Attribute-based Approaches Research Articles

How applicable are attribute-based approaches for human-centered ranking creation?

Item rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, lack flexibility in expressing various types of user preferences, or do not support all mandatory steps in the ranking-creation workflow. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. The iterative design process resulted in different visual interfaces that enable users to formalize their preferences based on a taxonomy of attribute scoring functions. RankASco enables broad user groups to (a) select attributes of interest, (b) express preferences on attributes through interactively tailored scoring functions, and (c) analyze and refine item ranking results. We validate RankASco in a user study with 24 participants in comparison to a general purpose tool. We report on commonalities and differences with respect to usefulness and usability and ultimately present three personas that characterize common user behavior in ranking-creation. On the human factors side, we have also identified a series of interesting behavioral variables that have an influence on the task performance and may shape the design of human-centered ranking solutions in the future.

Attribute-Based Approaches for Secure Data Sharing in Industrial Contexts

The sharing of data is becoming increasingly important for the process and manufacturing industries that are using data-driven models and advanced analysis to assess production performance and make predictions, e.g., on wear and tear. In such environments, access to data needs to be accurately controlled to prevent leakage to unauthorized users while providing easy to manage policies. Data should further be shared with users outside trusted domains using encryption. Finally, means for revoking access to data are needed. This paper provides a survey on attribute-based approaches for access control to data, focusing on policy management and enforcement. We aim to identify key properties provided by attribute-based access control (ABAC) and attribute-based encryption (ABE) that can be combined and used to meet the abovementioned needs. We describe such possible combinations in the context of a proposed architecture for secure data sharing. The paper concludes by identifying knowledge gaps to provide direction to future research on attribute-based approaches for secure data sharing in industrial contexts.

3D ground-penetrating radar attribute classification: A case study from a paleokarst breccia pipe in the Billefjorden area on Spitsbergen, Svalbard

Ground-penetrating radar (GPR) is a method that can provide detailed information about the near subsurface in sedimentary and carbonate environments. The classical interpretation of GPR data (e.g., based on manual feature selection) often is labor-intensive and limited by the experience of the interpreter. Novel attribute-based classification approaches, typically used for seismic interpretation, can provide faster, more repeatable, and less biased interpretations. We have recorded a 3D GPD data set collected across a paleokarst breccia pipe in the Billefjorden area on Spitsbergen, Svalbard. After performing advanced processing, we compare the results of a classical GPR interpretation to the results of an attribute-based classification. Our attribute classification incorporates a selection of dip and textural attributes as the input for a k-means clustering approach. Similar to the results of the classical interpretation, the resulting classes differentiate between undisturbed strata and breccias or fault zones. The classes also reveal details inside the breccia pipe that are not discerned in the classical interpretation. Using nearby outcropping breccia pipes, we infer that the intrapipe GPR facies result from subtle differences, such as breccia lithology, clast size, or pore-space filling.

Attribute-based data access control in mobile cloud computing: Taxonomy and open issues

With the thriving growth of the cloud computing, the security and privacy concerns of outsourcing data have been increasing dramatically. However, because of delegating the management of data to an untrusted cloud server in data outsourcing process, the data access control has been recognized as a challenging issue in cloud storage systems. One of the preeminent technologies to control data access in cloud computing is Attribute-based Encryption (ABE) as a cryptographic primitive, which establishes the decryption ability on the basis of a user’s attributes. This paper provides a comprehensive survey on attribute-based access control schemes and compares each scheme’s functionality and characteristic. We also present a thematic taxonomy of attribute-based approaches based on significant parameters, such as access control mode, architecture, revocation mode, revocation method, revocation issue, and revocation controller. The paper reviews the state-of-the-art ABE methods and categorizes them into three main classes, such as centralized, decentralized, and hierarchal, based on their architectures. We also analyzed the different ABE techniques to ascertain the advantages and disadvantages, the significance and requirements, and identifies the research gaps. Finally, the paper presents open issues and challenges for further investigations.

Recognizing an Action Using Its Name: A Knowledge-Based Approach

Existing action recognition algorithms require a set of positive exemplars to train a classifier for each action. However, the amount of action classes is very large and the users' queries vary dramatically. It is impractical to pre-define all possible action classes beforehand. To address this issue, we propose to perform action recognition with no positive exemplars, which is often known as the zero-shot learning. Current zero-shot learning paradigms usually train a series of attribute classifiers and then recognize the target actions based on the attribute representation. To ensure the maximum coverage of ad-hoc action classes, the attribute-based approaches require large numbers of reliable and accurate attribute classifiers, which are often unavailable in the real world. In this paper, we propose an approach that merely takes an action name as the input to recognize the action of interest without any pre-trained attribute classifiers and positive exemplars. Given an action name, we first build an analogy pool according to an external ontology, and each action in the analogy pool is related to the target action at different levels. The correlation information inferred from the external ontology may be noisy. We then propose an algorithm, namely adaptive multi-model rank-preserving mapping (AMRM), to train a classifier for action recognition, which is able to evaluate the relatedness of each video in the analogy pool adaptively. As multiple mapping models are employed, our algorithm has better capability to bridge the gap between visual features and the semantic information inferred from the ontology. Extensive experiments demonstrate that our method achieves the promising performance for action recognition only using action names, while no attributes and positive exemplars are available.

What is a good project manager? An Aristotelian perspective

The purpose of this paper is to take a critical look at the question “what is a competent project manager?” and bring some fresh added-value insights. This leads us to analyze the definitions, and assessment approaches of project manager competence. Three major standards as prescribed by PMI, IPMA, and GAPPS are considered for review from an attribute-based and performance-based approach and from a deontological and consequentialist ethics perspectives. Two fundamental tensions are identified: an ethical tension between the standards and the related competence assessment frameworks and a tension between attribute and performance-based approaches. Aristotelian ethical and practical philosophy is brought in to reconcile these differences. Considering ethics of character that rises beyond the normative deontological and consequentialist perspectives is suggested. Taking the mediating role of praxis and phrónêsis between theory and practice into consideration is advocated to resolve the tension between performance and attribute-based approaches to competence assessment.

