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Many attempts have been made to generalize the concept of intuitionistic fuzzy sets (IFS) like Pythagorean (PFS), q-rung orthopair (q-OFS), and linear Diophantine (LDFS). However, these generalizations have many advantages and disadvantages. Among the disadvantages, the main concern with these sets is that they cannot capture the situation where both or at least one of the memberships and non-membership grades are equal to 1. Secondly, how to reduce the dependency between the membership and non-membership grades. Thus, any data in the form X  = {<  x 1 ; (1,0.9) > , <  x 2 ; (0.3,1) > , <  x 3 ; (1,1) >} is not handled by the IFS and other versions of IFS because 1 + 0.9 = 1.9 > 1, 0.3 + 1 = 1.3 > 1, and 1 + 1 = 2 > 1. We propose the new idea of the q-fractional fuzzy set ( q f r s ), which can handle all such situations, using the q-intercept of the straight line and letting both membership and non-membership grades approach 100% without depending on each other. The q  = 2 is the smallest value for which all the situations in the first quadrant are tackled, and the sum of membership and non-membership grades is near 1. For all other values of q  > 2, the sum of membership and non-membership grades approaches 0, i.e., the larger the value of q, i.e., the intercepts, the sum of memberships and non-membership grades approaches 0. For q  = 1, the first intercept is simply the intuitionistic fuzzy set. We provide the basic properties of the q-fractional fuzzy set using the extension principle of fuzzy sets and develop some aggregation operators. We also developed a new q-fractional fuzzy neural network and provided an example.

To achieve system-level properties of a multiagent system, the behavior of individual agents should be controlled and coordinated. One way to control agents without limiting their autonomy is to enforce norms by means of sanctions. The dynamicity and unpredictability of the agents’ interactions in uncertain environments, however, make it hard for designers to specify norms that will guarantee the achievement of the system-level objectives in every operating context. In this paper, we propose a runtime mechanism for the automated revision of norms by altering their sanctions. We use a Bayesian Network to learn, from system execution data, the relationship between the obedience/violation of the norms and the achievement of the system-level objectives. By combining the knowledge acquired at runtime with an estimation of the preferences of rational agents, we devise heuristic strategies that automatically revise the sanctions of the enforced norms. We evaluate our heuristics using a traffic simulator and we show that our mechanism is able to quickly identify optimal revisions of the initially enforced norms.

With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in non-uniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments.

Journal peer review lies at the heart of academic quality control. This article explores the journal peer review process and seeks to examine how the reviewing process might itself contribute to papers, leading them to be more highly cited and to achieve greater recognition. Our work builds on previous observations and views expressed in the literature about (a) the role of actors involved in the research and publication process that suggest that peer review is inherent in the research process and (b) on the contribution reviewers themselves might make to the content and increased citation of papers. Using data from the journal peer review process of a single journal in the Social Sciences field (Business, Management and Accounting), we examine the effects of peer review on papers submitted to that journal including the effect upon citation, a novel step in the study of the outcome of peer review. Our detailed analysis suggests, contrary to initial assumptions, that it is not the time taken to revise papers but the actual number of revisions that leads to greater recognition for papers in terms of citation impact. Our study provides evidence, albeit limited to the case of a single journal, that the peer review process may constitute a form of knowledge production and is not the simple correction of errors contained in submitted papers.

Software companies commonly develop and maintain variants of systems, with different feature combinations for different customers. Thus, they must cope with variability in space. Software companies further must cope with variability in time, when updating system variants by revising existing software features. Inevitably, variants evolve orthogonally along these two dimensions, resulting in challenges for software maintenance. Our work addresses this challenge with ECSEST (Extraction and Composition for Systems Evolving in Space and Time), an approach for locating feature revisions and composing variants with different feature revisions. We evaluated ECSEST using feature revisions and variants from six highly configurable open source systems. To assess the correctness of our approach, we compared the artifacts of input variants with the artifacts from the corresponding composed variants based on the implementation of the extracted features. The extracted traces allowed composing variants with 99-100% precision, as well as with 97-99% average recall. Regarding the composition of variants with new configurations, our approach can combine different feature revisions with 99% precision and recall on average. Additionally, our approach retrieves hints when composing new configurations, which are useful to find artifacts that may have to be added or removed for completing a product. The hints help to understand possible feature interactions or dependencies. The average time to locate feature revisions ranged from 25 to 250 seconds, whereas the average time for composing a variant was 18 seconds. Therefore, our experiments demonstrate that ECSEST is feasible and effective.

