Tag Information Research Articles

Similarity Analysis-Based Indoor Localization Algorithm With Backscatter Information of Passive UHF RFID Tags

Passive radio frequency identification (RFID) is a low cost and low complexity localization technology. With introduction of reference tags, RFID technology can provide on-line reference information for pattern matching localization algorithms. One of the most popular algorithms is $k$ -nearest neighbor algorithm. While how to decide the $K$ value in estimation is still a problem. In addition, traditional received signal strength indicator (RSSI)-based algorithms suffer from multipath effects, it is difficult to further improve the localization accuracy. In this paper, we analyze the similarity of backscatter signals and present a novel range based indoor localization method, SAIL. According to the propagation distances of signals, reference tags that have similar distances to a reader antenna can be considered as a group. Inspired by the idea of grouping, clustering algorithm is utilized to get candidate tags for ranging. This method does not need to fix the number of reference tags in estimation. Besides RSSI, the RFID reader can also extract phase of signals coming from backscattered signals of responding tags. This guides us to combine RSSI and phase in similarity measure for clustering. In the simulation and experimental tests, SAIL is superior to other RFID schemes considering cost, location estimation error, and flexibility.

Systemic Identification of Hevea brasiliensis EST-SSR Markers and Primer Screening.

This research aimed to systematically identify and preliminarily validate the Hevea brasiliensis expressed sequence tag (EST) information using Simple Sequence Repeat (SSR) and provide evidence for further development of SSR molecular marker. The definition of general SSR features of Hevea EST splicing sequences and development of SSR primers founded the basis of diversity analysis and variety identification for Hevea tree resource. 1134 SSR loci were identified in the EST splicing sequence and distributed in 840 Unigene. The occurrence rate of SSR loci was 23.9%, and the average distribution distance of EST-SSR was 2.59 kb. The major repeat type was mononucleotide repeat motif, which accounted for 38.89%, while the corresponding value was 36.95% for dinucleotide repeat motif and 18.17% for trinucleotide repeat motif; the proportion of other motifs was only 5.99%. The superior repeat motifs for mononucleotide, dinucleotide, and trinucleotide were A/T, AG/CT, and AAG/CTT, respectively. 739 pair of primers were designed for 1134 SSR loci. PCR amplification was performed on Hevea Reyan5-11, Reyan87-6-47, and PR107, and 180 pairs of primers were selected which were able to amplify polymorphism bands.

A collaborative filtering recommendation method based on TagIEA expert degree model

In recent years, social networking services and e-commerce have been developing rapidly. The research of recommending in e-commerce service mainly focused on using the collaborative filtering algorithm. But the algorithm had the limitations of data sparsity and cold start. This paper presents a model using TagIEA expert degree metrics in the context of social e-commerce services, where tag and expert degree information are integrated into the collaborative filtering algorithm. The comprehensive recommendation based on the TagIEA expert degree can effectively mitigate the problems of cold start and data sparsity. Finally, this paper verifies the effectiveness of the improved collaborative filtering algorithm by experiments.

Spam Detection in Social Bookmarking Systems Using Tag Scores and Selective Evaluation

In this paper we study spam detection in social bookmarking systems. We analyze posts and develop features to find spammers from users by focusing on analysis of tags. In calculating tag scores, we carefully select tags to give different weights depending on how much spammer and non-spammers like them. To further enhance performance, we propose features based on semantic similarity, tagging tendency, and quantitative tag information and combine them with the tag features. We evaluate the features using the well-known dataset and see they work in detecting spammers. In addition, we do experiments to see how our proposed features work in different environments, namely, different number of posts per user, different classifiers employed, and different levels of feature computation.

A Novel Blockchain-Based Product Ownership Management System (POMS) for Anti-Counterfeits in the Post Supply Chain

For more than a decade now, radio frequency identification (RFID) technology has been quite effective in providing anti-counterfeits measures in the supply chain. However, the genuineness of RFID tags cannot be guaranteed in the post supply chain, since these tags can be rather easily cloned in the public space. In this paper, we propose a novel product ownership management system (POMS) of RFID-attached products for anti-counterfeits that can be used in the post supply chain. For this purpose, we leverage the idea of Bitcoin ’s blockchain that anyone can check the proof of possession of balance. With the proposed POMS, a customer can reject the purchase of counterfeits even with genuine RFID tag information, if the seller does not possess their ownership. We have implemented a proof-of-concept experimental system employing a blockchain-based decentralized application platform, Ethereum , and evaluated its cost performance. Results have shown that, typically, the cost of managing the ownership of a product with up to six transfers is less than U.S. $1.

