.6 kV) respectively, and the techniques are applied to parallel transformers in primary substations. Based on the results of the two networks, the capabilities of the whole ENW and the UK distribution networks have been estimated respectively by using linear estimation method. In addition, the VAr absorption capability of the tap stagger technique has been validated by using site trial data. The results show an average VAr absorption capability of 0.89MVAr for a primary substation, 315MVAr for ENW networks and about 2500MVAr for the UK at stagger level 4 and show an average demand reduction capability of 3.1% of the original demand at tap down level 3. The results of capability studies together with the validations results confirm that the flexible tap changing techniques are able to provide transmission networks with effective VAr support and demand response services. To assess network VAr absorption and demand response capability more precisely, this thesis also proposes an online load profile estimation method to estimate the load profiles of the network more accurately if not all substations in the network are monitored. The method uses Peak Load Share values, Euclidean Distance, and some load measurements to estimate load profiles. The method has been validated and compared with a traditional aggregation-based method. The results show an average estimation error of 13% ~ 23% in different conditions using the proposed method, and show an average estimation error reduction from about 47% (using the traditional method) to about 13% (using the proposed method). The results indicate that the developed method has a considerable improvement on the accuracy of load profile estimation.
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Baloch, Fariha. "Tag anti-collision algorithms for active and passive RFID networks with foresight." Diss., Wichita State University, 2014. http://hdl.handle.net/10057/10931.

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In the world where initiatives to automate jobs are becoming a norm, it is no surprise that the interest in radio frequency identification (RFID) networks has grown exponentially. With RFID technology, organizations around the world can reduce their workforce and grow their businesses. However, this technology is not yet at a maturity point. For example, in order for a cart full of groceries to go through an unmanned checkout lane, it is crucial that all of the tagged items are read and processed with 100% reliability. Also, the time to process items needs to be fast enough so that customers can pay and be on their way as quickly as possible. In order to achieve speed and reliability, many transmission control protocols have been devised. The most popular protocol with passive RFID equipment manufacturers is Electronic Product Code global (EPCglobal(R)) Class 1 Generation 2, or simply EPC C1G2. Transmission control in the EPC C1G2 protocol is achieved with framed slotted ALOHA (FSA), where tags pick a random slot from choices given by the reader, and when their turn comes, they backscatter their information to the reader. FSA produces three kinds of slots: empty, collided, and successful. Empty and collided slots are categorized under unsuccessful slots, and the time spent on these is considered as wasted time. Several research studies in the past have focused on reducing the occurrence of unsuccessful slots by using new and innovative methods and increasing RFID network throughput. The motivation of this research, however, is to reduce the overall time of reading tags in a passive and active RFID network by minimizing the time spent on unsuccessful slots. This research builds upon methods used in previous research, and proposes three new methods for passive RFID systems and one new method for active RFID systems in order to diminish wasted time on unsuccessful slots.
Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
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Holmeide, Øeyvind, and Markus Schmitz. "Advanced Network Tap Application for Flight Test Instrumentation Systems." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596387.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV
Digital data distribution systems are widely used in Aerospace and Defense products to allow devices to communicate with one another. In many cases it is desirable to monitor the data traffic flowing between two points in a copper or fiber based Operational or Onboard Network System (ONS) for Flight Test Instrumentation (FTI) purposes because these ONS systems may carry important data which can be used without duplicating/installing a specific FTI data acquisition system to receive this data. The two types of network taps that can be used are Inline Network Taps and network end-point taps. This paper examines the usage of Inline Network Taps for FTI applications and how they can support network access strategies and objectives. An Inline Network Tap is a hardware device which allows access to data flowing across a network. These devices are typically active/powered and have a number of ports: a first tap port, a second tap port, and one or more mirror ports. An in-line network tap inserted between the first and second tap port passes all data traffic through unimpeded but also copies that same data to one or more mirror ports. Some Inline Network Tap devices may also pass packets when the tap is not powered or a malfunction is detected on the device via an integrated by-pass function. If the Inline Network Tap device goes offline the unit automatically bypasses the tap connection and data traffic is directed through the bypass directly to network devices. This capability is crucial for inline usage on mission critical network segments that cannot afford the risk of losing the network connection. An in-line network tap can either be based on copper or fiber technology and as a "filterable" network tap can also provide advanced packet filtering capabilities. These filterable network taps can selectively pass data, e.g., based on VLAN ID or other parameters, to a mirror port for deep analysis, monitoring and recording. Another advanced tap function that is presented in this paper is the support for inserting time stamps at the tap level in monitored packets which provides a reference time when the data content of a given packet was generated at a data source. This capability is a significant feature for FTI applications as most ONS systems do not provide time stamped data.
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Books on the topic "Tag Network"

