Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery

Resource discovery is one of the key services in digitised cultural heritage collections. It requires intelligent mining in heterogeneous digital content as well as capabilities in large scale performance; this explains the recent advances in classification methods. Associative classifiers are convenient data mining tools used in the field of cultural heritage, by applying their possibilities to taking into account the specific combinations of the attribute values. Usually, the associative classifiers prioritize the support over the confidence. The proposed classifier PGN questions this common approach and focuses on confidence first by retaining only 100% confidence rules. The classification tasks in the field of cultural heritage usually deal with data sets with many class labels. This variety is caused by the richness of accumulated culture during the centuries. Comparisons of classifier PGN with other classifiers, such as OneR, JRip and J48, show the competitiveness of PGN in recognizing multi-class datasets on collections of masterpieces from different West and East European Fine Art authors and movements.

Unfolding kernel embeddings of graphs:enhancing class separation through manifold learning

In this paper, we investigate the use of manifold learning techniques to enhance the separation properties of standard graph kernels. The idea stems from the observation that when we perform multidimensional scaling on the distance matrices extracted from the kernels, the resulting data tends to be clustered along a curve that wraps around the embedding space, a behavior that suggests that long range distances are not estimated accurately, resulting in an increased curvature of the embedding space. Hence, we propose to use a number of manifold learning techniques to compute a low-dimensional embedding of the graphs in an attempt to unfold the embedding manifold, and increase the class separation. We perform an extensive experimental evaluation on a number of standard graph datasets using the shortest-path (Borgwardt and Kriegel, 2005), graphlet (Shervashidze et al., 2009), random walk (Kashima et al., 2003) and Weisfeiler-Lehman (Shervashidze et al., 2011) kernels. We observe the most significant improvement in the case of the graphlet kernel, which fits with the observation that neglecting the locational information of the substructures leads to a stronger curvature of the embedding manifold. On the other hand, the Weisfeiler-Lehman kernel partially mitigates the locality problem by using the node labels information, and thus does not clearly benefit from the manifold learning. Interestingly, our experiments also show that the unfolding of the space seems to reduce the performance gap between the examined kernels.

Attributed graph kernels using the Jensen-Tsallis q-differences

We propose a family of attributed graph kernels based on mutual information measures, i.e., the Jensen-Tsallis (JT) q-differences (for q  ∈ [1,2]) between probability distributions over the graphs. To this end, we first assign a probability to each vertex of the graph through a continuous-time quantum walk (CTQW). We then adopt the tree-index approach [1] to strengthen the original vertex labels, and we show how the CTQW can induce a probability distribution over these strengthened labels. We show that our JT kernel (for q  = 1) overcomes the shortcoming of discarding non-isomorphic substructures arising in the R-convolution kernels. Moreover, we prove that the proposed JT kernels generalize the Jensen-Shannon graph kernel [2] (for q = 1) and the classical subtree kernel [3] (for q = 2), respectively. Experimental evaluations demonstrate the effectiveness and efficiency of the JT kernels.

Trans-language, trans-culture, trans-translation?

The growth of the discipline of translation studies has been accompanied by are newed reflection on the object of research and our metalanguage. These developments have also been necessitated by the diversification of professions within the language industry. The very label translation is often avoided in favour of alternative terms, such as localisation (of software), trans creation (of advertising), trans editing (of information from press agencies). The competences framework developed for the European Master’s in Translation network speaks of experts in multilingual and multimedia communication to account for the complexity of translation competence. This paper addresses the following related questions: (i) How can translation competence in such awide sense be developed in training programmes? (ii) Do some competences required in the industry go beyond translation competence? and (iii) What challenges do labels such as trans creation pose?

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

The Routledge handbook of forensic linguistics

The Routledge Handbook of Forensic Linguistics provides a unique work of reference to the leading ideas, debates, topics, approaches and methodologies in Forensic Linguistics. Forensic Linguistics is the study of language and the law, covering topics from legal language and courtroom discourse to plagiarism. It also concerns the applied (forensic) linguist who is involved in providing evidence, as an expert, for the defence and prosecution, in areas as diverse as blackmail, trademarks and warning labels. The Routledge Handbook of Forensic Linguistics includes a comprehensive introduction to the field written by the editors and a collection of thirty-seven original chapters written by the world’s leading academics and professionals, both established and up-and-coming, designed to equip a new generation of students and researchers to carry out forensic linguistic research and analysis. The Routledge Handbook of Forensic Linguistics is the ideal resource for undergraduates or postgraduates new to the area.

