Classification Of Attributes Research Articles

FR-ANet: A Face Recognition Guided Facial Attribute Classification Network

In this paper, we study the problem of facial attribute learning. In particular, we propose a Face Recognition guided facial Attribute classification Network, called FR-ANet. All the attributes share low-level features, while high-level features are specially learned for attribute groups. Further, to utilize the identity information, high-level features are merged to perform face identity recognition. The experimental results on CelebA and LFWA datasets demonstrate the promise of the FR-ANet.

Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009–2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10–20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.

Classification and Mapping of Land Cover Types and Attributes in Al- Ahsaa Oasis, Eastern Region, Saudi Arabia Using Landsat-7 Data

Information about land use/cover is important and much more needed for different aspects of sustainable development and environmental management. Remote sensing datasets has become one of the most important and convenient tool to provide such information. The present study aimed to map land cover types for sub area in Al- Ahasaa Oasis, Saudi Arabia, using a subset of Landsat-ETM+ image. Different image preprocessing techniques in addition to a well-known and widely used classification method (i.e., Maximum Likelihood classifier) were applied. Accuracy assessment was carried out with 89% agreement and accepted according to the applied method. A different land cover classes were found in the study area, which includes (Sand dunes, Water bodies, Sabakha, Bare soil, Urban, and Agricultural lands). The study also revealed that the dominant land cover class is sand dunes with area approximately ± 70%. The study strongly indicated that the area has long been affected by sand movement. Finally, the study suggested that, further researches with more advanced methods rather than traditional methods are needed in the future to support the findings of this study, with a high degree of accuracy.

The perceptions of the causes of cardiac diseases: a taxonomy

Objective: This study verifies whether the open-ended question of the B-IPQ can collect causal attributions of patients with cardiac diseases, define the more frequent causal attributions reported, classify them and describe the relation between the classification of the causes and patients’ characteristics.Design: A group of 2011 patients with cardiac diseases was recruited during the first week of cardiac rehabilitation.Primary outcome measures: Every participant filled in the B-IPQ and the HADS. The qualitative and quantitative analyses of the text using T-LAB identified the most frequent causal attributions and their co-occurrences.Results: Among the patients, 26% did not recognise any causal attribution. The likelihood that the patients did not provide an answer was increased in older patients, females, patients with lower levels of education and higher levels of depression. Smoking and stress emerged as the most important attributions, followed by genetics, metabolic syndrome, work and nutrition. Four thematic clusters were identified: ‘work and stress’, ‘metabolic syndrome and hypertension’, ‘displeasures and body care’ and ‘heredity and other related diseases’.Conclusions: This study suggests a classification of the causal attributions in patients with cardiac diseases and identifies thematic patterns and unknown attributions. The themes identified can serve as categories for future closed-ended questions.

E. Wyndham Hulme's Classification of the Attributes of Books: On an Early Model of a Core Bibliographical Entity

Modelling bibliographical entities is a prominent activity within knowledge organization today. Current models of bibliographic entities, such as Functional Requirements for Bibliographical Records (FRBR) and the Bibliographic Framework (BIBFRAME), take inspiration from data-modelling methods developed by computer scientists from the mid-1970s on. Thus, it would seem that the modelling of bibliographic entities is an activity of very recent vintage. However, it is possible to find examples of bibliographical models from earlier periods of knowledge organization. The purpose of this paper is to draw attention to one such model, outlined by the early 20th-century British classification theorist E. Wyndham Hulme in his essay on “Principles of Book Classification” (1911–1912). There, Hulme set forth a classification of various attributes by which books can conceivably be classified. These he first divided into accidental and inseparable attributes. Accidental attributes were subdivided into edition-level and copy-level attributes and inseparable attitudes, into physical and non-physical attributes. Comparison of Hulme’s classification of attributes with those of FRBR and BIBFRAME 2.0 reveals that the different classes of attributes in Hulme’s classification correspond to groups of attributes associated with different bibliographical entities in those models. These later models assume the existence of different bibliographic entities in an abstraction hierarchy among which attributes are distributed, whereas Hulme posited only a single entity— the book—, whose various aspects he clustered into different classes of attributes. Thus, Hulme’s model offers an interesting alternative to current assumptions about how to conceptualize the relationship between attributes and entities in the bibliographical universe.

Empirical research on Kano's model and customer satisfaction.

