Reviewerly/exHarmony huggingface dataset:
Learning of Shape Models from Exemplars of Biological Objects in Images

Generalized shape models of objects are necessary to match and identify an object in an image. Acquiring these kinds of models’ special methods is necessary as they allow learning the similarity pair-wise between the shapes. Their main concern is the establishment of point correspondences between two shapes and the detection of outlier. Known algorithm assume that the aligned shapes are quite similar in a way. But special problems arise if we align shapes that are very different, for example aligning concave to convex shapes. In such cases, it is indispensable to consider the order of the point-sets and to enforce legal sets of correspondences, otherwise the calculated distances are incorrect. We present our novel shape alignment algorithm which can also handle such cases. The algorithm establishes symmetric and legal one-to-one point correspondences between arbitrary shapes, represented as ordered sets of 2D-points and returns a distance measure which runs between 0 and 1.
Quantum Computing as a Particular Case of Computing with Tensors

One of the main potential applications of uncertainty in computations is quantum computing. In this paper, we show that the success of quantum computing can be explained by the fact that quantum states are, in effect, tensors.
Semantic Interoperability: Perspectives and Technology Evaluation

Interoperability between semantic technologies is a must because they need to be in communication to interchange ontologies and use them in the distributed and open environment of the SemanticWeb. However, such interoperability is not straightforward due to the high heterogeneity in such technologies. This chapter describes the problem of semantic technology interoperability from two different perspectives. First, from a theoretical perspective by presenting an overview of the different factors that affect interoperability and, second, from a practical perspective by reusing evaluation methods and applying them to six current semantic technologies in order to assess their interoperability.
Frequent Pattern Mining

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
Population Size, Building Blocks, Fitness Landscape and Genetic Algorithm Search Efficiency in Combinatorial Optimization: An Empirical Study

In this chapter we analyse empirically genetic algorithm search efficiency on several combinatorial optimisation problems in relation to building blocks and fitness landscape. The test set includes five problems of different types and difficulty levels all with an equal chromosome length of 34 bits. Four problems were quite easy for genetic algorithm search while one, a folding problem, turned out to be a very hard one due to the uncorrelated fitness landscape. The results show that genetic algorithms are efficient in combining building blocks if the fitness landscape is well correlated and if the population size is large enough. An empirical formulae for the average number of generations needed for optimization and the corresponding risk level for the test set and population sizes are also given.
One-to-one iPad Technology in the Middle School Mathematics and Science Classrooms

Computer technology (CT) for student use is a popular avenue for school districts to pursue in their goal to attain higher academic achievement. The purpose of this study is to examine the use of iPads in a one-to-one setting, where every student has his own device 24/7, to determine the effects, if any, on academic achievement in the areas of mathematics and science. This comparison study used hierarchical linear modeling (HLM) to examine three middle schools in a private school district. Two of the schools have implemented a one-to-one iPad program with their sixth through eighth grades and the third school uses computers on limited occasions in the classroom and in a computer lab setting. The questions addressed were what effect, if any, do the implementation of a one-to-one iPad program and a teacher’s perception of his use of constructivist teaching strategies have on student academic achievement in the mathematics and science middle school classrooms. The research showed that although the program helped promote the use of constructivist activities through the use of technology, the one-to-one iPad initiative had no effect on academic achievement in the middle school mathematics and science classrooms.
Social Media Literacy: Integrating Online Identity Management into Engineering and Technology Education

