Tag Space Research Articles

An Indoor Tags Position Perception Method Based on GWO–MLP Algorithm for RFID Robot

This paper proposes a tag position perception method for scenarios such as package retrieval in unmanned warehouses and book management in libraries. This method can accurately predict the distribution of tag space positions in real–time during RFID robot inventory. Firstly, the signal strength (RSSI) and speed of identification (SoI) are used as features. The grey wolf optimization multi–layer perceptron neural network model (GWO–MLP) is employed to predict the distance of tag groups. Secondly, a tag orientation prediction algorithm is designed to estimate the orientation of the tag groups. Finally, the periodicity of the phase is determined by the characteristic of RSSI attenuation as the tag–to–antenna distance increases, solving the problem of position ambiguity caused by phase periodicity. The experiment has shown that this method achieves a high accuracy rate of 96.67% and 97% in predicting the distance and orientation of tag groups, respectively. The average error in distance perception for the single tag is less than 3 cm, enabling precise perception of RFID tag positions. This method facilitates more efficient operation management and accurate item traceability.

Neural Coupled Sequence Labeling for Heterogeneous Annotation Conversion

Supervised statistical models rely on large-scale high-quality labeled data, which is important for model training but expensive to construct. Therefore, instead of constructing new dataset, researchers have attempted to make full use of various existing heterogeneous datasets to boost model performance, considering it is ubiquitous that the same task may have multiple annotated data following different and incompatible annotation guidelines. Representative methods include the guide-feature method which use the knowledge projected from the source-side to the target-side as extra features for target model guidance, and the multi-task learning (MTL) method which simultaneously train on multiple heterogeneous annotations with shared parameters to gain resource-share knowledge. Though effective, the guide-feature method fails to directly use the source-side data as training data, and the MTL method ignores the implicit mappings between heterogeneous datasets. Compared with the above methods, directly converting the heterogeneous datasets into homogeneous datasets for target model training is a more straightforward and effective way to fully exploit heterogeneous resources. In this work, we propose a neural coupled sequence labeling model for heterogeneous annotation conversion. First, for each token, we map a given one-side tag into a set of bundled tags by concatenating the tag with all the possible tags at the other side. Then, we build a neural coupled model over the bundled tag space. Finally, we convert heterogeneous annotations into homogeneous annotations by performing constraint decoding on the coupled model. We also propose a pruning strategy to address the oversize issue of the bundled tag space, which improves efficiency without hurting model performance.Experiments for part-of-speech (POS) tagging, word segmentation (WS), and WS&POS tagging tasks show that our proposed neural coupled model consistently outperforms several benchmark models for all the three tasks by large margin.

Discovering Relationships among Tags and Geotags

This paper presents an analysis of the correlation of annotated information unit (textual) tags and geographical identification metadata geotags. In this paper, to make it possible for geotagging to be used in analysis with tagging, we prove that there is a strong correlation between tagging and geotagging information. Our approach uses tag similarity and newly employed geographical distribution similarity to determine inter-relationships among tags and geotags. From our initial experiments, we show that the power law is established between tag similarity and geographical distribution similarity; they are strongly correlated and the correlation can be used to find more relevant tags in the tag space. The power law, which is any polynomial relationship that exhibits the property of scale invariance, confirms that there is the relationship between tagging and geotagging and the relationship is scalable in size of tags and geotags.

Automatically Labelled Software Topic Model

Public software repositories (SR) maintain a massive amount of valuable data offering opportunities to support software engineering (SE) tasks. Researchers have applied information retrieval techniques in mining software repositories. Topic models are one of these techniques. However, this technique does not give an interpretation nor labels to the extracted topics and it requires manual analysis to identify them. Some approaches were proposed to automatically label the topics using tags in SR, but they do not consider the existence of spam-tags and they have difficulties to scale to large tag space. This article introduces a novel approach called automatically labelled software topic model (AL-STM) that labels the topics based on observed tags in SR. It mitigates the shortcomings of manual and automatic labelling of topics in SE. AL-STM is implemented using 22K GitHub projects and evaluated in a SE task (tag recommending) against the currently used techniques. The empirical results suggest that AL-STM is more robust in terms of MAP and nDCG, and more scalable to large tag space.

