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• First survey of relevant research activities on Digital Calibration Certificate-DCC. • A thorough analysis of both the calibration process and the calibration certificate. • Modeling the calibration process, the calibration certificate life-cycle. • Identifying Calibration use-cases and stakeholders. • Advantages and disadvantages of various potential DCC formats. Calibration Certificates (CC) are essential for maintaining the accuracy of measurement instruments, which in turn are necessary to maintain the quality of products and services. Such certificates are mainly used in legal metrology for quality audits, accreditation and examination processes; and also in the operational metrology to adjust measurement results according to device uncertainty. Paper-based CC (PCC) or a digital copy of the paper format has been successfully used for this purpose for many decades. However, the PCC starts to fail short with the emergence of new technologies and applications that require automated creation, processing and updates of the calibration certificates. The last years have witnessed an intense effort on digital transformation in metrology and especially developing Digital Calibration Certificates (DCC). Until recently, we noticed the lack of agreement on the nature and the usage of such important new metrology artifacts as well as some repetition in the work by different researchers. To help reaching a minimum consensus on the subject, we present in this paper, a thorough analysis of the calibration certificate and the calibration process. Besides that, we aim by this work to provide an overview of the carried research on that point to help peer researchers to view the big picture and to avoid wasting their efforts in repeating same work on same ideas. Consequently, they can recognize the peer researchers and organizations working on the same topics, find available resources, identify research gaps and identify the best potential directions of their efforts as well. Finally, our paper aims also to establish a bridge between the metrological concepts and information technology concepts such that more computer science researchers can be involved in solving the problem. [ABSTRACT FROM AUTHOR]

• Eulerian trails are classical and important objects in theoretical computer science field. • Enumeration of Eulerian trails is known to be #P-complete. • Although the existence of an algorithm with linear time per output is known, the existence of a faster algorithm is open. • This paper shows optimal algorithms for Eulerian trails which run in constant time per solution on average. In this paper, we consider enumeration problems for edge-distinct and vertex-distinct Eulerian trails. Two Eulerian trails are said to be edge-distinct if the edge sequences are not identical, and they are said to be vertex-distinct if the vertex sequences are not identical. To solve these problems, we propose optimal enumeration algorithms that run in O (N + m) total time, where N is the number of solutions and m is the number of edges in an input connected graph. The proposed algorithms are based on the reverse search technique introduced by [Avis and Fukuda, DAM 1996], and the push-out amortization technique introduced by [Uno, WADS 2015]. [ABSTRACT FROM AUTHOR]

The multiple-attribute decision-making (MADM) problem is resolved through the q -rung complex diophantine neutrosophic normal set (q -rung CDNNS). An important way to express uncertain information is using q -rung orthopair fuzzy sets (q -ROFs). Yager introduced q -ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q -rung CDNN weighted averaging (q -rung CDNNWA), q -rung CDNN weighted geometric (q -rung CDNNWG), q -rung generalized CDNN weighted averaging (q -rung GCDNNWA) and q -rung generalized CDNN weighted geometric (q -rung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology. Four factors are to evaluate when determining a sensor robotics system's quality: resolution, sensitivity, error, and environment. It is possible to compare expert opinions with the criteria and determine the best alternative. Therefore, the value of q significantly impacts the model's results. To prove that the models considered are valid and useful, we will compare the current and proposed models. Thus, q has a significant impact on the results of the model. [ABSTRACT FROM AUTHOR]