Dominant Channel Occupancy for Wi-Fi Backscatter Uplink in Industrial Internet of Things

This paper presents a dominant channel occupancy (DCO) mechanism for the Wi-Fi backscatter uplink in the industrial Internet of things (IIoT). The DCO provides high-priority channel access and reliable burst transmission to the Wi-Fi backscatter devices, thereby enabling the Wi-Fi backscatter tag to deliver its tag information to the Wi-Fi reader without interference from neighboring legacy Wi-Fi devices to guarantee the timeliness and reliability of the IIoT system. For the former, we consider three types of medium access control (MAC) configurations: “carrier sense multiple access with collision avoidance (CSMA/CA) starting with short inter-frame space (SIFS)”, “freezing of the backoff period”, and “reduced CWmin.” In addition, the DCO uses the SIFS between burst packets to guarantee reliable burst transmission. To verify the effectiveness of DCO and determine a proper value for MAC parameters, we conduct experimental simulations under IEEE 802.11n PHY/MAC environments. The simulation results show that the reduced CWmin has the most significant effect on the channel occupancy. The Wi-Fi backscatter devices achieve much higher throughput than the separate cases when two or more configurations are used simultaneously. Moreover, the results exhibit that the use of SIFS between consecutive packets supports reliable burst transmission regardless of the transmission of the legacy Wi-Fi devices in the vicinity.

Comparison and Analysis of Tag-Ranking Algorithms based on Clustering

Most information in Web 2.0 is made by users and classified by tags assigned by users. Tag-related services and research are focused on work such as automatic tagging and tag-cloud composition; however, classifying media resources and information according to tags and providing the results to users is not still up to the mark. In this paper, image resources and their tag information scattered in the web are collected and a tag-pair weight matrix is created, according to the relations and semantic similarities between tags. To overcome the problems of the existing system, a tag-pair weight matrix-based tag clustering (TBTC) algorithm was proposed to find highly related tags. The threshold used for clustering in this algorithm was studied, and an optimal threshold with high cluster cohesion was determined. Finally, as an experiment, 500 images with the keyword 'tomato' were searched from the Flickr website and highly related tags were derived from the proposed algorithm. The results of this research were examined and compared with the results of existing studies. It was found that the proposed research showed more advanced accuracy and precision than earlier methods.

Weakly Supervised Deep Matrix Factorization for Social Image Understanding.

The number of images associated with weakly supervised user-provided tags has increased dramatically in recent years. User-provided tags are incomplete, subjective and noisy. In this paper, we focus on the problem of social image understanding, i.e., tag refinement, tag assignment, and image retrieval. Different from previous work, we propose a novel weakly supervised deep matrix factorization algorithm, which uncovers the latent image representations and tag representations embedded in the latent subspace by collaboratively exploring the weakly supervised tagging information, the visual structure, and the semantic structure. Due to the well-known semantic gap, the hidden representations of images are learned by a hierarchical model, which are progressively transformed from the visual feature space. It can naturally embed new images into the subspace using the learned deep architecture. The semantic and visual structures are jointly incorporated to learn a semantic subspace without overfitting the noisy, incomplete, or subjective tags. Besides, to remove the noisy or redundant visual features, a sparse model is imposed on the transformation matrix of the first layer in the deep architecture. Finally, a unified optimization problem with a well-defined objective function is developed to formulate the proposed problem and solved by a gradient descent procedure with curvilinear search. Extensive experiments on real-world social image databases are conducted on the tasks of image understanding: image tag refinement, assignment, and retrieval. Encouraging results are achieved with comparison with the state-of-the-art algorithms, which demonstrates the effectiveness of the proposed method.

Putting the behavior into animal movement modeling: Improved activity budgets from use of ancillary tag information.

