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The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions. [ABSTRACT FROM AUTHOR]

Sentiment Analysis in text, especially text containing scientific citations, is an emerging research field with important applications in the research community. This review explores the field of sentiment analysis by focusing on the interpretation of citations, presenting a detailed description of techniques and methods ranging from lexicon-based approaches to Machine and Deep Learning models. The importance of understanding both the emotion and the intention behind citations is emphasized, reflecting their critical role in scientific communication. In addition, this study presents the challenges faced by researchers (such as complex scientific terminology, multilingualism, and the abstract nature of scientific discourse), highlighting the need for specialized language processing techniques. Finally, future research directions include improving the quality of datasets as well as exploring architectures and models to improve the accuracy of sentiment detection. [ABSTRACT FROM AUTHOR]

Reducing the size of the training set, which involves replacing it with a condensed set, is a widely adopted practice to enhance the efficiency of instance-based classifiers while trying to maintain high classification accuracy. This objective can be achieved through the use of data reduction techniques, also known as prototype selection or generation algorithms. Although there are numerous algorithms available in the literature that effectively address single-label classification problems, most of them are not applicable to multilabel data, where an instance can belong to multiple classes. Well-known transformation methods cannot be combined with a data reduction technique due to different reasons. The Condensed Nearest Neighbor rule is a popular parameter-free single-label prototype selection algorithm. The IB2 algorithm is the one-pass variation of the Condensed Nearest Neighbor rule. This paper proposes variations of these algorithms for multilabel data. Through an experimental study conducted on nine distinct datasets as well as statistical tests, we demonstrate that the eight proposed approaches (four for each algorithm) offer significant reduction rates without compromising the classification accuracy. [ABSTRACT FROM AUTHOR]

Partition-based clustering is widely applied over diverse domains. Researchers and practitioners from various scientific disciplines engage with partition-based algorithms relying on specialized software or programming libraries. Addressing the need to bridge the knowledge gap associated with these tools, this paper introduces kClusterHub, an AutoML-driven web tool that simplifies the execution of partition-based clustering over numerical, categorical and mixed data types, while facilitating the identification of the optimal number of clusters, using the elbow method. Through automatic feature analysis, kClusterHub selects the most appropriate algorithm from the trio of k-means, k-modes, and k-prototypes. By empowering users to seamlessly upload datasets and select features, kClusterHub selects the algorithm, provides the elbow graph, recommends the optimal number of clusters, executes clustering, and presents the cluster assignment, through tabular representations and exploratory plots. Therefore, kClusterHub reduces the need for specialized software and programming skills, making clustering more accessible to non-experts. For further enhancing its utility, kClusterHub integrates a REST API to support the programmatic execution of cluster analysis. The paper concludes with an evaluation of kClusterHub's usability via the System Usability Scale and CPU performance experiments. The results emerge that kClusterHub is a streamlined, efficient and user-friendly AutoML-inspired tool for cluster analysis. [ABSTRACT FROM AUTHOR]

Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered "close" to the user for whom the prediction is being generated have given to the same product or service. In general, "close" users for some user u correspond to users that have rated items similarly to u and these users are termed as "near neighbors". As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset's density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method's efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets. [ABSTRACT FROM AUTHOR]

The Reduction by Space Partitioning (RSP3) algorithm is a well-known data reduction technique. It summarizes the training data and generates representative prototypes. Its goal is to reduce the computational cost of an instance-based classifier without penalty in accuracy. The algorithm keeps on dividing the initial training data into subsets until all of them become homogeneous, i.e., they contain instances of the same class. To divide a non-homogeneous subset, the algorithm computes its two furthest instances and assigns all instances to their closest furthest instance. This is a very expensive computational task, since all distances among the instances of a non-homogeneous subset must be calculated. Moreover, noise in the training data leads to a large number of small homogeneous subsets, many of which have only one instance. These instances are probably noise, but the algorithm mistakenly generates prototypes for these subsets. This paper proposes simple and fast variations of RSP3 that avoid the computationally costly partitioning tasks and remove the noisy training instances. The experimental study conducted on sixteen datasets and the corresponding statistical tests show that the proposed variations of the algorithm are much faster and achieve higher reduction rates than the conventional RSP3 without negatively affecting the accuracy. [ABSTRACT FROM AUTHOR]

