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Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%. [ABSTRACT FROM AUTHOR]

The new way of thinking science from Newtonian determinism to nonlinear unpredictability and the dawn of advanced computer science and technology can be summarized in the words of the theoretical physicist Michel Baranger, who, in 2000, said in a conference: "Twenty-first-century theoretical physics is coming out of the chaos revolution; it will be about complexity and its principal tool will be the computer.". This can be extended to natural sciences in general. Modelling and predicting ionosphere variables have been considered since many decades as a paramount objective of research by scientists and engineers. The new approach to natural sciences influenced also ionosphere research. Ionosphere as a part of the solar–terrestrial environment is recognized to be a complex chaotic system, and its study under this new way of thinking should become an important area of ionospheric research. After discussing the new context, this paper will try to review recent advances in the exploration of ionosphere parameter time series in terms of chaos theory and the use of machine-learning algorithms. [ABSTRACT FROM AUTHOR]

Automated generation of image captions is a demanding AI crisis as it necessitates the exploitation of numerous methods from diverse computer science fields. Deep learning (DL) approaches have revealed marvelous results in a lot of diverse appliances. On the other hand, data augmentation in DL that imitates the quantity and the variety of training data without the need of gathering additional data is a hopeful area in machine learning (ML). Producing textual descriptions for a specified image is a demanding task using the computer. This survey makes a critical analysis of about 65 papers regarding image captioning. More particularly, varied performance measures that are contributed in diverse articles are analyzed. In addition, a comprehensive study is made regarding the maximal performances and varied features deployed in each work. Moreover, chronological analysis and dataset analysis are done and finally, the survey extends with the determination of varied research challenges, which might be productive for the analysts to endorse enhanced upcoming works on image captioning. [ABSTRACT FROM AUTHOR]

Entropy and extropy are emerging concepts in machine learning and computer science. Within the past decade, statisticians have created estimators for these measures. However, associated variability metrics, specifically varentropy and varextropy, have received comparably less attention. This paper presents a novel methodology for computing varentropy and varextropy, drawing inspiration from Bayesian nonparametric methods. We implement this approach using a computational algorithm in R and demonstrate its effectiveness across various examples. Furthermore, these new estimators are applied to test uniformity in data. [ABSTRACT FROM AUTHOR]

Regularization regression learning scheme finds its powerful applications in many areas, such as Computer science, manufacturing engineering, economic decision making, etc. In this paper, the indefinite kernel based coefficient dependent regularization algorithm for block-wise streaming data is proposed, and learning performance of this algorithm is studied by bounding the learning error. Our learning scheme works in an online and weighted average manner. The total error is decomposed into weighted average of local variance error and weighted average of local bias error. By the kernel decompose and integral operator method, satisfied error bound and learning rates are derived. Our error analysis shows that mild growth of the sizes of data block and the underregularization strategy can guarantee the convergence of the learning scheme. [ABSTRACT FROM AUTHOR]

With the application of machine learning rapidly gaining popularity in computer science and other fields, neural network techniques have successfully simulated the performance of in-service pavements as they are efficient in predicting and solving nonlinear relationships and in dealing with uncertain large-area pavement problems. In this paper, we address the problem of the optimal timing of preventive maintenance of asphalt pavements to accurately predict the condition index (pavement condition index, PCI) of highway asphalt pavements and develop a highly accurate, long-period, multifactor prediction model with the suitability of preventive maintenance at its core. The prediction model is called differential evolution particle swarm optimization back propagation (DEPSO-BP) neural network, and the input dimension of the prediction model is determined by gray correlation analysis (GCA), and DEPSO is used to improve the search efficiency of BP neural network and the asphalt pavement usage performance with parameter continuity prediction model. Finally, the Qinglan Highway (G22) PCI of Gansu Province, China, is selected for example validation, and the prediction results are compared with those of the four models. The results show that the multifactor prediction model based on DEPSO-BP neural network has good generalization ability. This model is important for improving the economic efficiency of road maintenance, and can be used in the long-cycle process to provide model reference and scientific basis for the subsequent road maintenance budget application and decision-making scheme. [ABSTRACT FROM AUTHOR]

