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Formal Concept Analysis (FCA) is a natural framework to learn from examples. Indeed, learning from examples results in sets of frequent concepts whose extent contains mostly these examples. In terms of association rules, the above learning strategy can be seen as searching the premises of rules where the consequence is set. In its most classical setting, FCA considers attributes as a non-ordered set. When attributes of the context are partially ordered to form a taxonomy, Conceptual Scaling allows the taxonomy to be taken into account by producing a context completed with all attributes deduced from the taxonomy. The drawback, however, is that concept intents contain redundant information. In this article, we propose a parameterized algorithm, to learn rules in the presence of a taxonomy. It works on a non-completed context. The taxonomy is taken into account during the computation so as to remove all redundancies from intents. Simply changing one of its operations, this parameterized algorithm can compute various kinds of concept-based rules. We present instantiations of the parameterized algorithm to learn rules as well as to compute the set of frequent concepts.

We present an application of formal concept analysis aimed at representing a meaningful structure of knowledge communities in the form of a lattice-based taxonomy. The taxonomy groups together agents (community members) who develop a set of notions. If no constraints are imposed on how it is built, a knowledge community taxonomy may become extremely complex and difficult to analyze. We consider two approaches to building a concise representation, respecting the underlying structural relationships while hiding superfluous information: a pruning strategy based on the notion of concept stability and a representational improvement based on nested line diagrams and "zooming". We illustrate the methods on two examples: a community of embryologists and a community of researchers in complex systems.

Trace elements in squid statoliths were analyzed by PIXE for the following fourteen species in five families of different habitat origin: Ommastrephidae, Ommastrephes bartrami, Dosidicus gigas, Sthenoteuthis oualaniensis; Gonatidae, Gonatopsis makko, G. borealis, Berryteuthis magister; Loliginidae, Loligo bleekeri, L. duvaucelii, L. chinensis, L. edulis and Sepioteuthis lessoniana; Sepiidae, Sepia aculeata and Sepiella inermis; Sepiolidae, Rossia pacifica, Manganese, iron, copper, zinc and strontium were detected from statoliths of all species examined. Among these trace elements, Sr is the highest in concentration. Variation of statoliths Sr concentration reflects taxonomic position and the habitat of specimens. In Ommastrephids and Gonatids, that have oceanic habitat, statoliths Sr concentration is relatively high whereas that of Loliginids and Sepiids, that have coastal habitat, is comparatively low. This fact supports our previous report on this subject. R. pacifica exceptionally shows high statoliths Sr concentration although this species inhabits in coastal water.

During the last years, the possibility to use Artificial Neural Networks in experimental High Energy Physics has been widely studied. In particular, applications to pattern recognition and pattern classification problems have been investigated. The purpose of this article is to review the status of such investigations and the techniques established.

One motivation of Grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, efficient task scheduling algorithms are essential. However, scheduling algorithms in the Grid present high diversities that need to be classified. In this paper, with the help of an abstract scheduling architecture, some key features of the task scheduling problem in the Grid are discussed, followed by a taxonomy of the scheduling algorithms. Some typical examples are given in each category to present a picture of the current research and help to find new research problems.

Single sample face recognition (SSFR) is a challenging research problem in which only one face image per person is available for training. Moreover, the face image may have different pose, expression, illumination, occlusion etc. rendering this problem more complex. Several methods have been suggested by various researchers in literature to solve SSFR. Here, we provide a comprehensive review of the methods proposed in the last decade for solving SSFR problem and introduce a novel taxonomy for the same. We divide SSFR methods broadly into five categories viz. (i) feature based, (ii) virtual sample generation based, (iii) generic database based, (iv) Hybrid and (v) other methods. We have also briefly reviewed the face databases used for evaluating single sample face recognition methods. Furthermore, the performance of the methods has been analyzed in terms of classification accuracy as given in literature. At last, we also suggest some future direction to the researchers and practitioners working in this fascinating research area.

A definition of expert systems is given, its pragmatic demands are cited and its structure is described. The methods and ways of pattern recognition are used in the subsystems DIALOGUE, ANALYTIC and HOMEOSTAT. The recognition algorithms which work on the information to be retained in the data base and knowledge base are described. The problems of recognition appearing under the construction of expert systems are noted.

In the early 2000s, the Internet meant being able to connect different communication devices, whereas the focus in the last few years is on connecting “things” to the Internet. Although there is no distinct classification for these devices and things on the Internet, the Internet of Things (IoT) ecosystem primarily consists of a complex network of devices, sensors, and things. These “things” are controlled by humans and utilize the existing cloud infrastructure. These devices provide facilities and benefits to make our lives comfortable. IoT domains include smart homes, healthcare, manufacturing, smart wearables, smart cities, smart grids, industrial IoT, connected vehicles, and smart retail. Different IoT models involve human-to-IoT, IoT-to-IoT, IoT-to-traditional systems architectures. In most scenarios, the architecture ends up connecting to the unsecured Internet. This has thrown open several critical issues leading to cybersecurity attacks on IoT devices. IoT communications, protocols or the architecture were never been conceptualized to handle the new age cybersecurity attacks. IoT devices have limited compute, storage, network, or memory. In this research, the authors present a unique IoT attack framework named IAF focusing on the impact of IoT attacks on IoT applications and service levels. The authors also proposed an all-inclusive attack taxonomy classifying various attacks on IoT ecosystems.