Annotating semantic tags of locations in location-based social networks

Location-based social networks (LBSNs) have become popular platforms that allow users to share their check-in activities with friends. Annotating semantic tags of locations, as one of the hottest research topics in LBSNs, has attracted considerable attention. Semantic annotation requires sufficient location features to train classifiers. Based on the analysis of LBSN data, we find that users’ check-in activities have similarities that can promote the extraction of location features and improve the accuracy of semantic annotation. However, the existing studies ignored the use of the similarities of users’ check-in activities for extracting suitable location features. Therefore, in this paper, a new location feature, called the similar user pattern (SUP), is first extracted by capturing the similarities among the different users’ check-in activities. Second, annotating semantic tags of locations is treated as a multi-label classification problem. Thus, multi-label semantic annotation with an extreme learning machine (ELM) is proposed, called MSA-ELM. The MSA-ELM algorithm trains a binary ELM classifier for each tag in the tag space to support multi-label classification. Finally, a series of experiments are conducted to demonstrate both the accuracy and efficiency of annotating semantic tags of locations. The experimental results show that the SUP is a proper location feature, and the MSA-ELM algorithm has a good performance for multi-label semantic annotation.

Weakly Supervised POS Tagging without Disambiguation

Weakly supervised part-of-speech (POS) tagging is to learn to predict the POS tag for a given word in context by making use of partial annotated data instead of the fully tagged corpora. Weakly supervised POS tagging would benefit various natural language processing applications in such languages where tagged corpora are mostly unavailable. In this article, we propose a novel framework for weakly supervised POS tagging based on a dictionary of words with their possible POS tags. In the constrained error-correcting output codes (ECOC)-based approach, a unique L -bit vector is assigned to each POS tag. The set of bitvectors is referred to as a coding matrix with value { 1, -1}. Each column of the coding matrix specifies a dichotomy over the tag space to learn a binary classifier. For each binary classifier, its training data is generated in the following way: each pair of words and its possible POS tags are considered as a positive training example only if the whole set of its possible tags falls into the positive dichotomy specified by the column coding and similarly for negative training examples. Given a word in context, its POS tag is predicted by concatenating the predictive outputs of the L binary classifiers and choosing the tag with the closest distance according to some measure. By incorporating the ECOC strategy, the set of all possible tags for each word is treated as an entirety without the need of performing disambiguation. Moreover, instead of manual feature engineering employed in most previous POS tagging approaches, features for training and testing in the proposed framework are automatically generated using neural language modeling. The proposed framework has been evaluated on three corpora for English, Italian, and Malagasy POS tagging, achieving accuracies of 93.21%, 90.9%, and 84.5% individually, which shows a significant improvement compared to the state-of-the-art approaches.

Tag-aware dynamic music recommendation

We present a tag-aware dynamic music recommendation framework that achieves personalized and accurate music recommendations to users. The proposed framework leverages the available semantic labels (in terms of tags) of music tracks to complement a highly sparse user-item interaction matrix, which effectively addresses the data sparsity issue faced by most music recommendation systems. Music tracks are more accurately represented by aggregating the latent factors derived from both the tag space and the user interaction information. The proposed framework further employs a Gaussian state-space model to capture the evolving nature of users’ preferences over time, which helps achieve time-sensitive recommendation of music. A variational approximation is developed to achieve fast inference and learning of model parameters. Experiments conducted using actual music data and comparison with state-of-the-art competitive recommendation algorithms help demonstrate the effectiveness of the proposed framework.

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.

Homophilic replicator equations.

Tags are conspicuous attributes of organisms that affect the behaviour of other organisms toward the holder, and have previously been used to explore group formation and altruism. Homophilic imitation, a form of tag-based selection, occurs when organisms imitate those with similar tags. Here we further explore the use of tag-based selection by developing homophilic replicator equations to model homophilic imitation dynamics. We assume that replicators have both tags (sometimes called traits) and strategies. Fitnesses are determined by the strategy profile of the population, and imitation is based upon the strategy profile, fitness differences, and similarity in tag space. We show the characteristics of resulting fixed manifolds and conditions for stability. We discuss the phenomenon of coat-tailing (where tags associated with successful strategies increase in abundance, even though the tags are not inherently beneficial) and its implications for population diversity. We extend our model to incorporate recurrent mutations and invasions to explore their implications upon tag and strategy diversity. We find that homophilic imitation based upon tags significantly affects the diversity of the population, although not the ESS. We classify two different types of invasion scenarios by the strategy and tag compositions of the invaders and invaded. In one scenario, we find that novel tags introduced by invaders become more readily established with homophilic imitation than without it. In the other, diversity decreases. Lastly, we find a negative correlation between homophily and the rate of convergence.

Tag-aware recommender systems based on deep neural networks

Many researchers have introduced tag information to recommender systems to improve the performance of traditional recommendation techniques. However, user-defined tags will usually suffer from many problems, such as sparsity, redundancy, and ambiguity. To address these problems, we propose a new recommendation algorithm based on deep neural networks. In the proposed algorithm, users׳ profiles are initially represented by tags and then a deep neural network model is used to extract the in-depth features from tag space layer by layer. In this way, representations of the raw data will become more abstract and advanced, and therefore the unique structure of tag space will be revealed automatically. Based on those extracted abstract features, users׳ profiles are updated and used for making recommendations. The experimental results demonstrate the usefulness of the proposed algorithm and show its superior performance over the clustering based recommendation algorithms. In addition, the impact of network depth on the algorithm performance is also investigated.