Statistical information theory is a method for quantifying the amount of stochastic uncertainty in a system. This theory originated in communication theory. The application of information theoretic approaches has been extended to different fields. This paper aims to perform a bibliometric analysis of information theoretic publications listed on the Scopus database. The data of 3701 documents were extracted from the Scopus database. The software used for analysis includes Harzing's Publish or Perish and VOSviewer. Results including publication growth, subject areas, geographical contributions, country co-authorship, most cited publications, keyword co-occurrence analysis, and citation metrics are presented in this paper. Publication growth has been steady since 2003. The United States has the highest number of publications and received more than half of the total citations from all 3701 publications. Most of the publications are in computer science, engineering, and mathematics. The United States, the United Kingdom, and China have the highest collaboration across countries. The focus on information theoretic is slowly shifting from mathematical models to technology-driven applications such as machine learning and robotics. This study highlights the trends and developments of information theoretic publications, which helps researchers to understand the state of the art of information theoretic approaches for future contributions in this research domain. [ABSTRACT FROM AUTHOR]

Many network/graph structures are continuously monitored by various sensors that are placed at a subset of nodes and edges. The multidimensional data collected from these sensors over time create large-scale graph data in which the data points are highly dependent. Monitoring large-scale attributed networks with thousands of nodes and heterogeneous sensor data to detect anomalies and unusual events is a complex and computationally expensive process. This paper introduces a new generic approach inspired by state-space models for network anomaly detection that can utilize the information from the network topology, the node attributes (sensor data), and the anomaly propagation sets in an integrated manner to analyze the entire network all at once. This article presents how heterogeneous network sensor data can be analyzed to locate the sources of anomalies as well as the anomalous regions in a network, which can be impacted by one or multiple anomalies at any time instance. Experimental results demonstrate the superior performance of our proposed framework in detecting anomalies in attributed graphs. Summary of Contribution: With the increasing availability of large-scale network sensors and rapid advances in artificial intelligence methods, fundamentally new analytical tools are needed that can integrate data collected from sensors across the networks for decision making while taking into account the stochastic and topological dependencies between nodes, sensors, and anomalies. This paper develops a framework to intelligently and efficiently analyze complex and highly dependent data collected from disparate sensors across large-scale network/graph structures to detect anomalies and abnormal behavior in real time. Unlike general purpose (often black-box) machine learning models, this paper proposes a unique framework for network/graph structures that incorporates the complexities of networks and interdependencies between network entities and sensors. Because of the multidisciplinary nature of the paper that involves optimization, machine learning, and system monitoring and control, it can help researchers in both operations research and computer science domains to develop new network-specific computing tools and machine learning frameworks to efficiently manage large-scale network data. [ABSTRACT FROM AUTHOR]

"Multi-agent systems (MAS)" have been extensively studied across various fields, including robotics, economics, biology, and computer science. A distinctive feature of these systems is the ability of multiple agents, each with different characteristics, to perform system-wide tasks through local bottom-up interactions. Furthermore, design and control methods for system networks based on graph theory are being developed. Recent applications of these methods include autonomous driving technology, smart grids, and understanding social systems. This special issue aims to deepen the understanding of MAS, focusing on their control and applications. It features 16 papers, including one review paper. The accepted papers cover a wide range of topics, including reinforcement learning, autonomous mobility systems, and machine learning, presenting the latest research findings on MAS. These studies provide valuable insights into various aspects and potential applications of MAS. We hope that this issue will be beneficial to our readers and contribute to the advancement of future research. [ABSTRACT FROM AUTHOR]

