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We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification "worth" is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Code smell is a software quality problem caused by software design flaws. Refactoring code smells can improve software maintainability. While prior works mostly focused on Java code smells, only a few prior researches detect and refactor code smells of Python. Therefore, we intend to outline a route (i.e. sequential refactoring operation) for refactoring Python code smells, including LC, LM, LMC, LPL, LSC, LBCL, LLF, MNC, CCC and LTCE. The route could instruct developers to save effort by refactoring the smell strongly correlated with other smells in advance. As a result, more smells could be resolved by a single refactoring. First, we reveal the co-occurrence and the inter-causation between smells. Then, we evaluate the smells’ correlation. Results highlight seven groups of smells with high co-occurrence. Meanwhile, 10 groups of smells correlate with each other in a significant level of Spearman’s correlation coefficient at 0.01. Finally, we generate the refactoring route based on the association rules, we exploit an empirical verification with 10 developers involved. The results of Kendall’s Tau show that the proposed refactoring route has a high inter-agreement with the developer’s perception. In conclusion, we propose four refactoring routes to provide guidance for practitioners, i.e. {LPL $\to$ LLF}, {LPL $\to$ LBCL}, {LPL $\to$ LMC} and {LPL $\to$ LM $\to$ LC $\to$ CCC $\to$ MNC}.

We present a new keyword extraction algorithm that applies to a single document without using a corpus. Frequent terms are extracted first, then a set of co-occurrences between each term and the frequent terms, i.e., occurrences in the same sentences, is generated. Co-occurrence distribution shows importance of a term in the document as follows. If the probability distribution of co-occurrence between term a and the frequent terms is biased to a particular subset of frequent terms, then term a is likely to be a keyword. The degree of bias of a distribution is measured by the χ²-measure. Our algorithm shows comparable performance to tfidf without using a corpus.

Recently, there has been an increasing need for artificial text summarizing algorithms that attempt to automatically condense a document into a shorter form due to the rapid and exponential growth of textual data. As a result, automated text summarization is utilized by an extensive wide range of companies to help people find the most significant information. But still, there is a challenge in creating a brief and short summary of the original material that contains the key concepts. This paper intends to propose an abstractive text summarization based on LSTM with transfer learning methods. Initially, the long text-based document summarization process includes two phases. In the first phase, the input text is preprocessed for stop word removal and stemming techniques to reduce the document size. Then features like improved bag of words, TF-IDF, aspect term extraction and average word length-based features are extracted from the preprocessed text. In the second phase, the knowledge extraction has been performed. In the knowledge extraction, tokens are generated using BERT tokenization and the co-occurrence is utilized using improved co-occurrence matrix generation. The proposed method achieves the highest recall in 80% of learning which is 1.75% 1.39%, 2.58%, 1.19%, 0.30%, and 2.28% better than the other models such as Bi-GRU, RNN, LSTM, SVM-LR, CNN, LSTM-GRU and GRU respectively. Further, the knowledge extraction and the extracted features are subjected to the improved LSTM network, where the features are trained as the pre-trained model via transfer learning. This is the phase, where the final summary is produced.

The purpose of this study is to analyse the structure of social network co-occurrence and co-authorship of scientific documents of social innovation which are indexed in Scopus database. By using scientometric and network analysis techniques, the records were retrieved and integrated. It has been used a combination of software packages, including VOSviewer, Gephi, HistCite, Publish or Perish and NodeXL, for data analysis and mapping. Analysing all keywords shows that the most important keywords, based on frequency distribution, are innovation, sustainable growth and social entrepreneurship. Thematic mapping of the keywords using co-words analysis technique indicates that the topics innovation, social services and social change had top ranking in degree centrality, closeness centrality and betweenness indicators. The analysis of the co-authorship network of the field demonstrated that it is disconnected and sparse. Moreover, the total number of citations was 8,350. Mapping the knowledge structure of social innovation papers extracted from Scopus database could help to represent and visualise the thematic structure of research in the field of Social Science and Knowledge Studies and identify more specific research focuses within this field. It should be noted that in this study, the importance of concepts such as innovation, sustainable development and social entrepreneurship has been confirmed by reviewing the literature on these issues.