Animal movement research relies on biotelemetry, and telemetry‐based locations are increasingly augmented with ancillary information. This presents an underutilized opportunity to enhance movement process models. Given tags designed to record specific behaviors, efforts are increasing to update movement models beyond reliance solely upon horizontal movement information to improve inference of space use and activity budgets. We present two state‐space models adapted to incorporate ancillary data to inform three discrete movement states: directed, resident, and an activity state. These were developed for two case studies: (1) a “haulout” model for Weddell seals, and (2) an “activity” model for Antarctic fur seals which intersperse periods of diving activity and inactivity. The methodology is easily implementable with any ancillary data that can be expressed as a proportion (or binary) indicator. A comparison of the models augmented with ancillary information and unaugmented models confirmed that many behavioral states appeared mischaracterized in the latter. Important changes in subsequent activity budgets occurred. Haulout accounted for 0.17 of the overall Weddell seal time budget, with the estimated proportion of time spent in a resident state reduced from a posterior median of 0.69 (0.65–0.73; 95% HPDI) to 0.54 (0.50–0.58 HPDI). The drop was more dramatic in the Antarctic fur seal case, from 0.57 (0.52–0.63 HPDI) to 0.22 (0.20–0.25 HPDI), with 0.35 (0.31–0.39 HPDI) of time spent in the inactive (nondiving) state. These findings reinforce previously raised contentions about the drawbacks of behavioral states inferred solely from horizontal movements. Our findings have implications for assessing habitat requirements; estimating energetics and consumption; and management efforts such as mitigating fisheries interactions. Combining multiple sources of information within integrated frameworks should improve inference of relationships between movement decisions and fitness, the interplay between resource and habitat dependencies, and their changes at the population and landscape level.

Generic and Automated Data Evaluation in Analytical Measurement.

In the past year, automation has become more and more important in the field of elemental and structural chemical analysis to reduce the high degree of manual operation and processing time as well as human errors. Thus, a high number of data points are generated, which requires fast and automated data evaluation. To handle the preprocessed export data from different analytical devices with software from various vendors offering a standardized solution without any programming knowledge should be preferred. In modern laboratories, multiple users will use this software on multiple personal computers with different operating systems (e.g., Windows, Macintosh, Linux). Also, mobile devices such as smartphones and tablets have gained growing importance. The developed software, Project Analytical Data Evaluation (ADE), is implemented as a web application. To transmit the preevaluated data from the device software to the Project ADE, the exported XML report files are detected and the included data are imported into the entities database using the Data Upload software. Different calculation types of a sample within one measurement series (e.g., method validation) are identified using information tags inside the sample name. The results are presented in tables and diagrams on different information levels (general, detailed for one analyte or sample).

Combining Neighborhood Based Collaborative Filtering with Tag Information for Personalized Recommendation

Collaborative filtering recommendation is one of the most effective recommending techniques, which provide customers with suggestions according to their interests. However, neighborhood based collaborative filtering methods confront great challenges of data sparsity and lack of accessorial information in the context of big data. To address these problems, we propose a hybrid model combining tag information and neighborhood based collaborative filtering. A folksonomy network model based on tag information is proposed to analyze the tag relevance between different items. And tag relevance is incorporated into rating prediction of neighborhood based collaborative filtering for improving the recommendation accuracy. Experiments on MovieLens and Netflix datasets are carried out to evaluate the performance of our method. The results show that our method outperforms other methods and can improve recommending quality effectively.

Tri-Clustered Tensor Completion for Social-Aware Image Tag Refinement.

Social image tag refinement, which aims to improve tag quality by automatically completing the missing tags and rectifying the noise-corrupted ones, is an essential component for social image search. Conventional approaches mainly focus on exploring the visual and tag information, without considering the user information, which often reveals important hints on the (in)correct tags of social images. Towards this end, we propose a novel tri-clustered tensor completion framework to collaboratively explore these three kinds of information to improve the performance of social image tag refinement. Specifically, the inter-relations among users, images and tags are modeled by a tensor, and the intra-relations between users, images and tags are explored by three regularizations respectively. To address the challenges of the super-sparse and large-scale tensor factorization that demands expensive computing and memory cost, we propose a novel tri-clustering method to divide the tensor into a certain number of sub-tensors by simultaneously clustering users, images and tags into a bunch of tri-clusters. And then we investigate two strategies to complete these sub-tensors by considering (in)dependence between the sub-tensors. Experimental results on a real-world social image database demonstrate the superiority of the proposed method compared with the state-of-the-art methods.

