The opening of the Arctic routes has great economic and strategic value, but the navigation environment is highly uncertain, unknown and complex. Aiming at the ambiguity and partial reliability of ...the uncertain information in the Arctic routes, based on the Z-number theory and the grey relational theory, this study establishes a grey relational optimization decision-making model for the comprehensive benefit evaluation of the Northeast Passage (NEP). First, the discrete Z-number is used to represent the comprehensive benefit evaluation information of channel, and Jensen Shannon divergence is introduced to measure the difference information between discrete Z-numbers. Then, on the basis of the grey relational degree of discrete Z-numbers, a new multi-dimensional fuzzy grey multi-criteria decision-making model is established and subsequently optimized using the PSO algorithm. Finally, the effectiveness and superiority of the new model are verified by comprehensively evaluating the benefit of using the Arctic NEP and the traditional route under different sea ice conditions. Results show that the comprehensive benefit of the Suez Passage is still the highest, but that of the NEP has been steadily increasing. Therefore, with future improvements in Arctic sea ice conditions and infrastructures, the NEP may become the best route for ocean shipping.
Being a pair of dual concepts, the normalized distance and similarity measures are important tools for decision-making and pattern recognition under the intuitionistic fuzzy set framework. In this ...paper, we first construct some counterexamples to illustrate that two existing similarity measures do not meet the axiomatic definition of intuitionistic fuzzy similarity measures. We then show that (1) these two measures cannot effectively distinguish some intuitionistic fuzzy values (IFVs); (2) except for the endpoints, there exist infinitely many pairs of IFVs, where the maximum distance “1” can be achieved under these two distances, leading to counter-intuitive results. To overcome these drawbacks, we introduce the concept of strict intuitionistic fuzzy distance measure (SIFDisM) and strict intuitionistic fuzzy similarity measure (SIFSimM), and propose an improved intuitionistic fuzzy distance measure based on Jensen-Shannon divergence. Moreover, we prove that (1) it is a SIFDisM; (2) its dual similarity measure is a SIFSimM; (3) its induced entropy is an intuitionistic fuzzy entropy. Comparative analysis and numerical examples demonstrate that our proposed distance measure is superior to the existing ones. In particular, our proposed distance measure can better distinguish and rank intuitionistic fuzzy sets.
The use of Project Gutenberg (PG) as a text corpus has been extremely popular in statistical analysis of language for more than 25 years. However, in contrast to other major linguistic datasets of ...similar importance, no consensual full version of PG exists to date. In fact, most PG studies so far either consider only a small number of manually selected books, leading to potential biased subsets, or employ vastly different pre-processing strategies (often specified in insufficient details), raising concerns regarding the reproducibility of published results. In order to address these shortcomings, here we present the Standardized Project Gutenberg Corpus (SPGC), an open science approach to a curated version of the complete PG data containing more than 50,000 books and more than 3 × 10 9 word-tokens. Using different sources of annotated metadata, we not only provide a broad characterization of the content of PG, but also show different examples highlighting the potential of SPGC for investigating language variability across time, subjects, and authors. We publish our methodology in detail, the code to download and process the data, as well as the obtained corpus itself on three different levels of granularity (raw text, timeseries of word tokens, and counts of words). In this way, we provide a reproducible, pre-processed, full-size version of Project Gutenberg as a new scientific resource for corpus linguistics, natural language processing, and information retrieval.
The complexity of cellular networks makes them difficult to manage and prone to failures. This has led to the application of artificial intelligence mechanisms for failure management tasks. ...Nevertheless, the availability of labeled data on failure cases is limited, making unsupervised techniques the most relevant to apply. However, these techniques require network experts to analyze the results through a costly process to link them to specific cases. Intending to ease the labeling of cases, the present work proposes a framework to determine the relevant metrics for any clustering. Furthermore, the framework is evaluated using data from real cellular networks.
In this paper, we investigate two fast subgraph kernels based on a depth-based representation of graph-structure. Both methods gauge depth information through a family of K-layer expansion subgraphs ...rooted at a vertex 1. The first method commences by computing a centroid-based complexity trace for each graph, using a depth-based representation rooted at the centroid vertex that has minimum shortest path length variance to the remaining vertices 2. This subgraph kernel is computed by measuring the Jensen–Shannon divergence between centroid-based complexity entropy traces. The second method, on the other hand, computes a depth-based representation around each vertex in turn. The corresponding subgraph kernel is computed using isomorphisms tests to compare the depth-based representation rooted at each vertex in turn. For graphs with n vertices, the time complexities for the two new kernels are O(n2) and O(n3), in contrast to O(n6) for the classic Gärtner graph kernel 3. Key to achieving this efficiency is that we compute the required Shannon entropy of the random walk for our kernels with O(n2) operations. This computational strategy enables our subgraph kernels to easily scale up to graphs of reasonably large sizes and thus overcome the size limits arising in state-of-the-art graph kernels. Experiments on standard bioinformatics and computer vision graph datasets demonstrate the effectiveness and efficiency of our new subgraph kernels.