The dynamic travelling salesman problem (DTSP) is a natural extension of the standard travelling salesman problem, and it has attracted significant interest in recent years due to is practical applications. In this article, we propose an efficient solution for DTSP, based on a genetic algorithm (GA), and on the one-by-one revision of two sides (GORTS). More specifically, GORTS combines the global search ability of GA with the fast convergence feature of the method of one-by-one revision of two sides, in order to find the optimal solution in a short time. An experimental platform was designed to evaluate the performance of GORTS with TSPLIB. The experimental results show that the efficiency of GORTS compares favourably against other popular heuristic algorithms for DTSP. In particular, a prototype logistics system based on GORTS for a supermarket with an online map was designed and implemented. It was shown that this can provide optimised goods distribution routes for delivery staff, while considering real-time traffic information.

Statistical machine learning algorithms usually assume the availability of data of considerable size to train the models. However, they would fail in addressing domains where data is difficult or expensive to obtain. Transfer learning has emerged to address this problem of learning from scarce data by relying on a model learned in a source domain where data is easy to obtain to be a starting point for the target domain. On the other hand, real-world data contains objects and their relations, usually gathered from noisy environments. Finding patterns through such uncertain relational data has been the focus of the Statistical Relational Learning (SRL) area. Thus, to address domains with scarce, relational, and uncertain data, in this paper, we propose TreeBoostler, an algorithm that transfers the SRL state-of-the-art Boosted Relational Dependency Networks learned in a source domain to the target domain. TreeBoostler first finds a mapping between pairs of predicates to accommodate the additive trees into the target vocabulary. After, it employs two theory revision operators devised to handle incorrect relational regression trees aiming at improving the performance of the mapped trees. In the experiments presented in this paper, TreeBoostler has successfully transferred knowledge between several distinct domains. Moreover, it performs comparably or better than learning from scratch methods in terms of accuracy and outperforms a transfer learning approach in terms of accuracy and runtime.

The Paris Olympics and Paralympics are scheduled to take place between 26 July and 8 September 2024, whereby electric vertical take-off and landing aircraft are anticipated to take to the skies to offer a new mobility solution to spectators of the Games. This will allow paying members of the public to move between different points within the Paris region akin to an on-demand taxi service, but through the air; passenger air taxi services (PATS). These passengers, as consumers, will have certain rights and duties under European Union law. To determine the level of protection afforded to these air passengers, a full assessment of Regulation (EC) No 261/2004 is required. As the revision of the Regulation is currently on the European Commission’s agenda, it is also important to consider its revision in light of PATS, whereby new technology, emerging business practices, changing customer behaviour and societal expectations for the level of legal protection of PATS users must be considered. This article will, therefore, assess the current version of the Regulation, in light of the interpretation from the European Court, to see whether it applies to PATS and, if so, whether it is suitable or if specific amendments need to be added to the planned revised Regulation.

Participatory enterprise modeling is about gathering domain experts and involving them directly in the creation of models, aided by modeling experts. It is meant to increase commitment to and quality of models. This paper presents an exploratory study focusing on the subjective view of the domain experts. We investigated the influence of direct collaboration versus individual modeling, and the influence of model revisions by modeling experts on psychological ownership and perceived model quality. We chose process modeling as a particular form of enterprise modeling. Our results give hint that domain experts working individually with a modeling expert perceive model quality as higher than those working collaboratively whereas psychological ownership did not show any difference. Revisions caused changes in the subjects’ assessments only of model quality. Moreover, we will present qualitative results from interviews we led with the participants. They reveal interesting insight on how outcome and perception of the procedure and the method in both settings can be positively influenced. The interviews also emphasize the special role of the method experts who are sometimes even considered as co-owners of the model.

Quality control allows companies to verify whether the products conform to requirements and specifications. However, while Artificial Intelligence is increasingly used to automate the visual inspection process, a manual revision can be required when the model cannot determine whether a piece is defective or not with enough confidence. Therefore, means must be devised to optimize the manual revision of such products, to increase the speed and quality of labeling. In this paper, we perform experiments to determine whether different defect hinting techniques and data imbalance mitigation techniques can enhance the manual revision process. Furthermore, we contrast the performance of two groups of persons with different skills and education levels and their perceptions when executing the experiments. We performed the experiments on real-world data provided by Philips Consumer Lifestyle BV