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Tu shu guan zi xun xi tong zhi tan tao. Taibei Shi: Taiwan xue sheng shu ju, 1985.

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Davie, Bruce S. Switching in IP networks: IP switching, tag switching, and related technologies. San Francisco, Calif: Morgan Kaufmann Publishers, 1998.

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The Tao of network security monitoring: Beyond intrusion detection. Boston: Addison-Wesley, 2005.

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Networking: How to creatively tap your people resources. Mission, Kan: SkillPath Publications, 1993.

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Liebing, Edward. NetWare user's guide: Version 2.2 : tap the power of NetWare v2.2. Redwood City, Calif: M&T Books, 1991.

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Bosbeke, André van. Kirschen en Co: Het blauwe netwerk. Berchem: EPO, 1988.

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Hŭi-gap, Chʻoe, ed. Dijitʻŏl kŭmnyung tae hyŏngmyŏng. Sŏul-si: Samsŏng Kyŏngje Yŏnʼguso, 2002.

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Bu ru 21 shi ji de nong ye xin xi hua: Xin xi zi yuan kai fa li lun tan tao. Beijing: Jing ji ke xue chu ban she, 2000.

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Shanghai shu zi jiao yu gong gong fu wu ping tai yan jiu. Shanghai Shi: Shanghai jiao tong da xue chu ban she, 2011.

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Hagemann, Stephan. A framework for the consistent usage of tag-based mashups. [Amsterdam]: Ios Press, 2010.

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Book chapters on the topic "Tag Network"

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Trattner, Christoph, Denis Helic, and Markus Strohmaier. "Tag Clouds." In Encyclopedia of Social Network Analysis and Mining, 2103–7. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_126.

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Trattner, Christoph, Denis Helic, and Markus Strohmaier. "Tag Clouds." In Encyclopedia of Social Network Analysis and Mining, 3035–39. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_126.

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Trattner, Christoph, Denis Helic, and Markus Strohmaier. "Tag Clouds." In Encyclopedia of Social Network Analysis and Mining, 1–5. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_126-1.

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Armknecht, Frederik, Ahmad-Reza Sadeghi, Ivan Visconti, and Christian Wachsmann. "On RFID Privacy with Mutual Authentication and Tag Corruption." In Applied Cryptography and Network Security, 493–510. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13708-2_29.

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Kim, Jinhwan, Hyocheol Jeong, Myungjae Kim, Haosong Gou, Munseok Choi, and Younghwan Yoo. "Tag-to-Tag Mesh Network Using Dual-Radio RFID System for Port Logistics." In Dynamics in Logistics, 311–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11996-5_28.

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Meisner, Eric, Wei Yang, and Volkan Isler. "Probabilistic Network Formation through Coverage and Freeze-Tag." In Springer Tracts in Advanced Robotics, 3–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00312-7_1.

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Arita, Seiko, and Koji Tsurudome. "Construction of Threshold Public-Key Encryptions through Tag-Based Encryptions." In Applied Cryptography and Network Security, 186–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01957-9_12.

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Blackburn, Simon R., and M. J. B. Robshaw. "On the Security of the Algebraic Eraser Tag Authentication Protocol." In Applied Cryptography and Network Security, 3–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39555-5_1.

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Mistry, Oly, and Sandip Sen. "Probabilistic Approaches to Tag Recommendation in a Social Bookmarking Network." In Principles and Practice of Multi-Agent Systems, 270–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25920-3_19.