Products are now developed based on what customers desire, and thus attractive quality creation has become crucial. In studies on customer satisfaction, methods for analyzing quality attributes and enhancing customer satisfaction have been proposed to facilitate product development. Although substantial studies have performed to assess the impact of the attributes on customer satisfaction, little research has been conducted that quantitatively calculate the odds of customer satisfaction for the Kano classification, fitting a nonlinear relationship between attribute-level performance and customer satisfaction. In the present study, the odds of customer satisfaction were determined to identify the classification of quality attributes, and took customer psychology into account to suggest how decision-makers should prioritize the allocation of resources. A novel method for quantitatively assessing quality attributes was proposed to determine classification criteria and fit the nonlinear relationship between quality attributes and customer satisfaction. Subsequently, a case study was conducted on bicycle user satisfaction to verify the novel method. The concept of customer satisfaction odds was integrated with the value function from prospect theory to understand quality attributes. The results of this study can serve as a reference for product designers to create attractive quality attributes in their products and thus enhance customer satisfaction.

Class-specific attribute reducts in rough set theory

The concept of attribute reducts plays a fundamental role in rough set analysis. There are at least two possibilities to define an attribute reduct. A classification-based or global attribute reduct is a minimal subset of condition attributes that preserves the positive region of the decision classification, namely, the positive regions of all decision classes, in a decision table. A class-specific, class-dependent, or local attribute reduct is a minimal subset of condition attributes that preserves the positive region of a particular decision class. While a classification-based reduct may not work equally well for every decision class, a class-specific attribute reduct is optimally tailored to a particular decision class. However, studies in rough set theory are dominated by classification-based reducts; there is very limited investigation on class-specific reducts. An objective of this paper is to draw attention to class-specific reducts. We systematically compare the two types of reducts and investigate their relationships with respect to both individual reducts and families of all reducts. It is possible to derive a class-specific reduct from a classification-based reduct and to derive a classification-based reduct from a family of class-specific reducts. The families of all class-specific reducts provide a pair of lower and upper bounds of the family of all classification-based reducts. Based on a three-way classification of attributes into the pair-wise disjoint sets of core, marginal, and nonuseful attributes, we examine relationships between the corresponding classes of classification-based and class-specific attributes. The union of the sets of class-specific core attributes is the set of classification-based core attributes. It is only possible to obtain an upper bound for the set of classification-based marginal attributes and a lower bound for the set of classification-based nonuseful attributes from the family of the class-specific corresponding sets of attributes.

A DRAFT CLASSIFICATION OF ATTRIBUTES IN KOREAN AND POLISH LANGUAGES

Analyzing various classifications of sentence parts in Korean and Polish, it is easy to identify the existence of attributes in both of them, although the English term itself is not actually being used by Korean linguists, and thus might not be well known. Nevertheless, since the function of gwanhyeongeo (Kor. 관형어) in Korean, and attributes in Polish is similar, for the sake of transparency, this particular term will be used.The aim of this article is to propose a comprehensive classification of attributes in both target languages based on formal, syntactic and semantic parameters. In order to do it in the most exhaustive way, firstly different approaches concerning the definition of attributes, as well as the methods of their classification, proposed by various linguists in both languages will be examined. The author hopes to find out to which extent the typological differences between Korean and Polish are reflected in the attributes’ properties, according to which they can be classified.

An Investigation into Restaurant Attributes: A Basis for a Typology

ABSTRACTThis article has the aim of presenting the basis for a new, clearer classification of restaurant attributes. The research followed an interpretive approach with a systematic review of the literature, compared and contrasted with the findings of six focus group interviews. A new model was devised with seven categories of restaurant attributes. This article presents a model that needs to be tested. Also, follow-up articles with more detail about the attributes under each category will be presented. This article organizes the disparate literature on restaurant attributes and looks into the relationship between attributes, particularly its influence on perceived consumer value.

How hedge woody species diversity and habitat change is a function of land use history and recent management in a European agricultural landscape

European hedged agricultural landscapes provide a range of ecosystem services and are an important component of cultural and biodiversity heritage. This paper investigates the extent of hedges, their woody species diversity (including the influence of historical versus recent hedge origin) and dynamics of change. The rationale is to contribute to an ecological basis for hedge habitat management. Sample sites were allocated based on a multivariate classification of landscape attributes. All field boundaries present in each site were mapped and surveyed in 1998 and 2007. To assess diversity, a list of all woody species was recorded in one standard 30 m linear plot within each hedge. There was a net decrease in hedge habitat extent, mainly as a result of removal, and changes between hedges and other field boundary types due to the development and loss of shrub growth-form. Agricultural intensification, increased rural building, and variation in hedge management practices were the main drivers of change. Hedges surveyed at baseline, which were lost at resurvey, were more species rich than new hedges gained. Hedges coinciding with historical land unit boundaries of likely Early Medieval origin were found to be more species rich. The most frequent woody species in hedges were native, including a high proportion with Fraxinus excelsior, a species under threat from current and emerging plant pests and pathogens. Introduced species were present in circa 30% of hedges. We conclude that since hedge habitat distribution and woody species diversity is a function of ecology and anthropogenic factors, the management of hedges in enclosed agricultural landscapes requires an integrated approach.