The construction of mobile RFID authentication mechanism and relative ownership transfer protocols for supply chain

Purpose – The purpose of this paper is to complete the following works: construct RFID integration mechanism in the supply chain; build mutual authentication mechanism; and build the transfer mechanism in supply chain. Design/methodology/approach – In order to transfer RFID tag information to others safely in mobile RFID supply chain circumstances, the authors propose a RFID authentication mechanism which can ensure the identity and make the mutual authentication. Moreover, the authors propose a secure transfer method of ownership for the specific role in different regions under the structure of Mobile RFID. Findings – The task of administrator server is to check the order, store the merchandise information, distribute the authority, certify the security and transfer the ownership. It can make the whole supply chain more mobility, and transfer faster. Originality/value – The goals of this research are to achieve mainly: using mobile RFID system to design secure mutual authentication mechanism, transfer mechanisms with faster transfer and authority distribution. The authors introduce the security authentication and competence transfer mechanism in supply chain.

Development of a panel of unigene-derived polymorphic EST–SSR markers in lentil using public database information

Lentil (Lens culinaris Medik.), a diploid (2n=14) with a genome size greater than 4000Mbp, is an important cool season food legume grown worldwide. The availability of genomic resources is limited in this crop species. The objective of this study was to develop polymorphic markers in lentil using publicly available curated expressed sequence tag information (ESTs). In this study, 9513 ESTs were downloaded from the National Center for Biotechnology Information (NCBI) database to develop unigene-based simple sequence repeat (SSR) markers. The ESTs were assembled into 4053 unigenes and then analyzed to identify 374 SSRs using the MISA microsatellite identification tool. Among the 374 SSRs, 26 compound SSRs were observed. Primer pairs for these SSRs were designed using Primer3 version 1.14. To classify the functional annotation of ESTs and EST–SSRs, BLASTx searches (using E-value 1×10−5) against the public UniProt (http://www.uniprot.org/) and NCBI (http://www.ncbi.nlh.nih.gov/) databases were performed. Further functional annotation was performed using PLAZA (version 3.0) comparative genomics and GO annotation was summarized using the Plant GO slim category. Among the synthesized 312 primers, 219 successfully amplified Lens DNA. A diverse panel of 24 Lens genotypes was used to identify polymorphic markers. A polymorphic set of 57 markers successfully discriminated the test genotypes. This set of polymorphic markers with functional annotation data could be used as molecular tools in lentil breeding.

Magnetic Suspension Array Technology: Controlled Synthesis and Screening in Microfluidic Networks.

Information tagging and processing are vital in information-intensive applications, e.g., telecommunication and high-throughput drug screening. Magnetic suspension array technology may offer intrinsic advantages to screening applications by enabling high distinguishability, the ease of code generation, and the feasibility of fast code readout, though the practical applicability of magnetic suspension array technology remains hampered by the lack of quality administration of encoded microcarriers. Here, a logic-controlled microfluidic system enabling controlled synthesis of magnetic suspension arrays in multiphase flow networks is realized. The smart and compact system offers a practical solution for the quality administration and screening of encoded magnetic microcarriers and addresses the universal need of process control for synthesis in microfluidic networks, i.e., on-demand creation of droplet templates for high information capacity. The demonstration of magnetic suspension array technology enabled by magnetic in-flow cytometry opens the avenue toward point-of-care multiplexed bead-based assays, clinical diagnostics, and drug discovery.