Semi-supervised Gaussian process latent variable model with pairwise constraints

In machine learning, Gaussian process latent variable model (GP-LVM) has been extensively applied in the field of unsupervised dimensionality reduction. When some supervised information, e.g., pairwise constraints or labels of the data, is available, the traditional GP-LVM cannot directly utilize such supervised information to improve the performance of dimensionality reduction. In this case, it is necessary to modify the traditional GP-LVM to make it capable of handing the supervised or semi-supervised learning tasks. For this purpose, we propose a new semi-supervised GP-LVM framework under the pairwise constraints. Through transferring the pairwise constraints in the observed space to the latent space, the constrained priori information on the latent variables can be obtained. Under this constrained priori, the latent variables are optimized by the maximum a posteriori (MAP) algorithm. The effectiveness of the proposed algorithm is demonstrated with experiments on a variety of data sets. © 2010 Elsevier B.V.

An empirical study of international private label branding in the food sector in Hungary

Private label branding strategies differ to that of the manufacturer. The study aims to identify optimal private label branding strategies for (a) utilitarian products and (b) hedonistic products, considering the special factors reflected in consumer behavior related to private labels in Hungary. The issue of House of Brands and Branded House strategies are discussed and evaluated in the light of retail business models. Focus group interviews and factor analysis of the survey found differences in branding strategies preferred by consumers for the two product categories. The study also outlines a strong trend in possible private label development based on consumer’s changing attitude in favor of national products.

Alternative ways for private label manufacturing

Private labels are a growing phenomenon globaly. retatlers become stronger and stronger by offering their own quality private label product for customers in all segments. Certainly they do not open factories to produce these items but rather search for dedicated private label producers or pressure branded goods manufacturers to produce it for them. The article deals with the strategic choiches manufacturers can have and suggest the necessary factors that need to be evaluated to decide on the winning business model - in considering wether or not to enter in private label production - through literature and a case study on the ice cream market in Hungary

Strategic options of firms considering private label production

Private labels are a growing phenomenon globaly. Retailers become stronger and stronger by offering their own quality private label product for customers in all segments. Certainly they do not open factories to produce these items but rather search for dedicated private label producers or pressure branded goods manufacturers to produce it for them. The article deals with the strategic choices manufacturers can have and suggest the necessary factors that need to be evaluated to decide on the winning business model- in considering wether or not to enter in private label production- through literature and a case study on the ice cream market in Hungary.

Reading before buying - exploring consumer attitudes toward informative function of packaging

Oftentimes, packaging is the first and only marketing tool consumers encounter before a purchase, therefore it is considered to be the most important communication and informative tool (Behaegel, 1991; Peters, 1994). The aim of the research is to better understand food label usage of consumers. To make the identification of behaviour patterns possible and to understand the way consumers use labels on packaging netnography has been chosen as the research method. We identified market factors in our research which result in label use. Based on our results, two large consumer segments were identified: conscious and non-conscious consumer behaviours. Reading information on packaging can be classified in two ways, according to method of use (superficial, conditional, incidental) and place (home, or point of sale).

A csomagoláson található információtartalom újragondolása sokdimenziós skálázással az élelmiszeripar példáján (Rethinking of information content on food packaging using multidimensional scaling)

A csomagolás részét képezi a jelölés – vagy más néven címke, label –, aminek elsődleges funkciója a termék tulajdonságairól való tájékoztatás, amellett, hogy a vállalat és a fogyasztó egyik legfontosabb találkozási pontja. Kiemelt szerepe van a marketing és a vállalati menedzsment eszköztárában, hiszen a fogyasztói döntéshozatal meghatározó forrása. A szerző írásában a jelölések definícióját, fajtáit és csoportosítását tárja az olvasó elé, majd ismerteti jelentőségét, fontosságát és szerepét az élelmiszer-ipari termékek segítségével. Ezután egy 630 fős megkérdezés eredményeképp a sokdimenziós skálázás (MDS) módszerével a jelölések új értelmezését mutatja be: a jelöléseket három dimenzió mentén lehet elhelyezni (előzetes tudás, érdek, megbízhatóság), valamint ezenkívül a jelölések öt homogén csoportot alkotnak (klasszikus, diétás, funkcionális, tudatos, előállítási). A téma jelentőségét az egészség és a környezet iránti növekvő érdeklődés, valamint a változó jogszabályi környezet is alátámasztja. / === / Signs, labels, claims are to inform consumers of product attributes, and are part of the packaging. Labeling is one of the most important marketing and management tool, while purchase decision is made at the point of purchase. The aim of this paper is to present the basic definitions and elements of information content on food packaging. The author developed a new approach to examine labeling using multidimensional scaling as a result of a pilot study. Labels are to distinguish through three dimensions: precognition, interest and reliability. Beyond that labels can be sorted to five homogeneous clusters based on classic, dietary, functional, conscious and production attributes. The relevancy of labeling is supported by growing interest of health and environmental issues and changing law environment.