Knowledge management (KM) in software engineering and software process improvement (SPI) are challenging. Most existing KM and SPI frameworks are too expensive to deploy or do not take an organization's specific needs or knowledge into consideration. There is thus a need for scalable improvement approaches that leverage knowledge already residing in the organizations.

This paper presents the Practice Selection Framework (PSF), an Experience Factory approach, enabling lightweight experience capture and use by utilizing postmortem reviews. Experiences gathered concern performance and applicability of practices used in the organization, gained from concluded projects. Project managers use these as decision support for selecting practices to use in future projects, enabling explicit knowledge transfer across projects and the development organization as a whole. Process managers use the experiences to determine if there is potential for improvement of practices used in the organization. This framework was developed and subsequently validated in industry to get feedback on usability and usefulness from practitioners. The validation consisted of tailoring and testing the framework using real data from the organization and comparing it to current practices used in the organization to ensure that the approach meets industry needs. The results from the validation are encouraging and the participants' assessment of PSF and particularly the tailoring developed was positive.

In Agile Software Development (ASD) effort estimation plays an important role during release and iteration planning. The state of the art and practice on effort estimation in ASD have been recently identified. However, this knowledge has not yet been organized. The aim of this study is twofold: (1) To organize the knowledge on effort estimation in ASD and (2) to use this organized knowledge to support practice and the future research on effort estimation in ASD. We applied a taxonomy design method to organize the identified knowledge as a taxonomy of effort estimation in ASD. The proposed taxonomy offers a faceted classification scheme to characterize estimation activities of agile projects. Our agile estimation taxonomy consists of four dimensions: estimation context, estimation technique, effort predictors and effort estimate. Each dimension in turn has several facets. We applied the taxonomy to characterize estimation activities of 10 agile projects identified from the literature to assess whether all important estimation-related aspects are reported. The results showed that studies do not report complete information related to estimation. The taxonomy was also used to characterize the estimation activities of four agile teams from three different software companies. The practitioners involved in the investigation found the taxonomy useful in characterizing and documenting the estimation sessions.

Designing cooperative multi-robot systems (MRS) requires expert knowledge both in control and artificial intelligence. Formation control is an important research within the research field of MRS. Since many researchers use different ways in approaching formation control, we try to give a taxonomy in order to help researchers design formation systems in a systematical way. We can analyze formation structures in two categories: control abstraction and robot distinguishability. The control abstraction can be divided into three layers: formation shape, reference type, and robotic control. Furthermore, robots can be classified as anonymous robots or identification robots depending on whether robots are distinguishable according to their inner states. We use this taxonomy to analyze some ground-based formation systems and to state current challenges of formation control. Such information becomes the design know-how in developing a formation system, and a case study of designing a multi-team formation system is introduced to demonstrate the usefulness of the taxonomy.

Resources like ontologies are used in a number of applications, including natural language processing, information retrieval(especially from the Internet). Different methods have been proposed to build such resources. This paper proposes a new method to extract information from the Web to build a taxonomy of terms and Web resources for a given domain. Firstly, a (CHIR) method is used to identify candidat terms. Then a similarity (SIM) measure is introduced to select relevant concepts to build the ontology. Our new algorithm, called (CHIRSIM), is easy to implement and can be efficiently integrated into an information retrieval system to help improve the retrieval performance. Experimental results show that the proposed approach can effectively and efficiently construct a cancer domain ontology from unstructured text documents.

Rudimentary knots are invoked to generate a representation of the elementary particles, a model that endows the particles with visualizable structure. The model correlates with the basic tenets, taxonomy, and interactions of the Standard Model, but goes beyond it in a number of important ways, the most significant being that all particles (hadrons and leptons, fermions and bosons) and interactions share a common topology. Among other consequences of the modeling are the topological basis for isospin invariance and its connection to electric charge, the necessary identity of electron and proton charge magnitudes, and the existence of precisely three generations on the particle family tree. The salient feature of the model is that the elementary particles are viewed not as discrete, point-like objects in a vacuum but rather as sustainable, membrane-like distortions embodying curvature and torsion in and of an otherwise featureless continuum and that their manifest physical attributes correlate with the distortion. There are additional connections to the theories of fiber bundles, superstrings and instantons and, historically, to the work of Kelvin in the mid-nineteenth century and Cartan in the 1920s among others.