Featured Application: Early diagnosis and warning mechanisms are essential in every health condition. The research described in this paper can provide the means for the development of medical assistance applications. The correlation between the kind of cesarean section and post-traumatic stress disorder (PTSD) in Greek women after a traumatic birth experience has been recognized in previous studies along with other risk factors, such as perinatal conditions and traumatic life events. Data from early studies have suggested some possible links between some vulnerable factors and the potential development of postpartum PTSD. The classification of each case in three possible states (PTSD, profile PTSD, and free of symptoms) is typically performed using the guidelines and the metrics of the version V of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) which requires the completion of several questionnaires during the postpartum period. The motivation in the present work is the need for a model that can detect possible PTSD cases using a minimum amount of information and produce an early diagnosis. The early PTSD diagnosis is critical since it allows the medical personnel to take the proper measures as soon as possible. Our sample consists of 469 women who underwent emergent or elective cesarean delivery in a university hospital in Greece. The methodology which is followed is the application of random decision forests (RDF) to detect the most suitable and easily accessible information which is then used by an artificial neural network (ANN) for the classification. As is demonstrated from the results, the derived decision model can reach high levels of accuracy even when only partial and quickly available information is provided. [ABSTRACT FROM AUTHOR]

Nowadays, large volumes of training data are available from various data sources and streaming environments. Instance-based classifiers perform adequately when they use only a small subset of such datasets. Larger data volumes introduce high computational cost that prohibits the timely execution of the classification process. Conventional prototype selection and generation algorithms are also inappropriate for data streams and large datasets. In the past, we proposed prototype generation algorithms that maintain a dynamic set of prototypes and are appropriate for such types of data. Dynamic because existing prototypes may be updated, or new prototypes may be appended to the set of prototypes in the course of processing. Still, repetitive generation of new prototypes may result to forming unpredictably large sets of prototypes. In this paper, we propose a new variation of our algorithm that maintains the prototypes in a convenient and manageable way. This is achieved by removing the weakest prototype when a new prototype is generated. The new algorithm has been tested on several datasets. The experimental results reveal that it is as accurate as its predecessor, yet it is more efficient and noise tolerant. [ABSTRACT FROM AUTHOR]

Although the $$k$$ -NN classifier is a popular classification method, it suffers from the high computational cost and storage requirements it involves. This paper proposes two effective cluster-based data reduction algorithms for efficient $$k$$ -NN classification. Both have low preprocessing cost and can achieve high data reduction rates while maintaining $$k$$ -NN classification accuracy at high levels. The first proposed algorithm is called reduction through homogeneous clusters (RHC) and is based on a fast preprocessing clustering procedure that creates homogeneous clusters. The centroids of these clusters constitute the reduced training set. The second proposed algorithm is a dynamic version of RHC that retains all its properties and, in addition, it can manage datasets that cannot fit in main memory and is appropriate for dynamic environments where new training data are gradually available. Experimental results, based on fourteen datasets, illustrate that both algorithms are faster and achieve higher reduction rates than four known methods, while maintaining high classification accuracy. [ABSTRACT FROM AUTHOR]

A popular and easy to implement classifier is the k-Nearest Neighbour (k-NN). However, sequentially searching for nearest neighbours in large datasets leads to inefficient classification because of the high computational cost involved. This article presents an adaptive hybrid and cluster-based method for speeding up the k-NN classifier. The proposed method reduces the computational cost as much as possible while maintaining classification accuracy at high levels. The method is based on the well-known k-means clustering algorithm and consists of two main parts: (i) a pre-processing algorithm that builds a two-level, cluster-based data structure, and (ii) a hybrid classifier that classifies new items by accessing either the first or the second level of the data structure. The proposed approach was tested on seven real life datasets and the experiential measurements were statistically validated by the Wilcoxon signed ranks test. The results show that the proposed classification method can be used either to achieve high accuracy with slightly higher cost or to reduce the cost at a minimum level with slightly lower accuracy. [ABSTRACT FROM AUTHOR]

The $$k$$ -NN classifier is a widely used classification algorithm. However, exhaustively searching the whole dataset for the nearest neighbors is prohibitive for large datasets because of the high computational cost involved. The paper proposes an efficient model for fast and accurate nearest neighbor classification. The model consists of a non-parametric cluster-based preprocessing algorithm that constructs a two-level speed-up data structure and algorithms that access this structure to perform the classification. Furthermore, the paper demonstrates how the proposed model can improve the performance on reduced sets built by various data reduction techniques. The proposed classification model was evaluated using eight real-life datasets and compared to known speed-up methods. The experimental results show that it is a fast and accurate classifier, and, in addition, it involves low pre-processing computational cost. [ABSTRACT FROM AUTHOR]

Data reduction techniques improve the efficiency of κ-Nearest Neigh-bour classification on large datasets since they accelerate the classification process and reduce storage requirements for the training data. IB2 is an effective prototype selection data reduction technique. It selects some items from the initial training dataset and uses them as representatives (prototypes). Contrary to many other techniques, IB2 is a very fast, one-pass method that builds its reduced (condensing) set in an incremental manner. New training data can update the condensing set without the need of the "old" removed items. This paper proposes a variation of IB2, that generates new prototypes instead of selecting them. The variation is called AIB2 and attempts to improve the efficiency of IB2 by positioning the prototypes in the center of the data areas they represent. The empirical experimental study conducted in the present worκ as well as the Wilcoxon signed ranκs test show that AIB2 performs better than IB2. [ABSTRACT FROM AUTHOR]