This paper concerns quantum heuristics that are able to extend the domain of quantum computing defining a promising way in the large number of well-known classical algorithms. Quantum approximate heuristics take advantage of alternation between one Hamiltonian defining the problem to solve and one mixing Hamiltonian. The adiabatic theorem initially defines in quantum physic allow to compute a solution for the Schrödinger equation, but the foundation of this methods requires strong skill in physics and mathematics. Our main objectives in this paper are at first to provide an algorithm-based presentation (as close as possible of the classical computer science community in operational research practice) of the adiabatic optimization and secondly to give a comprehensive resolution of the well-known SAT problem. This presentation gives opportunities to provide a concise but explicit analysis of the adiabatic capability to define a new efficient operational research trend. Our experiments encompass numerical evaluations on both simulator and on real quantum computer provided by IBM. Numerical evaluations using the QLM library meet the Qiskit evaluations. This contribution is at the crossroad of physic and computer science in the sense it proves the capabilities of quantum concepts to define a new and promising research trends in optimization. [ABSTRACT FROM AUTHOR]

Graph theory has been since long time very helpful either in science or in real life. Since it has been introduced, it was considered like a crossroad in science. Many problems are computer science, in logistics, in physics, in electrical engineering, in automatic science were all inspired by graph theory. Moreover, sone algorithms such as the shortest path are omnipresent within our life. That is why nobody could ignore the importance of graph theory. Nowadays, the graph theory, as is, could not answer correctly to our new needs. In my point, for the current graph theory to answer much better to our new problem-solving requirements, the theory must include two more main pillars: the semantic factor and the superimposition factor. In this paper we introduce the all-new graph theory: the semantic superposed graph theory. [ABSTRACT FROM AUTHOR]

Context: Crowdsourcing, or tapping into the power of the crowd for problem solving, has gained ever-increasing attraction since it was first introduced. Crowdsourcing has been used in different disciplines, and it is becoming well-accepted in the marketplace as a new business model which utilizes Human Intelligence Tasks (HITs). Objective: While both academia and industry have extensively delved into different aspects of crowdsourcing, there seems to be no common understanding of what crowdsourcing really means and what core and optional features it has. Also, we still lack information on the kinds and disciplines of studies conducted on crowdsourcing and how they defined it in the context of their application area. This paper will clarify this ambiguity by analysing the distribution and demographics of research in crowdsourcing and extracting taxonomy of the variability and commonality in the constructs defining the concept in the literature. Method: We conduct a systematic mapping study and analyse 113 papers, selected via a formal process, and report and discuss the results. The study is combined by a content analysis process to extract a taxonomy of features describing crowdsourcing. Results: We extract and describe the taxonomy of features which characterize crowdsourcing in its four constituents; the crowd, the crowdsourcer, the crowdsourced task and the crowdsourcing platform. In addition, we report on different mappings between these features and the characteristics of the studied papers. We also analyse the distribution of the research using multiple criteria and draw conclusions. For example, our results show a constantly increasing interest in the area, especially in North America and a significant interest from industry. Also, we illustrate that although crowdsourcing is shown to be useful in a variety of disciplines, the research in the field of computer science still seems to be dominant in investigating it. Conclusions: This study allows forming a clear picture of the research in crowdsourcing and understanding the different features of crowdsourcing and their popularity, what type of research was conducted, where and how and by whom. The study enables researchers and practitioners to estimate the current status of the research in this new field. Our taxonomy of extracted features provides a reference model which could be used to configure crowdsourcing and also define it precisely and make design decisions on which of its variation to adopt. [ABSTRACT FROM AUTHOR]

This paper presents a model for semantic network representation. According to the model, the main purpose of a semantic network is to help to correctly and quickly understand the features and structure of the solutions, and then make reliable predictions about their outcomes and rewards. These objectives are achieved under constrains like limited resources (e.g., memory size and access), time limitations, and variations of solution characteristics. The paper summarizes the elements of the model and presents a set of properties that describe the effectiveness of semantic networks in creative problem solving, including design. Six examples (based on three case studies from the related literature and three experimental studies conducted by the authors) discuss the model elements, characteristics, and properties. [ABSTRACT FROM AUTHOR]