Abstract Social Media Literacy: Integrating Online Identity Management into Engineering and Technology EducationOne of the most dangerous myths about the current student generation (Millenials) is that theyare Digital Natives. This myth implies that students born after 1980 know everything about everyavailable online social networking tool. Although using Facebook to connect with friends may besecond nature to them, implementing successful social media strategies to craft and manageprofessional online identities requires a new digital literacy - social media literacy. We propose aplan for online identity management and argue for the integration of social media literacy intoengineering education.Millennials spend a lot of time engaged in online social networking, sharing media andcommunicating with friends, but most of their activities are of a personal nature. When thatshared content they produce online is taken out of the personal context and reviewed by potentialemployers, it often appears strikingly inappropriate and leads to negative consequences. Collegestudents may lose jobs or internships, missing out on valuable career opportunities, because ofpublicly available, but inappropriate, social media content. A Microsoft survey of HR andmanagement professionals shows that searching online for information about potentialemployees is common practice in current recruitment processes. Unfortunately, too manystudents are unaware of the importance and healthy practices associated with maintaining aprofessional online identity. We administered an anonymous survey to engineering andtechnology students, assessing their current online behaviors and awareness about thesignificance of having a professional online identity and present the results here.This paper argues for the integration into engineering education of a new type of digital literacy,social media literacy. Social media literacy implies, although is not limited to, the ability tocarefully manage one’s online presence to craft a professional online identity. We propose a planfor creating and maintaining a professional online identity comprising three guiding principles:1) the creation of professional online content; 2) the optimization of created content for socialmedia; 3) the development and maintenance of a successful professional network. The proposedplan adapts proven business and corporate marketing practices and relates them to the individual,thus providing a straightforward structure for students to follow and successfully manage theironline identities.This paper is directly relevant to engineering and technology educators, who can adopt ouronline identity management plan and translate it into education programs for their students.References:Cross-Tab (2010). Online reputation in a connected world. Microsoft-commissioned report.Online. Retrieved from http://go.microsoft.com/?linkid=9709510.
Strengthening Canonical Pattern Databases with Structural Symmetries

Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic invariant under structural symmetry, thus making it especially attractive for symmetry-based pruning search methods. Further, we prove that this heuristic is at least as informative as using symmetric lookups over the original heuristic. An experimental evaluation confirms these theoretical results.
Statistical shape model building method using surface registration and model prototype

Abstract Automatic segmentation of organs from medical images is indispensable for the computer-assisted medical applications. Statistical Shape Models (SSMs) based scheme has been developed as an accurate and robust approach for extraction of anatomical structures, in which a crucial step is the need to place the sampled points (landmarks) with well corresponding across the whole training set. On the one hand, the correspondence of landmarks is related the quality of shape model. On the other hand, in clinical application some key positions of landmarks should be specified by physicians referring to the anatomic structure. In this paper, we develop an interactive method to build SSM that the landmark distribution can be modified manually without influencing the model quality. We extend an existing remeshing method to produce a model prototype in advance and surface features driven registration to insure the universal optimization of correspondence. The key landmarks are fixed during the prototype generation. We experimented and evaluated the proposed SSM method for lung regions, the deformations of which are considerable large.
Predictive models and abstract argumentation: the case of high-complexity semantics

Abstract In this paper, we describe how predictive models can be positively exploited in abstract argumentation. In particular, we present two main sets of results. On one side, we show that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argumentation framework. On the other side, we show that predictive models predict significant aspects of the solution to the preferred extensions enumeration problem. By exploiting an extensive set of argumentation framework features—that is, values that summarize a potentially important property of a framework—the proposed approach is able to provide an accurate prediction about which algorithm would be faster on a given problem instance, as well as of the structure of the solution, where the complete knowledge of such structure would require a computationally hard problem to be solved. Improving the ability of existing argumentation-based systems to support human sense-making and decision processes is just one of the possible exploitations of such knowledge obtained in an inexpensive way.
FCompress: An Algorithm for FASTQ Sequence Data Compression

Background: Biological sequence data have increased at a rapid rate due to the advancements in sequencing technologies and reduction in the cost of sequencing data. The huge increase in these data presents significant research challenges to researchers. In addition to meaningful analysis, data storage is also a challenge, an increase in data production is outpacing the storage capacity. Data compression is used to reduce the size of data and thus reduces storage requirements as well as transmission cost over the internet. Objective: This article presents a novel compression algorithm (FCompress) for Next Generation Sequencing (NGS) data in FASTQ format. Method: The proposed algorithm uses bits manipulation and dictionary-based compression for bases compression. Headers are compressed with reference-based compression, whereas quality scores are compressed with Huffman coding. Results: The proposed algorithm is validated with experimental results on real datasets. The results are compared with both general purpose and specialized compression programs. Conclusion: The proposed algorithm produces better compression ratio in a comparable time to other algorithms.
Verifiability of Helios Mixnet