The characterisation of geometric shapes produces their concise description and is, therefore, important for subsequent analyses, for example in Computer Vision, Machine Learning, or shape matching. A new method for extracting characterisation vectors of 2D geometric shapes is proposed in this paper. The shape of interest, embedded into a raster space, is swept several times by sweep-lines having different slopes. The interior shape's points, being in the middle of its boundary and laying on the actual sweep-line, are identified at each stage of the sweeping process. The midpoints are then connected iteratively into chains. The chains are filtered, vectorised, and normalised. The obtained polylines from the vectorisation step are used to design the shape's characterisation vector for further application-specific analyses. The proposed method was verified on numerous shapes, where single- and multi-threaded implementations were compared. Finally, characterisation vectors, among which some were rotated and scaled, were determined for these shapes. The proposed method demonstrated a good rotation- and scaling-invariant identification of equal shapes. [ABSTRACT FROM AUTHOR]

The field of statistical computing is rapidly developing and evolving. Shifting away from the formerly siloed landscape of mathematics, statistics, and computer science, recent advancements in statistical computing are largely characterized by a fusing of these worlds; namely, programming, software development, and applied statistics are merging in new and exciting ways. There are numerous drivers behind this advancement, including open movement (encompassing development, science, and access), the advent of data science as a field, and collaborative problem-solving, as well as practice-altering advances in subfields such as artificial intelligence, machine learning, and Bayesian estimation. In this paper, we trace this shift in how modern statistical computing is performed, and that which has recently emerged from it. This discussion points to a future of boundless potential for the field. [ABSTRACT FROM AUTHOR]

Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield. [ABSTRACT FROM AUTHOR]

Based on the analysis of data we observe that the share of single-authored papers was significantly high in theoretical computer science, while collaborative efforts dominate computer science system research like PL, AI, ML, etc. Collaborative authorship is higher in journals over conferences. Further, values of collaborative indicators are also high for journals except for the Machine Learning (ML) subfield. In addition, the author distribution patterns are different for conferences and journals. The findings also exhibited diversity in authorship trends across sub-fields of CS research. Our results show collaboration trends in conferences and journals of major CS subfields. Such collaborative patterns benefit the funding agency, policymakers, scientific community, and researchers to plan and execute their research. [ABSTRACT FROM AUTHOR]

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers. [ABSTRACT FROM AUTHOR]

Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available. Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations. This has sparked interest in using advanced techniques from mathematics, statistics and computer science to deal with, and benefit from, big data. At the same time, sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion. [ABSTRACT FROM AUTHOR]

Electrical utilities and system operators (SOs) are constantly looking for solutions to problems in the management and control of the power network. For this purpose, SOs are exploring new research fields, which might bring contributions to the power system environment. A clear example is the field of computer science, within which artificial intelligence (AI) has been developed and is being applied to many fields. In power systems, AI could support the fault prediction of cable joints. Despite the availability of many legacy methods described in the literature, fault prediction is still critical, and it needs new solutions. For this purpose, in this paper, the authors made a further step in the evaluation of machine learning methods (ML) for cable joint health assessment. Six ML algorithms have been compared and assessed on a consolidated test scenario. It simulates a distributed measurement system which collects measurements from medium-voltage (MV) cable joints. Typical metrics have been applied to compare the performance of the algorithms. The analysis is then completed considering the actual in-field conditions and the SOs' requirements. The results demonstrate: (i) the pros and cons of each algorithm; (ii) the best-performing algorithm; (iii) the possible benefits from the implementation of ML algorithms. [ABSTRACT FROM AUTHOR]

Recent advances in network representation learning have enabled significant improvement in the link prediction task, which is at the core of many downstream applications. As an increasing amount of mobility data become available because of the development of location-based technologies, we argue that this resourceful mobility data can be used to improve link prediction tasks. In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations. We model user offline location preference via a probabilistic factor model and represent user social relations using neural network representation learning. To capture the interrelationship of these two sources, we develop an anchor link method to align these two different user latent representations. Furthermore, we employ locality-sensitive hashing to project the aggregated user representation into a binary matrix, which not only preserves the data structure but also improves the efficiency of convolutional network learning. By comparing with several baseline methods that solely rely on social networks or mobility data, we show that our unified approach significantly improves the link prediction performance. Summary of Contribution: This paper proposes a novel framework that utilizes both user offline and online behavior for social link prediction by developing several machine learning algorithms, such as probabilistic factor model, neural network embedding, anchor link model, and locality-sensitive hashing. The scope and mission has the following aspects: (1) We develop a data and knowledge modeling approach that demonstrates significant performance improvement. (2) Our method can efficiently manage large-scale data. (3) We conduct rigorous experiments on real-world data sets and empirically show the effectiveness and the efficiency of our proposed method. Overall, our paper can contribute to the advancement of social link prediction, which can spur many downstream applications in information systems and computer science. [ABSTRACT FROM AUTHOR]