Object-Role Modeling

Object-Role Modeling (ORM) is an approach for modeling and querying information at the conceptual level, and for transforming ORM models and queries to or from other representations. Unlike attribute-based approaches such as Entity-Relationship (ER) modeling and class modeling within the Unified Modeling Language (UML), ORM is fact-oriented, where all facts and rules are modeled in terms of natural sentences easily understood and validated by nontechnical business users. ORM’s modeling procedure facilitates validation by verbalization and population with concrete examples. ORM’s graphical notation is far more expressive than that of ER diagrams or UML class diagrams, and its attribute-free nature makes it more stable and adaptable to changing business requirements. This article explains the fundamentals of ORM, illustrates some of its advantages as a data modeling approach, and outlines some recent research to extend ORM, with special attention to mappings to deductive databases.

Record Based Relational Peculiarity Oriented Mining

Peculiarity oriented mining (POM) is different from, and complementary to, existing approaches for discovering new, surprising and interesting patterns hidden in data. A main task of mining peculiarity rules is peculiarity identification. Previous methods for finding peculiar data are attribute-based approaches. This paper extends peculiarity oriented mining to relational peculiarity oriented mining (RPOM) in which peculiar data are identified on the record level. The experimental results in image sequences of tracking multiple walking people show that the proposed RPOM approach is effective.

The Variety of an Assortment: An Extension to the Attribute-Based Approach

In recent years, interest in category management has surged, and as a consequence, large retailers now systematically review their product assortments. Variety is a key property of assortments. Assortment variety can determine consumers' store choice and is only gaining in importance with today's increasing numbers of product offerings. To support retailers in managing their assortments, insight is needed into the influence of assortment composition on consumers' variety perceptions, and appropriate measures of assortment variety are required. This paper aims to extend the assortment variety model recently proposed by Hoch et al. (1999) in Marketing Science. It conceptualizes assortment variety from an attribute-based perspective and compares this with the productbased approach of Hoch, Bradlow, and Wansink (HBW). The attribute- based approach offers an alternative viewpoint for assortment variety. Attribute- and product-based approaches reflect basic conceptualizations of assortment variety that assume substantially different perception processes: a consumer comparing products one-by-one versus a consumer examining attributes across products in the assortment. While the product-based approach focuses on the dissimilarity between product pairs in an assortment, the attribute-based approach that we propose focuses on the marginal and joint distributions of the attributes. We conjecture and aim to show that an attribute-based approach suffices to predict consumers' perceptions of assortment variety. In operationalizing the attribute-based approach, two measures of assortment variety are described and compared to productbased measures. These two measures relate to the dispersion of attribute levels, e.g., if all products have the same color or different colors, and the dissociation between attributes, e.g., if product color and size are unrelated. The ability of product-based and attributed- based measures to predict consumers' perceptions of assortment variety is assessed. The product-based measures (Hamming) tap the dissimilarity of products in an assortment across attributes. The attribute-based measures tap the dispersion of attribute levels across products (Entropy) and the dissociation between product attributes (1–Lambda) in an assortment. In two studies, we examine the correlations between these measures in a well-behaved environment (study 1) and the predictive validity of the measures for perceived variety in a consumer experiment (study 2). Study 1, using synthetic data, shows that the attribute-based measures tap specific aspects of assortment variety and that the attribute-based measures are less sensitive to the size of assortments than product-based measures are. Whereas HBW focus on assortments of equal size, study 1 indicates that an extension to assortments of unequal size results in summed Hamming measures that correlate highly with assortment size. The latter is important when assortments of different size are compared. Next, we examine how well the measures capture consumers' perception of variety. Study 2, a consumer experiment, shows that the attribute-based measures account best for consumers' perceptions of variety. Attribute- based measures significantly add to the prediction of consumers' perceptions of variety, over and above the product-based measures, while the reverse is not the case. Interestingly, this study also indicates that assortment size may not be a good proxy for perceived assortment variety. The findings illustrate the value of an attribute-based conceptualization of assortment variety, since these measures (1) correlate only moderately with assortment size and (2) suffice to predict consumers' perceptions of assortment variety. In the final section we briefly discuss how attribute-based and product-based measures can be used in assortment management, and when productand attribute-based approaches may predict consumers' variety perceptions. We discuss how an attribute-based approach can identify which attribute levels and attribute combinations influence consumers' perceptions of variety most, while a productbased approach can identify influential products. Both approaches have applications in specific situations. For instance, an attributebased approach can identify influential attributes in an ordered, simultaneous presentation of products, while a product-based approach can assess the impact of sequential presentations of products better. In addition, we indicate how the random-intercept model estimated in study 2 can be further extended to capture the influence of, e.g., consumer characteristics.

The Impact of Critical Incidents on Customer Satisfaction in Business-to-Business Relationships

ABSTRACT The few existing studies of satisfaction measurement in business-to-business markets focus on attribute-based approaches and neglect the relevance of critical incidents in relationships. From preliminary empirical studies, we know that critical incidents have a significant impact on customer satisfaction. To analyze this impact, a complementary attribute- and incident-based approach is used. It is assumed that critical incidents exert both a direct impact through influencing the level of overall and attribute satisfaction, and an indirect impact of incidents leading to a changed arithmetic mode in overall satisfaction formation. The results of an empirical study in the logistics industry show several asymmetric direct effects and a tendency for nonlinear satisfaction formation if there are critical incidents.