Experiences of complex search tasks are important in social interaction and in problem solving. Considering the high importance of complex search experiences, many search experience management systems (SEMSs) have been introduced. Like any other life experience, complex search experiences should maintain 3 types of global coherence: temporal, causal and thematic coherence. However, to the best of our knowledge, none of the available SEMSs were designed to support all the 3 types of global coherence. In this paper, we introduce a coherence-oriented complex search experience management method named TimeTree. By organizing queries and clicks of a complex search task as a relative chronological source-tracking tree (RCST), TimeTree manages to support all the 3 types of global coherence. We describe a user study to evaluate TimeTree in 2 typical types of complex search task. The subjective evaluation results, the expert evaluation results, and the objective evaluation results all suggest that TimeTree can help maintain temporal, causal and thematic coherence for complex search experiences. [ABSTRACT FROM AUTHOR]

As a fundamental subject of theoretical computer science, the maximum independent set (MIS) problem not only is of purely theoretical interest, but also has found wide applications in various fields. However, for a general graph determining the size of a MIS is NP-hard, and exact computation of the number of all MISs is even more difficult. It is thus of significant interest to seek special graphs for which the MIS problem can be exactly solved. In this paper, we address the MIS problem in the pseudofractal scale-free web and the Sierpiński gasket, which have the same number of vertices and edges. For both graphs, we determine exactly the independence number and the number of all possible MISs. The independence number of the pseudofractal scale-free web is as twice as the one of the Sierpiński gasket. Moreover, the pseudofractal scale-free web has a unique MIS, while the number of MISs in the Sierpiński gasket grows exponentially with the number of vertices. [ABSTRACT FROM AUTHOR]

Remediation actions are performed in scenarios in which consequences of a problem should be promptly mitigated when its cause takes too long to be addressed or is unknown. Such scenarios are recurrent in the real world, including in the context of computer science. Existing approaches that address these scenarios are application-specific. Nevertheless, the reasoning about remediation actions as well as cause identification and resolution, in order to address problems permanently, can be abstracted in such a way that they can be incorporated to autonomous software components, often referred to as agents. They can thus autonomously deal with these scenarios, which we refer to as critical cause-effect situations . In this paper, we propose a domain-independent extension to the belief-desire-intention (BDI) architecture that provides such agents with this automated reasoning. Our work provides an extensible solution to this recurrent problem-solving strategy and allows agents to flexibly deal with resource-constrained scenarios. This solution removes the need for manually implementing the coordination of actions performed by agents, using causal models to capture the knowledge required to carry out this task. Therefore, it not only allows the development of systems with remediative behaviour, but also enables the reduction of development effort by means of a reusable infrastructure that can be used in several different domains. Our approach was evaluated based on an existing solution in the network resilience domain, which showed that our extended agent can autonomously address a network challenge, with a reduction in the development effort and no impact in agent performance. [ABSTRACT FROM AUTHOR]

In 1974 Lawson and Hanson produced a seminal active set strategy to solve least-squares problems with non-negativity constraints that remains popular today. In this paper we present TNT-NN, a new active set method for solving non-negative least squares (NNLS) problems. TNT-NN uses a different strategy not only for the construction of the active set but also for the solution of the unconstrained least squares sub-problem. This results in dramatically improved performance over traditional active set NNLS solvers, including the Lawson and Hanson NNLS algorithm and the Fast NNLS (FNNLS) algorithm, allowing for computational investigations of new types of scientific and engineering problems. For the small systems tested (5000 × 5000 or smaller), it is shown that TNT-NN is up to 95 × faster than FNNLS. Recent studies in rock magnetism have revealed a need for fast NNLS algorithms to address large problems (on the order of 10 5 × 10 5 or larger). We apply the TNT-NN algorithm to a representative rock magnetism inversion problem where it is 60× faster than FNNLS. We also show that TNT-NN is capable of solving large (45000 × 45000) problems more than 150 × faster than FNNLS. These large test problems were previously considered to be unsolvable, due to the excessive execution time required by traditional methods. [ABSTRACT FROM AUTHOR]