Keeping railway tracks in good operational condition is one of the most important tasks for railway owners. As a result, railway companies have to conduct track inspections periodically, which is costly and time-consuming. Due to the rapid development in computer science, many prediction models using machine learning methods have been developed. It is possible to discover the degradation pattern and develop accurate prediction models. The paper reviews the existing prediction methods for railway track degradation, including traditional methods and prediction methods based on machine learning methods, including probabilistic methods, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Grey Model (GM). The advantages, shortage, and applicability of methods are discussed, and recommendations for further research are provided. [ABSTRACT FROM AUTHOR]

This paper explores a series of thoughts about the meaning of intelligence in neuroscience and computer science. This work aims to present an understandable definition that fits our contemporary artificial intelligence background. The research methodology of this essay lies in existing theories of artificial intelligence, focused on computer science and neuroscience. I analyze the relationship between intelligence and neuroscience and Hawkin's Thousand Brains Theory, an approach to show what it is an intelligent agent according to neuroscience. Here, the main result relies on the verification that intelligence is only possible in the neocortex. According to this result, the study performs a second critical analysis aiming to demonstrate why there is no artificial intelligence today. [ABSTRACT FROM AUTHOR]

Capturing long-range dependencies during feature extraction is crucial for video-based person re-identification (re-id) since it would help to tackle many challenging problems such as occlusion and dramatic pose variation. Moreover, capturing subtle differences, such as bags and glasses, is indispensable to distinguish similar pedestrians. In this paper, we propose a novel and efficacious Statistic Attention (SA) block which can capture both the long-range dependencies and subtle differences. SA block leverages high-order statistics of feature maps, which contain both long-range and high-order information. By modeling relations with these statistics, SA block can explicitly capture long-range dependencies with less time complexity. In addition, high-order statistics usually concentrate on details of feature maps and can perceive the subtle differences between pedestrians. In this way, SA block is capable of discriminating pedestrians with subtle differences. Furthermore, this lightweight block can be conveniently inserted into existing deep neural networks at any depth to form Statistic Attention Network (SANet). To evaluate its performance, we conduct extensive experiments on two challenging video re-id datasets, showing that our SANet outperforms the state-of-the-art methods. Furthermore, to show the generalizability of SANet, we evaluate it on three image re-id datasets and two more general image classification datasets, including ImageNet. The source code is available at http://vipl.ict.ac.cn/resources/codes/code/SANet_code.zip. [ABSTRACT FROM AUTHOR]

Information-theoretic quantities like entropy and mutual information have found numerous uses in machine learning. It is well known that there is a strong connection between these entropic quantities and submodularity since entropy over a set of random variables is submodular. In this paper, we study combinatorial information measures that generalize independence, (conditional) entropy, (conditional) mutual information, and total correlation defined over sets of (not necessarily random) variables. These measures strictly generalize the corresponding entropic measures since they are all parameterized via submodular functions that themselves strictly generalize entropy. Critically, we show that, unlike entropic mutual information in general, the submodular mutual information is actually submodular in one argument, holding the other fixed, for a large class of submodular functions whose third-order partial derivatives satisfy a non-negativity property. This turns out to include a number of practically useful cases such as the facility location and set-cover functions. We study specific instantiations of the submodular information measures on these, as well as the probabilistic coverage, graph-cut, log-determinants, and saturated coverage functions and see that they all have mathematically intuitive and practically useful expressions. Finally, we also study generalized independence between subsets of datapoints (random variables in the entropic case), and connect the independence characterizations to independence in log-submodular distributions. Regarding applications, we connect the maximization of submodular (conditional) mutual information to problems such as mutual-information-based, query-based, and privacy preserving summarization—and we connect optimizing the multi-set submodular mutual information to clustering and robust partitioning. We perform real world as well as synthetic experiments on various data summarization tasks. [ABSTRACT FROM AUTHOR]