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Yan, Bin, Lichen Zhang, Longjiang Guo, Meirei Ren, and Ana Wang. "Personalized Recommendation Based on Tag Semantics in the Heterogeneous Information Network." In Communications in Computer and Information Science, 224–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1785-3_17.

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Conference papers on the topic "Tag Network"

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Wang, Chaoyue, Chaohui Wang, Chang Xu, and Dacheng Tao. "Tag Disentangled Generative Adversarial Network for Object Image Re-rendering." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/404.

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In this paper, we propose a principled Tag Disentangled Generative Adversarial Networks (TD-GAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties (such as viewpoint, illumination, expression, etc.). The whole framework consists of a disentangling network, a generative network, a tag mapping net, and a discriminative network, which are trained jointly based on a given set of images that are completely/partially tagged (i.e., supervised/semi-supervised setting). Given an input image, the disentangling network extracts disentangled and interpretable representations, which are then used to generate images by the generative network. In order to boost the quality of disentangled representations, the tag mapping net is integrated to explore the consistency between the image and its tags. Furthermore, the discriminative network is introduced to implement the adversarial training strategy for generating more realistic images. Experiments on two challenging datasets demonstrate the state-of-the-art performance of the proposed framework in the problem of interest.
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Chen, Bo, Wei Guo, Ruiming Tang, Xin Xin, Yue Ding, Xiuqiang He, and Dong Wang. "TGCN: Tag Graph Convolutional Network for Tag-Aware Recommendation." In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3340531.3411927.

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Wengang Cheng and Xiaolei Wang. "Image tag refinement using tag semantic and visual similarity." In 2011 International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2011. http://dx.doi.org/10.1109/iccsnt.2011.6182401.

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Banerjee, Subhasis, and Kalapriya Kannan. "Tag-In-Tag: Efficient flow table management in SDN switches." In 2014 10th International Conference on Network and Service Management (CNSM). IEEE, 2014. http://dx.doi.org/10.1109/cnsm.2014.7014147.

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Ahmad, Abeer, Yuanfei Huang, Xiao Sha, Akshay Athalye, Milutin Stanacevic, Samir R. Das, and Petar M. Djuric. "On Measuring Doppler Shifts between Tags in a Backscattering Tag-to-Tag Network with Applications in Tracking." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053291.

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Wang, Yun, Lun Du, Guojie Song, Xiaojun Ma, Lichen Jin, Wei Lin, and Fei Sun. "Tag2Gauss: Learning Tag Representations via Gaussian Distribution in Tagged Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/527.

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Keyword-based tags (referred to as tags) are used to represent additional attributes of nodes in addition to what is explicitly stated in their contents, like the hashtags in YouTube. Aside of being auxiliary information for node representation, tags can also be used for retrieval, recommendation, content organization, and event analysis. Therefore, tag representation learning is of great importance. However, to learn satisfactory tag representations is challenging because 1) traditional representation methods generally fail when it comes to representing tags, 2) bidirectional interactions between nodes and tags should be modeled, which are generally not dealt within existing research works. In this paper, we propose a tag representation learning model which takes tag-related node interaction into consideration, named Tag2Gauss. Specifically, since tags represent node communities with intricate overlapping relationships, we propose that Gaussian distributions would be appropriate in modeling tags. Considering the bidirectional interactions between nodes and tags, we propose a tag representation learning model mapping tags to distributions consisting of two embedding tasks, namely Tag-view embedding and Node-view embedding. Extensive evidence demonstrates the effectiveness of representing tag as a distribution, and the advantages of the proposed architecture in many applications, such as the node classification and the network visualization.
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Dowling, Matthew, Monica F. Bugallo, Samir Das, and Petar M. Djuric. "Tracking of Objects in a Passive Backscattering Tag-to-Tag Network." In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2018. http://dx.doi.org/10.1109/spawc.2018.8446002.

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Liu, Qi, Ruobing Xie, Lei Chen, Shukai Liu, Ke Tu, Peng Cui, Bo Zhang, and Leyu Lin. "Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation." In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3340531.3416021.

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Zhang, Yuan, Hongshen Chen, Yihong Zhao, Qun Liu, and Dawei Yin. "Learning Tag Dependencies for Sequence Tagging." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/637.