In the last decades, one issue that has received a lot of attention in classification problems is how to obtain better classifications. This problem becomes even more complicated when the number of classes is high. In this multiclass scenario, it is assumed that the class labels are independent of each other, and thus, most techniques and methods proposed to improve the performance of the classifiers rely on it. An alternative way to address the multiclass problem is to hierarchically distribute the classes in a collection of multiclass subproblems by reducing the number of classes involved in each local subproblem. In this paper, we propose a new method for inducing a class hierarchy from the confusion matrix of a multiclass classifier. Then, we use the class hierarchy to learn a tree-like hierarchy of classifiers for solving the original multiclass problem in a similar way as the top-down hierarchical classification approach does for working with hierarchical domains. We experimentally evaluate the proposal on a collection of multiclass datasets showing that, in general, the generated hierarchies not only outperforms the original (flat) classification but also hierarchical approaches based on other alternative ways of constructing the class hierarchy. [ABSTRACT FROM AUTHOR]

Boolean satisfiability (SAT) solving is one of the most competitive research areas of theoretical computer science. The performance of state-of-the-art SAT solvers has been continuously improving in the last decades and has reached a level where SAT solvers can be employed to solve real world problems in fields such as hardware and software verification, automated planning and many others. One of the important driving forces of this progress are the yearly organized (since 2002) SAT competitions. In this paper we describe the 2015 SAT Race that featured the traditional sequential and parallel tracks (with 64 core computers) and introduced the Incremental Library Track, which is particularly interesting for developers of SAT based applications. We describe the 2015 SAT Race and provide a detailed analysis of its results. [ABSTRACT FROM AUTHOR]

• Censoring outliers in a dataset with a class of CES distribution has been considered. • Cost function is formulated by maximum likelihood setting with two different penalties. • An efficient and low complexity algorithm is proposed guaranteeing the convergence. • Proposed algorithm generalizes FMCD to be applicable to non-Gaussian distributions. • Proposed algorithms exhibit satisfactory performance in non-Gaussian environments. In this paper, we discuss the problem of censoring outliers in a dataset with a class of complex elliptically symmetric (CES) distribution, which has various engineering applications. To this end, the regularized cost function is formulated by the maximum likelihood (ML) setting. Since the ML estimation of the free outlier subset requires solving a combinatorial problem, an efficient and low complexity algorithm is proposed that guarantees the convergence to a stationary point. Two independent and dependent penalty terms to the selected subset are considered. The conventional generalized inner product (GIP) and the submodular minimization approaches are employed in the derivation of the proposed algorithm in the independent and dependent cases, respectively. The dependent case improves performance by slightly increasing complexity. As a special case for Gaussian assumption and using a non-regularized cost function, the proposed algorithm reduces to the fast minimum covariance determinant (FMCD) algorithm, which has impressive efficiency and received considerable attention in statistics and computer science. Hence, the proposed algorithm extends/generalizes the FMCD to be applicable to the non-Gaussian distributions. The simulation results highlight that the proposed algorithms exhibit satisfactory performance in CES environments. [ABSTRACT FROM AUTHOR]

Finding a closest lattice vector to a given target vector is a widely studied problem in computational mathematics and computer science. The classical enumeration algorithm was given by Kannan in 1983 together with an algorithm for the shortest vector problem. The time complexity of the closest vector enumeration algorithm based on HKZ-reduced bases was proven to within 2 O ( n ) n n 2 by Hanrot and Stehlé in 2007. Using their point-counting method, the constant hidden in “ O ( n ) ” is strictly bigger than 1.9. In this paper, we give a new counting method which we call “sequence counting” method to bound the number of loop iterations and show that the time complexity is actually upper bounded by 2 0.401 n + o ( n ) ⋅ n n 2 . [ABSTRACT FROM AUTHOR]