Apart from their generic relationship to knots and their application to particle physics [1], flattened Moebius strips (FMS) are of intrinsic interest as elements of a genus with specific rules of combination and a unique taxonomy. Here, FMS taxonomy is developed in detail from combinatorial and lexicographic points of view which include notions of degeneracy, completeness and excited states. The results are compared to the standard, spin-parameterized, abstract hierarchy derived by group-theoretic arguments as the direct product of vector spin spaces [2]. A review of the notion of excited states then leads to a new and different model of Beta decay that employs only fusion and fission. There is additional discussion of the relationship between twist and charge and an operator/tensor formulation of the fusion and fission of basic FMS units. Associating a Hopf algebra to FMS operations as a step toward a topological quantum field theory is also investigated. The notion of spinor/twistor networks is seen to emerge from a consideration of FMS configurations for higher values of twist and the introduction of a mode dual to the canonical FMS configuration. The last section discusses the connection of the MS genus to fiber bundle/gauge theory, the concept of spin, and the Dirac equation of the electron.

The taxonomy of flattened Moebius strips (FMS) is reexamined in order to systematize the basis for its development. An FMS is broadly characterized by its twist and its direction of traverse. All values of twist can be realized by combining elementary FMS configurations in a process called fusion but the result is degenerate; a multiplicity of configurations can exist with the same value of twist. The development of degeneracy is discussed in terms of several structural factors and two principles, conservation of twist and continuity of traverse. The principles implicate a corresponding pair of constructs, a process of symbolic convolution, and the inner product of symbolic vectors. Combining constructs and structural factors leads to a systematically developed taxonomy in terms of twist categories assembled from permutation groups. Taxonomical structure is also graphically revealed by the geometry of an expository edifice that validates the convolution process while displaying the products of fusion. A formulation that combines some of the algebraic precepts of Quantum Mechanics with the primitive combinatorics and degeneracies inherent to the FMS genus is developed. The potential for further investigation and application is also discussed. An appendix outlines the planar extension of the fusion concept and another summarizes a related application of convolution.

In spite of the fact that public procurement is increasingly becoming a popular technique for small business empowerment, there are various challenges facing SMEs in public procurement. Using Nigeria as a platform, this paper examines barriers that hinder SMEs access to public procurement markets. Data were collected from literature review, analysis of documents and semi-structured interviews. The results show that lack of transparency in tendering often discourages SMEs from getting involved in public procurement in Nigeria. It further highlights the need for actions to address issues facing SMEs at different stages of the procurement process. By integrating research findings into existing knowledge, a taxonomy of techniques for enhancing SMEs access to public procurement is proposed. The taxonomy reveals specific schemes and measures to promote SME participation in public procurement. This will offer guidance to governments, policy makers and procurement experts on the implementation of SME-friendly procurement practices. The study adds to the on-going debates on the significance of public procurement policy on SME development.

This research work investigates the simultaneous interaction of entrepreneurial orientation dimensions of the firm with the leader’s psychological factors for a better explanation of SMEs’ performance. The universal and contingency approach are reductionist and do not allow us to achieve the research objective. So, to study the simultaneous interaction between a great number of variables, we are mobilizing for this purpose the configurational approach and particularly, the perspective of alignment (fit) as “configuration”. Indeed, we are pursuing a quantitative methodological approach and conducting a field survey through a research questionnaire distributed to 100 industrial Tunisian SMEs.

The results of this research reveal a taxonomy of four configurations of industrial Tunisian SMEs significantly different from each other and which have different effects on performance. The configurations are named, the “Leader”, the “Creative”, the “Ambitious” and the “Conservative”.

Grand Challenges in Biodiversity Informatics.

Using Biodiversity Information Effectively.

By 2018, business analytics (BA), believed by global CIOs to be of strategic importance, had for years been their top priority. It is also a focus of academic research, as shown by a large number of papers, books, and research reports. On the other hand, the BA domain suffers from several incorrect, imprecise, and incomplete notions. New areas and concepts emerge quickly; making it difficult to ascertain their structure. BA-related taxonomies play a crucial role in analyzing, classifying, and understanding related objects. However, according to the literature on taxonomy development in information systems (IS), in most cases the process is ad hoc. BA taxonomies and frameworks are available in the literature; however, some are excessively general frameworks with a high-level conceptual focus, while others are application or domain-specific. Our paper aims to present a novel semi-automatic method for taxonomy development and maintenance in the field of BA using content analysis and text mining. The contribution of our research is threefold: (1) the taxonomy development method, (2) the draft taxonomy for BA, and (3) identifying the latest research areas and trends in BA.

Businesses today, including non-profits, recognise the need for knowledge management (KM). KM may require new strategies and goals before it can be implemented, or it can be aligned with current business strategies for quicker implementation. The framework presented here is for managers in companies and organisations to use to align their KM strategies with business strategies to improve performance involving financial growth, cost reduction and customer satisfaction. A study of three strategic types of organisations (defender, prospector, analyser) and interviews at a large corporation and a non-profit organisation suggests that the conceptual framework presented in this paper can be verified. More empirical evidence of alignment is planned, as organisations become more sophisticated users of KM.

The authors have been working for over three years on the taxonomy and conceptual framework for KM/BS Alignment (also known as KMSABS) and present a procedure for implementation in this paper. The KM/BS Alignment model involves concepts, actors, actions and processes. An important aspect of the methodology is for businesses and organisations to identify their strategic character to support appropriate interactions associated with knowledge. As shown in this paper, product and knowledge managers can affect goal alignment and interaction in an organisation if they implement change based on the suggested framework.