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Sequence tagging is the basis for multiple applications in natural language processing. Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. Sequence tagging actually possesses complex dependencies and interactions among the input tokens and the output tags. We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously. A tag LSTM is augmented to manage the output tag dependencies and word-tag interactions, while three mechanisms are presented to efficiently incorporate token context representation and tag dependency. Extensive experiments on part-of-speech tagging and named entity recognition tasks show that the proposed model outperforms the BiLSTM-CRF baseline by effectively incorporating the tag dependency feature.
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Qian, Qiao, Bo Tian, Minlie Huang, Yang Liu, Xuan Zhu, and Xiaoyan Zhu. "Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network." In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/p15-1132.

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Reports on the topic "Tag Network"

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Alstadsæter, Annette, Wojciech Kopczuk, and Kjetil Telle. Social Networks and Tax Avoidance: Evidence from a Well-Defined Norwegian Tax Shelter. Cambridge, MA: National Bureau of Economic Research, October 2018. http://dx.doi.org/10.3386/w25191.

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Merrill, W. M., K. Sohrabi, G. J. Pottie, W. Kaiser, and H. L. Liu. Pico Wireless Integrated Network Sensors (PicoWINS): Investigating the Feasibility of Wireless Tactical Tags. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada404577.

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Souza, Pedro CL, Imran Rasul, and Áureo de Paula. Identifying network ties from panel data: theory and an application to tax competition. The IFS, October 2019. http://dx.doi.org/10.1920/wp.cem.2019.5519.

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Boning, William, John Guyton, Ronald Hodge, Joel Slemrod, and Ugo Troiano. Heard it Through the Grapevine: Direct and Network Effects of a Tax Enforcement Field Experiment. Cambridge, MA: National Bureau of Economic Research, February 2018. http://dx.doi.org/10.3386/w24305.

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Chen, Dan, and Qiang Zhou. Induction of Apoptosis by Targeting the Microtubule Network: Using HIV Tat as a Model System. Fort Belvoir, VA: Defense Technical Information Center, April 2003. http://dx.doi.org/10.21236/ada415803.

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Chen, Dan, and Qiang Zhou. Induction of Apoptosis by Targeting the Microtubule Network: Using HIV Tat as a Model System. Fort Belvoir, VA: Defense Technical Information Center, April 2004. http://dx.doi.org/10.21236/ada428033.

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YU, DANTONG, and Shudong Jin. Arra: Tas::89 0227::Tas Recovery Act 100g Ftp: An Ultra-High Speed Data Transfer Service Over Next Generation 100 Gigabit Per Second Network. Office of Scientific and Technical Information (OSTI), March 2014. http://dx.doi.org/10.2172/1121761.

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Amerio, Silvia. Study of t$\bar{t}$ production in tau jets channel at CDFII using neural networks. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/948188.

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Oppel, Annalena. Beyond Informal Social Protection – Personal Networks of Economic Support in Namibia. Institute of Development Studies (IDS), November 2020. http://dx.doi.org/10.19088/ids.2020.002.

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This paper poses a different lens on informal social protection (ISP). ISP is generally understood as practices of livelihood support among individuals. While studies have explored the social dynamics of such, they rarely do so beyond the conceptual space of informalities and poverty. For instance, they discuss aspects of inclusion, incentives and disincentives, efficiency and adequacy. This provides important insights on whether and to what extent these practices provide livelihood support and for whom. However, doing so in part disregards the socio-political context within which support practices take place. This paper therefore introduces the lens of between-group inequality through the Black Tax narrative. It draws on unique mixed method data of 205 personal support networks of Namibian adults. The results show how understanding these practices beyond the lens of informal social protection can provide important insights on how economic inequality resonates in support relationships, which in turn can play a part in reproducing the inequalities to which they respond.
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Roberts, Peter M., Emily S. Schultz-Fellenz, and Richard E. Kelley. Addressing concerns related to geologic hazards at the site of the proposed Transuranic Waste Facility , TA-63, Los Alamos National Laboratory: focus on the current Los Alamos Seismic Network earthquake catalog, proximity of identified seismic events to the proposed facility , and evaluation of prev. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1038125.

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