Tag-mediated cooperation with non-deterministic genotype-phenotype mapping

Tag-mediated cooperation provides a helpful framework for resolving evolutionary social dilemmas. However, most of the previous studies have not taken into account genotype-phenotype distinction in tags, which may play an important role in the process of evolution. To take this into consideration, we introduce non-deterministic genotype-phenotype mapping into a tag-based model with spatial prisoner's dilemma. By our definition, the similarity between genotypic tags does not directly imply the similarity between phenotypic tags. We find that the non-deterministic mapping from genotypic tag to phenotypic tag has non-trivial effects on tag-mediated cooperation. Although we observe that high levels of cooperation can be established under a wide variety of conditions especially when the decisiveness is moderate, the uncertainty in the determination of phenotypic tags may have a detrimental effect on the tag mechanism by disturbing the homophilic interaction structure which can explain the promotion of cooperation in tag systems. Furthermore, the non-deterministic mapping may undermine the robustness of the tag mechanism with respect to various factors such as the structure of the tag space and the tag flexibility. This observation warns us about the danger of applying the classical tag-based models to the analysis of empirical phenomena if genotype-phenotype distinction is significant in real world. Non-deterministic genotype-phenotype mapping thus provides a new perspective to the understanding of tag-mediated cooperation.

Projective nonnegative matrix factorization for social image retrieval

Increasingly many social images with tags are available on photo sharing websites. Due to the subjectivity and diversity of social tagging behaviors, noisy and missing tags for images are inevitable. To tackle this problem, this paper proposes a novel factor analysis model, named ProjecTive Nonnegative Matrix Factorization (PTNMF) with ℓ2,1-norm regularization, which introduces linear transformation and ℓ2,1-norm minimization into a joint framework of NMF. For tagging data, a new interpretation is adopted to distinguish the relevant tags and irrelevant tags instead of the typically used binary scheme. In our model, the image latent representation is assumed to be projected from its original feature representation with an orthogonal transformation matrix. The projection makes convenient to embed any images including out-of-samples into the latent space. That is, the proposed method enables to handle the out-of-sample problem. The ℓ2,1-norm regularization makes the transformation matrix suitable for selecting the effective features. Local geometry preservations of image space (tag space) are explored as constraints in order to make image similarity (tag correlation) consistent in the original space and the corresponding latent space. We investigate the performance of the proposed method on image retrieval and compare it to existing work on the challenging NUS-WIDE dataset. Extensive experiments indicate the effectiveness and potentials of the proposed method in real-world applications.

Learning to rank image tags with limited training examples.

With an increasing number of images that are available in social media, image annotation has emerged as an important research topic due to its application in image matching and retrieval. Most studies cast image annotation into a multilabel classification problem. The main shortcoming of this approach is that it requires a large number of training images with clean and complete annotations in order to learn a reliable model for tag prediction. We address this limitation by developing a novel approach that combines the strength of tag ranking with the power of matrix recovery. Instead of having to make a binary decision for each tag, our approach ranks tags in the descending order of their relevance to the given image, significantly simplifying the problem. In addition, the proposed method aggregates the prediction models for different tags into a matrix, and casts tag ranking into a matrix recovery problem. It introduces the matrix trace norm to explicitly control the model complexity, so that a reliable prediction model can be learned for tag ranking even when the tag space is large and the number of training images is limited. Experiments on multiple well-known image data sets demonstrate the effectiveness of the proposed framework for tag ranking compared with the state-of-the-art approaches for image annotation and tag ranking.

Rough set-based meta-heuristic clustering approach for social e-learning systems

An imperative challenge of Web 2.0 is the way that an incredible measure of information has been incited over a brief time. Tags are generally used to dig and arrange the Web 2.0 resources. Clustering the tag information is exceptionally dreary since the tag space is significant in a few social tagging sites. Tag clustering is the method of collecting the comparative tags into groups. The tag clustering is truly helpful for searching and arranging the Web 2.0 resources furthermore vital for the achievement of social tagging systems. In this paper, the clustering techniques apply to the social e-learning tagging system http://www.pumrpelearning.com; furthermore, we proposed a hybrid tolerance rough set-based particle swarm optimisation TRS-PSO for clustering tags. At that stage, the proposed technique is contrasted with benchmark clustering algorithm k-means with particle swarm optimisation PSO-based grouping method. The exploratory investigation represents the character of the suggested methodology.