Texture analysis has been efficiently utilized in the area of terrain classification. In this application, features have been obtained in the 2D image domain. In this paper we suggest 3D co-occurrence texture features by extending the concept of co-occurrence feature to the 3D world. The suggested 3D features are described as a 3D co-occurrence matrix by using a co-occurrence histogram of digital elevations at two contiguous positions. The practical construction of the co-occurrence matrix limits the number of levels of digital elevation. If the digital elevation is quantized into a few levels over the whole DEM (Digital Elevation Map), distinctive features cannot be obtained. To resolve this quantization problem, we employ the local quantization technique which can preserve the variation of elevations with a small number of quantization levels. SOM (Self-Organizing Maps), FCM (Fuzzy C-mean) and GBFCM (Gradient Based Fuzzy C-mean) clustering algorithms are employed to implement the terrain classifier, since these ANN clustering algorithms are known as robust against the high dimensionality problem in the classification process. Experimental results show that the classification accuracy with the addition of 3D co-occurrence features is significantly improved over the conventional classification method only with 2D features.

Due to the availability of innovative financial products and services, financial engineering has expanded to include more practices and engineering techniques (FinTech, InsurTech, and other technologies). Financial engineering (FE) has succeeded through the stock market, financial instruments, and service developments. This study combines qualitative and quantitative approaches with bibliometric analysis to address a vacuum in financial literature. Co-citation, co-occurrence, and bibliographic coupling analysis techniques are used to make inferences about the structure of FE research in finance from January 2007 to December 2022. The study used 347 filtered research articles from the Scopus database and processed through VOS-Viewer and Biblioshiny through “R” to justify study objectives. The relevance of authors, journals, and organizations in a contemporary topic study is recognized using bibliometrics analysis. The study topic analysis revealed that modern portfolio theory acknowledges the importance of FE’s revolutionary products, such as FinTech and InsurTech, as recent advances in risk management techniques. Future research could take a multi-dimensional approach based on the current theme. New research themes emerged from the study, i.e., Financial Engineering and emerging financial markets and risk management, investment and financial crisis, FinTech, and InsurTech innovation, and Option pricing.

In this paper, we create three new statistical measures to characterize the spatial patterns and structure of firms and subsectors of industry. The new measures give a direct quantification of the tendency of subsectors to spatially agglomerate, the likelihood of cross-subsector spatial co-agglomeration, and the potential of leading and following behaviors between firms. Unlike the conventional measures which deduce possible agglomeration through deviations from the baseline distribution or distance density, our measures study agglomeration in terms of clustering and co-location behaviors to provide a clear physical picture on spatial agglomeration. With each measure quantifying one of the three different ways in which firms agglomerate spatially, their collective use provides a combined perspective on the agglomeration behavior and location choice of firms that is not available from the conventional measures. We employ this joint application of measures to show that Singapore maritime firms have a propensity to distribute themselves spatially in a manner that allows them to reap the benefits of localization externalities from the agglomeration economy.

Transcription factors (TFs) bind to the regulatory regions of genes in a cooperative manner. This article describes a method to detect pairs of transcription factor binding sites which co-occur in known regulatory regions more often than expected by mere combination of the individual binding sites. We determine frequently co-occurring TF pairs and evaluate the method using known TF interactions.

Furthermore we use co-occurrence scores to assess the regulatory potential of a sequence region by calculating a graph-based score. We show results for the score on known regulatory regions.

Intimate partner violence (IPV) is an important social and public health problem, affecting millions of women worldwide. Violence in a relationship can occur in multiple ways, including physical violence, psychological aggression, and sexual violence. In this study, utilizing data from the National Intimate Partner and Sexual Violence Survey (NISVS), we comprehensively investigate the interplay between physical, psychological, and sexual violence, in terms of their co-occurrence patterns, their relation to trauma symptoms and overall health of victims. For this purpose, we perform network analysis and develop a visualization technique that enables in-depth navigation of the three-dimensional (physical, psychological, sexual) space of violence. Our findings show that physical violence tends to significantly co-occur with psychological abuse, and violence intensifies when both are present. We also find that sexual violence tends to overlap less with other types of violence, particularly with physical violence. Milder forms of psychological abuse are prominent in the population and seem to represent a separate type of abuse (micro-aggression) in terms of its occurrence patterns. Finally, we observe that trauma symptoms and health problems tend to be reported more by survivors at the presence of intense psychological aggression. Our findings can be useful in developing treatments that target different patterns of IPV.