•We investigate two fast subgraph kernels based on a depth-based representation.•Our kernels gauge depth information rooted at a vertex for a graph.•The time complexities for the two kernels are O(n2) and O(n3).•We evaluate the performance of our subgraph kernels on standard graph datasets.•We demonstrate the effectiveness of the proposed subgraph kernels.
Time series forecasting becomes important due to its wide application in many fields. A variety of methods have been developed to address this problem based on different information in the time ...series. In this paper, a novel approach is proposed to accurately forecast the time series based on the visibility graph. The similarity between nodes is measured by the topological structure information based on the Jensen-Shannon divergence. The information from top-φ most similar nodes is considered to determine the final predicted value with the weighting coefficient obtained by the Gaussian membership function. Two real-world time series data sets are applied to demonstrate the applicability of our proposed model, and results show that our proposed method can achieve lower error indicators compared to other existing network-based approaches.
We develop a novel method for measuring the similarity between complete weighted graphs, which are probed by means of the discrete-time quantum walks. Directly probing complete graphs using ...discrete-time quantum walks is intractable due to the cost of simulating the quantum walk. We overcome this problem by extracting a commute time minimum spanning tree from the complete weighted graph. The spanning tree is probed by a discrete-time quantum walk which is initialized using a weighted version of the Perron-Frobenius operator. This naturally encapsulates the edge weight information for the spanning tree extracted from the original graph. For each pair of complete weighted graphs to be compared, we simulate a discrete-time quantum walk on each of the corresponding commute time minimum spanning trees and, then, compute the associated density matrices for the quantum walks. The probability of the walk visiting each edge of the spanning tree is given by the diagonal elements of the density matrices. The similarity between each pair of graphs is then computed using either: 1) the inner product or 2) the negative exponential of the Jensen-Shannon divergence between the probability distributions. We show that in both cases the resulting similarity measure is positive definite and, therefore, corresponds to a kernel on the graphs. We perform a series of experiments on publicly available graph datasets from a variety of different domains, together with time-varying financial networks extracted from data for the New York Stock Exchange. Our experiments demonstrate the effectiveness of the proposed similarity measures.
The paper introduces a novel probability descriptor for genome sequence comparison, employing a generalized form of Jensen-Shannon divergence. This divergence metric stems from a one-parameter ...family, comprising fractions up to a maximum value of half. Utilizing this metric as a distance measure, a distance matrix is computed for the new probability descriptor, shaping Phylogenetic trees via the neighbor-joining method. Initial exploration involves setting the parameter at half for various species. Assessing the impact of parameter variation, trees drawn at different parameter values (half, one-fourth, one-eighth). However, measurement scales decrease with parameter value increments, with higher similarity accuracy corresponding to lower scale values. Ultimately, the highest accuracy aligns with the maximum parameter value of half. Comparative analyses against previous methods, evaluating via Symmetric Distance (SD) values and rationalized perception, consistently favor the present approach's results. Notably, outcomes at the maximum parameter value exhibit the most accuracy, validating the method's efficacy against earlier approaches.
This paper proposes a unified scheme for video browsing and exploration. Our scheme involves two components, video key frame extraction and 3D visualization. For key frame extraction, we develop a ...generic approach in which the Jensen–Shannon divergence (JSD), Jensen–Rényi divergence (JRD) and Jensen–Tsallis divergence (JTD) are investigated for measuring the difference between neighboring video frames, segmenting a video clip into shots then possibly into sub-shots, and choosing key frames in each shot. Our novel approach is computationally inexpensive and yet effective, as shown by experimental results. As for 3D visualization, an innovative prototype, in which key frames, their related information and video contexts are displayed, is created for video exploration. Our visualization tool also contributes to easily locating, highlighting and removing the possible redundant key frames. The key frames selected by our proposed approach and their corresponding visualization interface are combined to lead to a fast grasp of video contents.
Multi-source information fusion has attracted considerable attention in the few past years and plays a great role in real applications. However, the uncertainty or conflict will make the fusion ...results unreasonable. Furthermore, the information may be collected in the form of complex number that cannot be processed by existing methods. In this article, to handle the above issues, the complex evidence theory (CET) is exploited. CET is the generalization of Dempster–Shafer evidence theory, where the mass function is modeled by complex number, called complex mass function (CMF). In order to deal with multi-source information fusion from the perspective of the complex plane, a new Complex Jensen–Shannon divergence (CJS divergence) is presented in this article. The proposed CJS divergence can effectively measure the conflict between two CMFs, and it satisfies the properties of boundedness, symmetry and nondegeneracy. In addition, for a better combination result, we have adjusted the complex Dempster’s rule of combination, which is called the reinforced complex evidence combination rule (RCECR). Then an algorithm for multi-source information fusion is proposed based on the CJS divergence and the RCECR. Some numerical examples and two applications in target identification and medical diagnosis illustrate the effectiveness of the new approach.
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