STEGANOGRAPHY APPLICATION PROGRAM USING THE ID3V2 IN THE MP3 AUDIO FILE ON MOBILE PHONE

The MPEG Layer-III File or MP3 can contain the ID3v2 tag which able to store information not only the song and singer’s name but also song lyric and alblum cover figure. The steganography technique on audio generally only make use of the main body of the audio file, but has not ever use the part of the ID3v2 to hide an message. This study an steganography technique which make use of this ID3v2 tag space where message is encrypted using McEliece cryptosystem method which applying the public key in the form of matrix then inserted into the MP3 file applying Before All Frames (BAF) method, whereas the public key is inserted into album cover figure which is stored in the ID3v2 tag.

Projective Matrix Factorization with unified embedding for social image tagging

This paper presents a general formulation, named ProJective Matrix Factorization with unified embedding (PJMF), by which social image retagging is transformed to the nearest tag-neighbor search for each image. We solve the proposed PJMF as an optimization problem mainly considering the following issues. First, we attempt to find two latent representations in a unified space for images and tags respectively and explore the two representations to reconstruct the observed image-tag correlation in a nonlinear manner. In this case, the relevance between an image and a tag can be directly modeled as the pair-wise similarity in the unified space. Second, the image latent representation is assumed to be projected from its original visual feature representation with an orthogonal transformation matrix. The projection makes convenient to embed any images including out-of-samples into the unified space, and naturally the image retagging problem can be solved by the nearest tag-neighbors search for those images in the unified space. Third, local geometry preservations of image space and tag space respectively are explored as constraints in order to make image similarity (and tag relevance) consistent in the original space and the corresponding latent space. Experimental results on two publicly available benchmarks validate the encouraging performance of our work over the state-of-the-arts.

Molecule: Sistema de organização e visualização de Tags

Several platforms allow users to tag resources and share information. Over time various forms of tags visualization have been developed in order to facilitate the visualization of the tag space and the search and retrieval of resources. The tag cloud stands out as the most common form of tags visualization. This paper presents a study carried out on their limitations and proposes an alternative. It also suggests a new interpretation on how to search and view information associated with tags, thus differing from the method of direct search term in the database that is currently used mostly. As a result of this interpretation, a viable and innovative system was achieved, Molecule, that overcome some of the problems associated with the traditional tag cloud.

Using a Network Analysis Approach for Organizing Social Bookmarking Tags and Enabling Web Content Discovery

This article describes an innovative approach to reorganizing the tag space generated by social bookmarking services. The objective of this work is to enable effective search and discovery of Web content using social bookmarking tags. Tags are metadata generated by users for Web content annotation. Their potential as effective Web search and discovery tool is hindered by challenges such as, the tag space being untidy due to ambiguity, and hidden or implicit semantics. Using a novel analytics approach, we conducted network analyses on tags and discovered that tags are generated for different purposes and that there are inherent relationships among tags. Our approach can be used to extract the purposes of tags and relationships among the tags and this information can be used as facets to add structure and hierarchy to reorganize the flat tag space. The semantics of relationships and hierarchy in our proposed faceted model of tags enable searches on annotated Web content in an effective manner. We describe the implementation of a prototype system called FASTS to demonstrate feasibility and effectiveness of our approach.

Content-based and collaborative techniques for tag recommendation: an empirical evaluation

The rapid growth of the so-called Web 2.0 has changed the surfers' behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users' mental model; secondly, the absence of a controlled vocabulary reduces the users' learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results.

Evolutionary taxonomy construction from dynamic tag space

Collaborative tagging becomes a common feature of current web sites, facilitating ordinary users to annotate and represent online resources. The large collection of tags and their relationships form a tag space. In this kind of tag space, the popularity and correlation amongst tags capture the current social interests. Tags are freely chosen keywords and difficult to organize. As a hierarchical concept structure to represent the subsumption relationships, automatically extracted taxonomies become a viable method to manage collaborative tags. However, tags change over time, and it is also imperative to incorporate the temporal tag evolution into the extracted taxonomies. In this paper, we formalize the problem of evolutionary taxonomy generation over a large collection of tags. A line of taxonomies are generated to reflect the temporal changes of underlying tag space. The proposed evolutionary taxonomy framework consists of two novel contributions. First, we develop a context-aware edge selection algorithm for taxonomy extraction. This method is built on seminal association-rule mining algorithm. Second, we propose several strategies for evolutionary taxonomy fusion, which smooths the newly generated taxonomy with prior ones. We conduct an extensive performance study using a large real-life web page tagging dataset (i.e., Del.ici.ous). The empirical results clearly verify the effectiveness and efficiency of the proposed approach.