A detailed and extensive empirical study of empty prediction of multi-label classification is conducted in this paper and to the best of our knowledge this work is the first empirical study of this problem. Total 8 state of the art multi-label classification methods, BR, CC, CLR, HOMER, RAkEL, ECC, MLkNN, and BRkNN, are compared on 11 datasets. The empirical results clearly answer the two research questions, (1) whether empty prediction problems happen in commonly used state of the art multi-label classification methods and what their empty prediction rates (EPR) on different test sets are and (2) what multi-label classification methods are with overall highest/lowest EPRs. Specifically, it is empirically shown that every method considered all made empty predictions on different datasets. In addition, several thresholding methods which in theory can solve empty prediction are compared. The clear answers to the two research questions and the experimental findings are the main contributions of this work to multi-label classification. [ABSTRACT FROM AUTHOR]

Engineering design is a complex socio-technical activity characterized by co-evolution of problem and solution. However, the actual design theories are not well-suited to represent and model the complexity of design activity, the co-evolution and its dynamics. Therefore, there is a need to develop design activity reasoning theories and tools, which can theorize and simulate the model of co-evolution and its dynamics. Multiagent systems have the capacity to play an important role in developing and analyzing models and theories of interactivity in socio-technical societies, particularly in design. This paper first addresses a theory for design activity reasoning. Then, it will present a multiagent system, called ADEA ( Agents-based DEsign activity Analysis ), in order to model, simulate and analyze this theory. The agents of the ADEA platform formalize the necessary design roles, characterizing the design activity as well as the relationship between design parameters in the design space. ADEA’s platform shows that cognitive limitation of role agents has been overcome, considering their relationship with the design space modeled as a network of design parameter agents. [ABSTRACT FROM AUTHOR]

Currently, the research in computer science has been exponentially expanded beyond its own fields into the other research fields such as medical science, business, and social science in forms of collaborative researches. This collaborative researches stimulate a new recommending algorithm for determining a potential research collaborator under the interdisciplinary environment. Unlike other research fields, the research problems in computer science can be transformed to other known and solvable problems. In this paper, a new hybrid algorithm based on dynamic collaboration over time was proposed for recommending an appropriate collaborator. Besides considering only three basic factors concerning social proximity, friendship, and complementarity skill as employed by others’, three new additional factors related to research interest, up-to-date publication data, and seniority of researcher are involved in our analysis. A set of new measures for all six recommending factors were proposed. The experiments were conducted with real bibliographic data within six continuous years of publication and over six topics in computer science. Our results were significantly higher than the results of the other methods at 90% confidence level. [ABSTRACT FROM AUTHOR]

Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great breakthroughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively. [ABSTRACT FROM AUTHOR]

The use of agent-based simulation models in NetLogo for research is growing rapidly in a number of fields of science and en- gineering, including in computer science, for example for analyzing the performances of asynchronous search techniques based on distributed constraints. Distributed Constraint programming (DisCSP/DCOP) is a programming approach used to describe and solve large classes of problems such as searching, combinatorial and planning problems. The purpose of this paper is to present an open-source and complete solution in NetLogo that allows modeling, simulation and evaluation of the distributed constraints using single computer or clusters, simulation or real execution. Our tool allows the use of various search techniques and also the evaluation and analysis of the performance of the asynchronous search techniques in two manners: simulation mode and real exe- cution. We also explain our methodology for running the NetLogo models in a cluster computing environment (simulation mode) or on a network of computers (simulation real execution). This tool is aimed to allow the evaluation of distributed algorithms in conditions as similar as possible to the real situations. [ABSTRACT FROM AUTHOR]

This paper presents the first results to combine two theoretically sound methods (spectral projection and multigrid methods) together to attack ill-conditioned linear systems. Our preliminary results show that the proposed algorithm applied to a Krylov subspace method takes much fewer iterations for solving an ill-conditioned problem downloaded from a popular online sparse matrix collection. [ABSTRACT FROM AUTHOR]