Placing immigrant incorporation: Identity, trust, and civic engagement in Little Havana

Immigrant incorporation in the United States has been a topic of concern and debate since the founding of the nation. Scholars have studied many aspects of the phenomenon, including economic, political, social, and spatial. The most influential paradigm of immigrant incorporation in the US has been, and continues to be, assimilation, and the most important place in and scale at which incorporation occurs is the neighborhood. This dissertation captures both of these integral aspects of immigrant incorporation through its consideration of three dimensions of assimilation – identity, trust, and civic engagement – among Latin American immigrants and American-born Latinos in Little Havana, a predominantly immigrant neighborhood in Miami, Florida. Data discussed in the dissertation were gathered through surveys and interviews as part of a National Science Foundation-funded study carried out in 2005-2006. The combination of quantitative and qualitative data allows for a nuanced understanding of how immigrant incorporation is occurring locally during the first decade of the twentieth century. Findings reveal that overall Latin American immigrants and their American-born offspring appear to be becoming American with regard to their ethnic and racial identities quickly, evidenced through the salience and active employment of panethnic labels, while at the same time they are actively reshaping the identificational structure. The Latino population, however, is not monolithic and is cleaved by diversity within the group, including country of origin and socioeconomic status. These same factors impede group cohesion in terms of trust and its correlate, community. Nevertheless, the historically dominant ancestry group in Little Havana – Cubans – has been able to reach notable levels of trust and build and conserve a more solid sense of community than non-Cuban residents. With respect to civic engagement, neighborhood residents generally participate at rates lower than the overall US population and ethnic subpopulations. This is not the case for political engagement, however, where self-reported voting registration and turnout in Little Havana surpasses that of most benchmarked populations. The empirical evidence presented in this dissertation on the case of Latinos in Little Havana challenges the ways that identity, trust, and civic engagement are conceptualized and theorized, especially among immigrants to the US.

Navigating health sources on the Internet: A mixed-methods examination of online consumer reviews and expert text on psychotropic drugs

Purpose. The Internet has provided an unprecedented opportunity for psychotropic medication consumers, a traditionally silenced group in clinical trial research, to have voice by contributing to the construction of drug knowledge in an immediate, direct manner. Currently, there are no systematic appraisals of the potential of online consumer drug reviews to contribute to drug knowledge. The purpose of this research was to explore the content of drug information on various websites representing themselves as consumer- and expert-constructed, and as a practical consideration, to examine how each source may help and hinder treatment decision-making.^ Methodology. A mixed-methods research strategy utilizing a grounded theory approach was used to analyze drug information on 5 exemplar websites (3 consumer- and 2 expertconstructed) for 2 popularly prescribed psychotropic drugs (escitalopram and quetiapine). A stratified simple random sample was used to select 1,080 consumer reviews from the websites (N=7,114) through February 2009. Text was coded using QDA Miner 3.2 software by Provalis Research. A combination of frequency tables, descriptive excerpts from text, and chi-square tests for association were used throughout analyses.^ Findings. The most frequently mentioned effects by consumers taking either drug were related to psychological/behavioral symptoms and sleep. Consumers reported many of the same effects as found on expert health sites, but provided more descriptive language and situational examples. Expert labels of less serious on certain effects were not congruent with the sometimes tremendous burden described by consumers. Consumers mentioned more than double the themes mentioned in expert text, and demonstrated a diversity and range of discourses around those themes.^ Conclusions. Drug effects from each source were complete relative to the information provided in the other, but each also offered distinct advantages. Expert health sites provided concise summaries of medications’ effects, while consumer reviews had the added advantage of concrete descriptions and greater context. In short, consumer reviews better prepared potential consumers for what it’s like to take psychotropic drugs. Both sources of information benefit clinicians and consumers in making informed treatment-related decisions. Social work practitioners are encouraged to thoughtfully utilize online consumer drug reviews as a legitimate additional source for assisting clients in learning about treatment options.^

User-centric music information retrieval

The rapid growth of the Internet and the advancements of the Web technologies have made it possible for users to have access to large amounts of on-line music data, including music acoustic signals, lyrics, style/mood labels, and user-assigned tags. The progress has made music listening more fun, but has raised an issue of how to organize this data, and more generally, how computer programs can assist users in their music experience. An important subject in computer-aided music listening is music retrieval, i.e., the issue of efficiently helping users in locating the music they are looking for. Traditionally, songs were organized in a hierarchical structure such as genre->artist->album->track, to facilitate the users’ navigation. However, the intentions of the users are often hard to be captured in such a simply organized structure. The users may want to listen to music of a particular mood, style or topic; and/or any songs similar to some given music samples. This motivated us to work on user-centric music retrieval system to improve users’ satisfaction with the system. The traditional music information retrieval research was mainly concerned with classification, clustering, identification, and similarity search of acoustic data of music by way of feature extraction algorithms and machine learning techniques. More recently the music information retrieval research has focused on utilizing other types of data, such as lyrics, user-access patterns, and user-defined tags, and on targeting non-genre categories for classification, such as mood labels and styles. This dissertation focused on investigating and developing effective data mining techniques for (1) organizing and annotating music data with styles, moods and user-assigned tags; (2) performing effective analysis of music data with features from diverse information sources; and (3) recommending music songs to the users utilizing both content features and user access patterns.