Searching for new high temperature superconductors has long been a key research issue. Fe-based superconductors attract researchers' attention due to their high transition temperature, strong irreversibility field, and excellent crystallographic symmetry. By using doping methods and dopant levels, different types of new Fe-based superconductors are synthesized. The transition temperature is a key indicator to measure whether new superconductors are high temperature superconductors. However, the condition for measuring transition temperature are strict, and the measurement process is dangerous. There is a strong relationship between the lattice parameters and the transition temperature of Fe-based superconductors. To avoid the difficulties in measuring transition temperature, in this paper, we adopt a machine learning method to build a model based on the lattice parameters to predict the transition temperature of Fe-based superconductors. The model results are in accordance with available transition temperatures, showing 91.181% accuracy. Therefore, we can use the proposed model to predict unknown transition temperatures of Fe-based superconductors. [ABSTRACT FROM AUTHOR]

Modern businesses benefit significantly from advances in computer vision technology, one of the important sectors of artificially intelligent and computer science research. Advanced computer vision issues like image processing, object recognition, and biometric authentication can benefit from using deep learning methods. As smart devices and facilities advance rapidly, current networks such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing demand. Classification of images, object classification, and facial recognition software are some of the most difficult computer vision problems that can be solved using deep learning methods. As a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to solve the above challenges by introducing a new methodology named Optimizing Computer Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency algorithms (DL-EA) for computer vision to address the issues mentioned and improve the system's outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper to recognize pattern recognition and intelligent management systems and provide driven methodology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can summarize the significant areas for future research and potential solutions, including image enhancement, machine vision, and access control. [ABSTRACT FROM AUTHOR]

Over the last 20 years Machine Learning (ML) has been applied to a wide variety of applications in the fields of engineering and computer science. In the field of material science in particular, it has been used to help speed up predictions of structure property relationships and in general enhance the material design process. In this paper, we review the current status of ML and its specific application to polymer composites process simulation. We also review some case studies going beyond this focus, especially in the fields of computational fluid dynamics, solid mechanics and Computer Aided Engineering (CAE), to show the potential for further application in our research area. The types of ML algorithms, tools, techniques used in the various applications and their couplings with other CAE software tools are summarized and the overall result/potential of each application/method is highlighted. [ABSTRACT FROM AUTHOR]

Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to design improved and efficient state-of-the-art clustering algorithms. • Provide an up-to-date comprehensive review of the different clustering techniques. • Highlight novel and most recent practical applications areas of clustering. • Provide a convenient research path for new researchers. • Help experts develop new algorithms for emerging challenges in the research area. [ABSTRACT FROM AUTHOR]

Prediction of visual attention is a new and challenging subject, and to the best of our knowledge, there are not many pieces of research devoted to the anticipation of students' cognition when solving tests. The aim of this paper is to propose, implement, and evaluate a machine learning method that is capable of predicting saliency maps of students who participate in a learning task in the form of quizzes based on quiz questionnaire images. Our proposal utilizes several deep encoder–decoder symmetric schemas which are trained on a large set of saliency maps generated with eye tracking technology. Eye tracking data were acquired from students, who solved various tasks in the sciences and natural sciences (computer science, mathematics, physics, and biology). The proposed deep convolutional encoder–decoder network is capable of producing accurate predictions of students' visual attention when solving quizzes. Our evaluation showed that predictions are moderately positively correlated with actual data with a coefficient of 0.547 ± 0.109. It achieved better results in terms of correlation with real saliency maps than state-of-the-art methods. Visual analyses of the saliency maps obtained also correspond with our experience and expectations in this field. Both source codes and data from our research can be downloaded in order to reproduce our results. [ABSTRACT FROM AUTHOR]