A Model based Resource Recommender System on Social Tagging Data

Web (2.0) is the place where people can upload, share and access various sources of information. Web (2.0) has given rise to information overloading problem and knowledge starvation. Recommender Systems (RS) helps in alleviating this overloading problem and gaining the exact information what we need. RS suggest user items or products based on their browsing or purchasing history. RS suggest list of items by identifying similar users with explicit user-item rating. But, in real time applications most users do not rate items. In current web (2.0) social tagging applications help us to find useritem ratings implicitly based on the user’s interest and preferences they give for the list of items. In this paper we have proposed a model based resource recommendation on social tagging information which has improved the performance of the RS. In the proposed system the topic is identified from the tagged data, based on the topic user profile is constructed by semantic approach and the recommendation is done for the user.

Design of Warehouse Management System Using IPS under Bluetooth Environment

Convergence of manufacturing industry and ICT becomes a new competence and it is also regarded as revolution of production method. At the warehouse, monitoring information of the job order and completion is needed for the judgement of the process whether it has any problems or none. For this kind of action, database system that can interrelate material information and job order information should be adapted. Thus, the author suggest the warehouse management system using IPS(Indoor Positioning System) under Bluetooth environment. This system can make material management, job control management and location management using Tag information. It can also be utilized in existing warehouse.

Similarity Search for Trajectories of RFID Tags in Supply Chain Traffic

In this fast developing period the use of RFID have become more significant in many application domaindue to drastic cut down in the price of the RFID tags. This technology is evolving as a means of tracking objects and inventory items. One such diversified application domain is in Supply Chain Management where RFID is being applied as the manufacturers and distributers need to analyse product and logistic information in order to get the right quantity of products arriving at the right time to the right locations. Usually the RFID tag information collected from RFID readers is stored in remote database and the RFID data is being analyzed by querying data from this database based on path encoding method by the property of prime numbers. In this paper we propose an improved encoding scheme that encodes the flows of objects in RFID tag movement. A Trajectory of moving RFID tags consists of a sequence of tagsthat changes over time. With the integration of wireless communications and positioning technologies, the concept of Trajectory Database has become increasingly important, and has posed great challenges to the data mining community.The support of efficient trajectory similarity techniques is indisputably very important for the quality of data analysis tasks in Supply Chain Traffic which will enable similar product movements.

A framework for diversifying recommendation lists by user interest expansion

Recommender systems have been widely used to discover users’ preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N recommendation list involves not only relevance but also diversity. Most traditional recommendation algorithms are difficult to generate a diverse item list that can cover most of his/her interests for each user, since they mainly focus on predicting accurate items similar to the dominant interests of users. Additionally, they seldom exploit semantic information such as item tags and users’ interest labels to improve recommendation diversity. In this paper, we propose a novel recommendation framework which mainly adopts an expansion strategy of user interests based on social tagging information. The framework enhances the diversity of users’ preferences by expanding the sizes and categories of the original user-item interaction records, and then adopts traditional recommendation models to generate recommendation lists. Empirical evaluations on three real-world data sets show that our method can effectively improve the accuracy and diversity of item recommendation.

Trinity: Walking on a User-Object-Tag Heterogeneous Network for Personalised Recommendations

The rapid evolution of the Internet has been appealing for effective recommender systems to pinpoint useful information from online resources. Although historical rating data has been widely used as the most important information in recommendation methods, recent advancements have been demonstrating the improvement in recommendation performance with the incorporation of tag information. Furthermore, the availability of tag annotations has been well addressed by such fruitful online social tagging applications as CiteULike, MovieLens and BibSonomy, which allow users to express their preferences, upload resources and assign their own tags. Nevertheless, most existing tag-aware recommendation approaches model relationships among users, objects and tags using a tripartite graph, and hence overlook relationships within the same types of nodes. To overcome this limitation, we propose a novel approach, Trinity, to integrate historical data and tag information towards personalised recommendation. Trinity constructs a three-layered object-user-tag network that considers not only interconnections between different types of nodes but also relationships within the same types of nodes. Based on this heterogeneous network, Trinity adopts a random walk with restart model to assign the strength of associations to candidate objects, thereby providing a means of prioritizing the objects for a query user. We validate our approach via a series of large-scale 10-fold cross-validation experiments and evaluate its performance using three comprehensive criteria. Results show that our method outperforms several existing methods, including supervised random walk with restart, simulation of resource allocating processes, and traditional collaborative filtering.