We study game-based definitions of individual and universal verifiability by Smyth, Frink and Clarkson. We prove that building voting systems from El Gamal coupled with proofs of correct key generation suffices for individual verifiability. We also prove that it suffices for an aspect of universal verifiability. Thereby eliminating the expense of individual-verifiability proofs and simplifying universal-verifiability proofs for a class of encryption-based voting systems. We use the definitions of individual and universal verifiability to analyse the mixnet variant of Helios. Our analysis reveals that universal verifiability is not satisfied by implementations using the weak Fiat-Shamir transformation. Moreover, we prove that individual and universal verifiability are satisfied when statements are included in hashes (i.e., when using the Fiat-Shamir transformation, rather than the weak Fiat-Shamir transformation).
Radon–Fourier descriptor for invariant pattern recognition

Radon transform is not only robust to noise, but also independent on the calculation of pattern centroid. In this paper, Radon–Mellin transform (RMT), which is a combination of Radon transform and Mellin transform, is proposed to extract invariant features. RMT converts any object into a closed curve. Radon–Fourier descriptor (RFD) is derived by applying Fourier descriptor to the obtained closed curve. The obtained RFD is invariant to scaling and rotation. (Generic) R-transform and some other Radon-based methods can be viewed as special cases of the proposed method. Experiments are conducted on some binary images and gray images.
Skew cyclic and skew constacyclic codes over the ring 𝔽p + u1𝔽p + ⋯ + u2m𝔽p

In this paper, we study skew cyclic and skew constacyclic codes over the ring [Formula: see text], where [Formula: see text] and [Formula: see text], for [Formula: see text] and [Formula: see text]. We have introduced some new Gray maps and determined the relation between codes using the [Formula: see text]-Gray images of skew constacyclic codes.
Replica symmetry and replica symmetry breaking for the traveling salesperson problem

We study the energy landscape of the traveling salesperson problem (TSP) using exact ground states and a novel linear programming approach to generate excited states with closely defined properties. We look at four different ensembles, notably the classic finite dimensional Euclidean TSP and the mean-field-like (1,2)-TSP, which has its origin directly in the mapping of the Hamiltonian circuit problem on the TSP. Our data supports previous conjectures that the Euclidean TSP does not show signatures of replica symmetry breaking neither in two nor in higher dimension. On the other hand the (1,2)-TSP exhibits some signature which does not exclude broken replica symmetry, making it a candidate for further studies in the future.
Pengembangan LKPD Berbasis Discovery Learning Pada Materi Fluida Dinamis

Discovery learning provides an opportunity for students to be active during the learning process.Learning process using LKPD can guide students to learn independently, so that learning becomes centered on students.This research aims to a) produce valid LKPD based discovery learning on dynamic fluid material, b) to know its attractiveness, usefulness, and easy to use, c) to know the effectiveness of the product.The research design uses the ADDIE model, with stages: analysis, design, development, implementation, and evaluation.The result of the tested the product showed that had a) validated by an expert and proper to used, b) interested (3,23); useful (3,36); and easy to use (3,30), and 3) The product was effectively tested to use in teaching learning with percentage 75,68%.
OntoGenesis: an architecture for automatic semantic enhancement of data services

Purpose This paper describes a software architecture that automatically adds semantic capabilities to data services. The proposed architecture, called OntoGenesis, is able to semantically enrich data services, so that they can dynamically provide both semantic descriptions and data representations. Design/methodology/approach The enrichment approach is designed to intercept the requests from data services. Therefore, a domain ontology is constructed and evolved in accordance with the syntactic representations provided by such services in order to define the data concepts. In addition, a property matching mechanism is proposed to exploit the potential data intersection observed in data service representations and external data sources so as to enhance the domain ontology with new equivalences triples. Finally, the enrichment approach is capable of deriving on demand a semantic description and data representations that link to the domain ontology concepts. Findings Experiments were performed using real-world datasets, such as DBpedia, GeoNames as well as open government data. The obtained results show the applicability of the proposed architecture and that it can boost the development of semantic data services. Moreover, the matching approach achieved better performance when compared with other existing approaches found in the literature. Research limitations/implications This work only considers services designed as data providers, i.e., services that provide an interface for accessing data sources. In addition, our approach assumes that both data services and external sources – used to enhance the domain ontology – have some potential of data intersection. Such assumption only requires that services and external sources share particular property values. Originality/value Unlike most of the approaches found in the literature, the architecture proposed in this paper is meant to semantically enrich data services in such way that human intervention is minimal. Furthermore, an automata-based index is also presented as a novel method that significantly improves the performance of the property matching mechanism.
Image set compression for similar images with priorities

