It is well recognized that visualizing traceability links between software artifacts helps developers to recover, browse, and maintain these inter-relationships effectively and efficiently. However, it is a major chal...
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It is well recognized that visualizing traceability links between software artifacts helps developers to recover, browse, and maintain these inter-relationships effectively and efficiently. However, it is a major challenge for researchers to efficiently visualize traceability links for big softwaresystems because of scalability and visual clutter issues. In this paper we present a new approach that combines treemap and hierarchical tree visualization techniques to provide a global structure of traces and a detailed overview of each trace. These both reduce visual clutter while still being highly scalable and interactive. Our usability study shows that our approach can support comprehension, browsing, and maintenance of traceability links.
Machine learning algorithms, which have been considered as robust methods in different computational fields, assume that the training and test data are drawn from the same distribution. This assumption may be violated...
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Machine learning algorithms, which have been considered as robust methods in different computational fields, assume that the training and test data are drawn from the same distribution. This assumption may be violated in many real world applications like bank failure prediction because training and test data may come from different time periods or domains. An efficient novel algorithm known as Fuzzy Refinement (FR) is proposed in this paper to solve this problem and improve the performance. The algorithm utilizes the fuzzy system and similarity concept to modify the instances' labels in target domain which was initially predicted by shift-unaware Fuzzy Neural Network (FNN) proposed by [1]. The experiments are performed using bank failure financial data of United States to evaluate the algorithm performance. The results address a significant improvement in the predictive accuracy of FNN due to applying the proposed algorithm.
The ground states of some many-body quantum systems can serve as resource states for the one-way quantum computing model, achieving the full power of quantum computation. Such resource states are found, for example, i...
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The ground states of some many-body quantum systems can serve as resource states for the one-way quantum computing model, achieving the full power of quantum computation. Such resource states are found, for example, in spin-52 and spin-32 systems. It is, of course, desirable to have a natural resource state in a spin-12, that is, qubit system. Here, we give a negative answer to this question for frustration-free systems with two-body interactions. In fact, it is shown to be impossible for any genuinely entangled qubit state to be a nondegenerate ground state of any two-body frustration-free Hamiltonian. What is more, we also prove that every spin-12 frustration-free Hamiltonian with two-body interaction always has a ground state that is a product of single- or two-qubit states. In other words, there cannot be any interesting entanglement features in the ground state of such a qubit Hamiltonian.
Evaluating the effectiveness of security awareness and training programs is critical for minimizing organizations’ human security risk. Based on a literature review and industry interviews, we discuss current practic...
The introduction of the European General Data Protection Regulation (GDPR) has imposed obligations on organisations collecting data in the EU. This has been beneficial to citizens due to rights reinforcement achieved ...
A keyphrase extraction technique endeavors to extract quality keyphrases from a given document, which provide a high-level summary of that document. Except statistical keyphrase extraction approaches, all other approa...
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A keyphrase extraction technique endeavors to extract quality keyphrases from a given document, which provide a high-level summary of that document. Except statistical keyphrase extraction approaches, all other approaches are either domain-dependent or require a sufficient amount of training data, which are rare at present. Therefore, in this paper, a new tree-based automatic keyphrase extraction technique is proposed, which is domain-independent and employs nominal statistical knowledge; but no train data are required. The proposed technique extracts a quality keyphrase through forming a tree from a candidate keyphrase; and later, it is expanded or shrunk or remained in the same state depending on other similar candidate keyphrases. At the end, keyphrases are extracted from the resultant trees based on a value, µ (which is the Maturity Index (MI) of a node in the tree), which enables flexibility in this process. A small µ value would yield many and/or lengthy keyphrases (greedy approach); whereas, a large µ value would yield lower and/or abbreviated keyphrases (conservative approach). Thereby, a user can extract his/her desired-level of keyphrases through tuning µ value. The effectiveness of the proposed technique is evaluated on an actual corpus, and compared with Rapid Automatic Keyphrase Extraction (RAKE) technique.
Algorithmic verification of realistic systems to satisfy safety and other temporal requirements has suffered from poor scalability of the employed formal approaches. To design systems with rigorous guarantees, many ap...
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In group decision making problems, given the importance of obtaining an accepted solution by the whole group, the consensus has attained a great attention and it is virtually a major goal of these problems. Consensus,...
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In group decision making problems, given the importance of obtaining an accepted solution by the whole group, the consensus has attained a great attention and it is virtually a major goal of these problems. Consensus, as traditionally meant to be a full and unanimous agreement, is often not reachable in practice. A more realistic approach is to use softer consensus measures, which assess the consensus degree in a more flexible way, and therefore reflect the large spectrum of possible partial agreements, guiding the consensus process until widespread agreement is achieved among experts. In particular, the interpretation of the consensus based on the concept of fuzzy majority has been used in the most of the consensus models proposed in the literature, as it is more human-consistent and suitable for reflecting human perceptions of the meaning of consensus. However, there are still some open questions to be addressed. In this paper, we are going to highlight some issues in order to focus researcher's attention on new problems that arise when using consensus models based on soft consensus measures in real-world applications.
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