Seismic facies analysis through musical attributes

ABSTRACTSeismic facies analysis is a well‐established technique in the workflow followed by seismic interpreters. Typically, huge volumes of seismic data are scanned to derive maps of interesting features and find particular patterns, correlating them with the subsurface lithology and the lateral changes in the reservoir. In this paper, we show how seismic facies analysis can be accomplished in an effective and complementary way to the usual one. Our idea is to translate the seismic data in the musical domain through a process called sonification, mainly based on a very accurate time–frequency analysis of the original seismic signals. From these sonified seismic data, we extract several original musical attributes for seismic facies analysis, and we show that they can capture and explain underlying stratigraphic and structural features. Moreover, we introduce a complete workflow for seismic facies analysis starting exclusively from musical attributes, based on state‐of‐the‐art machine learning computational techniques applied to the classification of the aforementioned musical attributes. We apply this workflow to two case studies: a sub‐salt two‐dimensional seismic section and a three‐dimensional seismic cube. Seismic facies analysis through musical attributes proves to be very useful in enhancing the interpretation of complicated structural features and in anticipating the presence of hydrocarbon‐bearing layers.

What makes continued mobile gaming enjoyable?

Earlier mobile game studies have largely focused on the pre-adoption phase while ignoring the post-adoption behaviors. Additionally, while the intrinsic factor of enjoyment is often considered important in affecting game play, little research has attempted to understand its drivers. To fill these gaps, we examine the role of enjoyment as a motive for continual mobile game use, and the key antecedents of enjoyment. Applying our two-dimensional classification of artifact-related attributes, we adopt game design attributes of challenge, variety, novelty, and design aesthetics; and playability attributes of ease of use, and interactivity, and measure their impact on enjoyment, and its effect on continuance intention. The model is tested against 207 actual users of various mobile games. Structural equation modeling (SEM) is employed for data analysis. The findings put ease of use, novelty, design aesthetic, and challenge under the spotlight. We conclude that continual mobile game use is strongly driven by enjoyment, which in turn is primarily driven by the system's capacity of regeneration and visually attractive and easy-to-use interface. The implications are discussed in the paper.

A geostatistical approach to measure the consistency between geological logs and quantitative covariates

Core logging is the geological study, recording and classification of petrophysical attributes of drill hole samples, such as lithology, alteration or mineralogical assemblage. The geological logging is qualitative and subject to errors because of its visual nature and other factors inherent to logging, such as low drill hole recoveries, difficulties in estimating the volumetric contents of minerals, or different logging criteria among geologists. To date, different tools for quality control and validation of geological logging have been elaborated, based on geological knowledge, statistics, geostatistics, image analysis, neural network and data mining. This paper presents an alternative approach based on geostatistical modeling for identifying and reclassifying potentially mislogged samples when quantitative covariates from geochemical analyses or metallurgical tests are available. The principle of this approach is to: (i) define geological domains for each quantitative variable by an adequate grouping of the log classes; (ii) transform the quantitative variables into normal scores, accounting for the previously defined domains, (iii) model the spatial correlation structure of the normal scores, (iv) perform leave-one-out cross validation and obtain predictions of the normal variables and the associated variance-covariance matrices of prediction errors; (v) calculate a measure of consistency for each sample and each possible logged class under a multivariate normal assumption; and (vi) compare these measures of consistency with the actual logged classes to detect suspicious logs. The methodology is demonstrated in a case study from an iron ore deposit, with data of rock type logged by geologists and seven quantitative variables (grades of elements of interest, loss on ignition and granulometry).