This Letter first proposes an algorithm for compressing the sets of the similar images with priorities. The proposed algorithm includes four modules, i.e. priority assigned, depth- and priority-constrained minimum arborescence generation, disparity reduction, and residue coding. Experimental results demonstrate that the proposed algorithm can effectively compress the similar images with priorities, and achieves similar rate-distortion performance to the state-of-the-art image set compression algorithm which has no constraints of depth and priority.
Improvement of Project Activities in University by Applying the Method of Project Management and Fundamental Competencies

A method to improve a quality of both product and process of each project in university is introduced.Special ideas and tools should be required to success projects, since students are beginners for project activities.The method of project management well known as PMBOK (Project Management Body Of Knowledge) GUIDE, that is widely used in business world, is applied to improve project activities in this study as a methodology for the processes of projects.In addition, achievement abilities are also important to proceed with projects.To satisfy this requirement, an idea of "Fundamental Competencies for Working Person" is also tried to apply in this study.Using the above method and idea, useful tools to improve the project activities and systematize a style to success the projects are described.
Aerodynamic characteristic analysis and layout optimization design for compound UAVs by using hybrid Fuzzy–PSO algorithm

In order to further improve the performance of compound UAVs at high and low speed with heavy loading, a type of compound coaxial UAV with fixed wings and tail ducted-rotor thrusters was proposed to meet technology requirement. The lift and thrust aerodynamic characteristics were analyzed to get some relative characteristic and the aerodynamic coupling relationships between the fixed wings and tail propulsion system. And an optimization method based on hybrid Fuzzy–PSO (HFPSO) algorithm was proposed to obtain the optimal configuration plan that can reach the best aerodynamic performance without considering the coaxial architecture. The optimal configuration plan was finally obtained by applying the HFPSO method which also proves the method is effective and reasonable through comparison.
An Open Source Geographical Information System Tool for Province Administration

We introduce a new open source Geographical Information System tool for public administration.Our tool contains utilities for the geographical analysis of public governance that supply services to urban, suburban, and rural areas.We present an architecture that handles content heavy geographical image data.In order to achieve fast computation time and ensure security, we present a layered architecture.The tool exploits open source accelerators and interfaces.We also consider client-side development and provide an open source front-end web framework.We enabled spatial data generation and presentation utilities.The results denote that our cache usage speeds up the image creation time by 8x.Field experiences show that our Geographical Information System tool contributes to the province development strategies in a fast, reliable, and scalable form.The tool is still in use by Kütahya Province Administration.
An Ontology-based Web Crawling Approach for the Retrieval of Materials in the Educational Domain

As the web continues to be a huge source of information for various domains, the information available is rapidly increasing.Most of this information is stored in unstructured databases and therefore searching for relevant information becomes a complex task and the search for pertinent information within a specific domain is time-consuming and, in all probability, results in irrelevant information being retrieved.Crawling and downloading pages that are related to the user's enquiries alone is a tedious activity.In particular, crawlers focus on converting unstructured data and sorting this into a structured database.In this paper, among others kind of crawling, we focus on those techniques that extract the content of a web page based on the relations of ontology concepts.Ontology is a promising technique by which to access and crawl only related data within specific web pages or a domain.The methodology proposed is a Web Crawler approach based on Ontology (WCO) which defines several relevance computation strategies with increased efficiency thereby reducing the number of extracted items in addition to the crawling time.It seeks to select and search out web pages in the education domain that matches the user's requirements.In WCO, data is structured based on the hierarchical relationship, the concepts which are adapted in the ontology domain.The approach is flexible for application to crawler items for different domains by adapting user requirements in defining several relevance computation strategies with promising results.