Regression Facial Attribute Classification via simultaneous dictionary learning

Recently, many researchers have attempted to classify Facial Attributes (FAs) by representing characteristics of FAs such as attractiveness, age, smiling and so on. In this context, recent studies have demonstrated that visual FAs are a strong background for many applications such as face verification, face search and so on. However, Facial Attribute Classification (FAC) in a wide range of attributes based on the regression representation -predicting of FAs as real-valued labels- is still a significant challenge in computer vision and psychology. In this paper, a regression model formulation is proposed for FAC in a wide range of FAs (e.g. 73 FAs). The proposed method accommodates real-valued scores to the probability of what percentage of the given FAs is present in the input image. To this end, two simultaneous dictionary learning methods are proposed to learn the regression and identity feature dictionaries simultaneously. Accordingly, a multi-level feature extraction is proposed for FAC. Then, four regression classification methods are proposed using a regression model formulated based on dictionary learning, SRC and CRC. Convincing results are acquired to handle a wide range of FAs and represent the probability of FAs on the PubFig, LFW, Groups and 10k US Adult Faces databases compared to several state-of-the-art methods.

Rapid Identification and Taxonomical Classification of Structural Seismic Attributes in a Regionwide Commercial Building Stock

AbstractAs territorial authorities, government agencies, and other large-asset owners were responding to regulatory and market forces in the wake of the 2010–2011 Canterbury, New Zealand, earthquakes by assessing and planning retrofits for buildings determined to be particularly vulnerable to earthquakes, an opportunity existed to identify and taxonomically classify structural seismic attributes in the largest regional commercial building stock in New Zealand. To that end, the Auckland Council proactively sought to assess thousands of commercial and industrial buildings across the Auckland region. As part of the Auckland Council program, a targeted sample out of a total of approximately 19,885 commercial buildings in the Auckland region was assessed with varying amounts of typological data recorded including lateral load resisting system type, number of stories, and time period of construction. Engineers, risk modelers, building regulators, and civil defense officials in other cities around the world can ...

Relative Efficiency of the Nonparametric Approach on Attribute Classification for Small Sample Cases

This simulation study aims to examine the relative efficiency of the nonparametric approach in comparison to the CDMs under various conditions to provide practitioners with useful and practical guideline that may be helpful for method selection in diagnostic assessment. The study shows that the nonparametric approach performs as good, if not better, as the CDM based empirical Bayes estimation method for attribute classification purposes when the sample size is relatively small. Impact of Q-matrix complexity for the nonparametric and CDM classifications is also examined. Results indicate that measuring many attributes simultaneously by an item may result in undesirable outcomes.

Model Similarity, Model Selection, and Attribute Classification.

Selecting the most appropriate cognitive diagnosis model (CDM) for an item is a challenging process. Although general CDMs provide better model-data fit, specific CDMs have more straightforward interpretations, are more stable, and can provide more accurate classifications when used correctly. Recently, the Wald test has been proposed to determine at the item level whether a general CDM can be replaced by specific CDMs without a significant loss in model-data fit. The current study examines the practical consequence of the test by evaluating whether the attribute-vector classification based on CDMs selected by the Wald test is better than that based on general CDMs. Although the Wald test can detect the true underlying model for certain CDMs, it is yet unclear how effective it is at distinguishing among the wider range of CDMs found in the literature. This study investigates the relative similarity of the various CDMs through the use of the newly developed dissimiliarity index, and explores the implications for the Wald test. Simulations show that the Wald test cannot distinguish among additive models due to their inherent similarity, but this does not impede the ability of the test to provide higher correct classification rates than general CDMs, particularly when the sample size is small and items are of low quality. An empirical example is included to demonstrate the viability of the procedure.

消費電力量と世帯属性の分類に基づく世帯群の合計消費電力量推定に関する一検討

消費電力量と世帯属性の分類に基づく世帯群の合計消費電力量推定に関する一検討

A Refined Quality Attribute Classification Model for New Product and Service Strategic Design

The Kano requirement model is based on the need to provide superior quality to clients. Thus, it classifies different product attributes based on their contribution to perceived quality. Nevertheless, in the case of new products or services their value is not perceived, but demanded by consumers. Therefore, literature presents the HWWP (Health, Weapon, Wealth, and Prospect) four quadrant model as an alternative for first time right design. The authors analyse the HWWP's value-added and customer importance principles and provide a refined model with a natural distribution of information and a strategic orientation. This refined perspective of customer needs evaluation is based on mathematical modelling and a thorough delimitation of quality attributes for managerial thinking by using elasticity curves from the economic domain in a previous case study. The present article aims to provide logical classification criteria for customer requirements as a first step towards strategic decision support in new product or service design. The refinement of the HWWP model deepens its position in the theory of attractive quality and transforms it in an innovative strategic tool for new product and service design.

Classification of forest land attributes using multi-source remotely sensed data

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.