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The 15th International Computer Science Symposium in Russia (CSR 2020) was organized by the Ural Federal University located in Ekaterinburg, Russian Federation [...]

This Special Issue contains four papers focused on hyperspectral data analysis [...]

This is the second edition of a special issue of Algorithms that is of a rather different nature compared to other Special Issues in the journal, which are usually dedicated to a particular subject in the area of algorithms [...]

Scientific papers published in journals or conferences, also considered academic publications, are the manifestation of scientific research achievements. Lots of scientific papers published in digital form bring new challenges for academic evaluation and information retrieval. Therefore, research on the ranking method of scientific papers is significant for the management and evaluation of academic resources. In this paper, we first identify internal and external factors for evaluating scientific papers and propose a publication ranking method based on an analysis of a heterogeneous academic network. We use four types of metadata (i.e., author, venue (journal or conference), topic, and title) as vertexes for creating the network; in there, the topics are trained by the SentenceLDA algorithm with the metadata of the abstract. We then use the Gibbs sampling method to create a heterogeneous academic network and apply the ConNetClus algorithm to calculate the probability value of publication ranking. To evaluate the significance of the method proposed in this paper, we compare the ranking results with BM25, PageRank, etc., and homogeneous networks in MAP and NDCG. As shown in our evaluation results, the performance of the method we propose in this paper is better than other baselines for ranking publications. [ABSTRACT FROM AUTHOR]

Fake news is an explosive subject, being undoubtedly among the most controversial and difficult challenges facing society in the present-day environment of technology and information, which greatly affects the individuals who are vulnerable and easily influenced, shaping their decisions, actions, and even beliefs. In the course of discussing the gravity and dissemination of the fake news phenomenon, this article aims to clarify the distinctions between fake news, misinformation, and disinformation, along with conducting a thorough analysis of the most widely read academic papers that have tackled the topic of fake news research using various machine learning techniques. Utilizing specific keywords for dataset extraction from Clarivate Analytics' Web of Science Core Collection, the bibliometric analysis spans six years, offering valuable insights aimed at identifying key trends, methodologies, and notable strategies within this multidisciplinary field. The analysis encompasses the examination of prolific authors, prominent journals, collaborative efforts, prior publications, covered subjects, keywords, bigrams, trigrams, theme maps, co-occurrence networks, and various other relevant topics. One noteworthy aspect related to the extracted dataset is the remarkable growth rate observed in association with the analyzed subject, indicating an impressive increase of 179.31%. The growth rate value, coupled with the relatively short timeframe, further emphasizes the research community's keen interest in this subject. In light of these findings, the paper draws attention to key contributions and gaps in the existing literature, providing researchers and decision-makers innovative viewpoints and perspectives on the ongoing battle against the spread of fake news in the age of information. [ABSTRACT FROM AUTHOR]

The paper [[10]] considers an assignment problem and some modifications which can be converted to routing, distribution, or scheduling problems. This special issue of I Algorithms i is dedicated to recent developments of scheduling algorithms and new applications. References 1 Werner F., Burtseva L., Sotskov Y. Special Issue on Algorithms for Scheduling Problems. For this problem, a hybrid metaheuristic algorithm is presented which combines a genetic algorithm with a so-called spotted hyena optimization algorithm. [Extracted from the article]

In this paper, we propose a method to aid authors in choosing alternative keywords that help their papers gain visibility. These alternative keywords must have a certain level of popularity in the scientific community and, simultaneously, be keywords with fewer competitors. The competitors are derived from other papers containing the same keywords. Having fewer competitors would allow an author’s paper to have a higher consult frequency. In order to recommend keywords, we must first determine an attention–survival score. The attention score is obtained using the popularity of a keyword. The survival score is derived from the number of manuscripts using the same keyword. With these two scores, we created a new algorithm that finds alternative keywords with a high attention–survival score. We used ontologies to ensure that alternative keywords proposed by our method are semantically related to the original authors’ keywords that they wish to refine. The hierarchical structure in an ontology supports the relationship between the alternative and input keywords. To test the sensibility of the ontology, we used two sources: WordNet and the Computer Science Ontology (CSO). Finally, we launched a survey for the human validation of our algorithm using keywords from Web of Science papers and three ontologies: WordNet, CSO, and DBpedia. We obtained good results from all our tests.

Edge computing is one of the technological areas currently considered among the most promising for the implementation of many types of applications. In particular, IoT-type applications can benefit from reduced latency and better data protection. However, the price typically to be paid in order to benefit from the offered opportunities includes the need to use a reduced amount of resources compared to the traditional cloud environment. Indeed, it may happen that only one computing node can be used. In these situations, it is essential to introduce computing and memory resource management techniques that allow resources to be optimized while still guaranteeing acceptable performance, in terms of latency and probability of rejection. For this reason, the use of serverless technologies, managed by reinforcement learning algorithms, is an active area of research. In this paper, we explore and compare the performance of some machine learning algorithms for managing horizontal function autoscaling in a serverless edge computing system. In particular, we make use of open serverless technologies, deployed in a Kubernetes cluster, to experimentally fine-tune the performance of the algorithms. The results obtained allow both the understanding of some basic mechanisms typical of edge computing systems and related technologies that determine system performance and the guiding of configuration choices for systems in operation. [ABSTRACT FROM AUTHOR]

As a crucial task in natural language processing, table question answering has garnered significant attention from both the academic and industrial communities. It enables intelligent querying and question answering over structured data by translating natural language into corresponding SQL statements. Recently, there have been notable advancements in the general domain table question answering task, achieved through prompt learning with large language models. However, in specific domains, where tables often have a higher number of columns and questions tend to be more complex, large language models are prone to generating invalid SQL or NoSQL statements. To address the above issue, this paper proposes a novel few-shot table prompt question answering approach. Specifically, we design a prompt template construction strategy for structured SQL generation. It utilizes prompt templates to restructure the input for each test data and standardizes the model output, which can enhance the integrity and validity of generated SQL. Furthermore, this paper introduces a contrastive exemplar selection approach based on the question patterns and formats in domain-specific contexts. This enables the model to quickly retrieve the relevant exemplars and learn characteristics about given question. Experimental results on the two datasets in the domains of electric energy and structural inspection show that the proposed approach outperforms the baseline models across all comparison settings. [ABSTRACT FROM AUTHOR]

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance. [ABSTRACT FROM AUTHOR]

This document is a summary of a journal article titled "Artificial Intelligence in Modeling and Simulation." The article discusses the integration of artificial intelligence (AI) into modeling and simulation (M&S) processes. It highlights the various applications of AI in fields such as engineering, physics, social sciences, and biology. The article also provides an overview of 11 selected papers from a special issue on AI and M&S, covering topics such as AI techniques for simulation and optimization, AI in agent-based modeling, AI for data processing and classification models, and artificial neural network (ANN) methods for improved M&S. The papers explore different methodologies and approaches to enhance the efficiency and validity of modeling and simulation using AI. The article concludes by emphasizing the progress and diverse uses of AI in M&S and expressing gratitude to the authors, reviewers, and editorial team involved in the special issue. [Extracted from the article]

With the rapid development of computer technology, communication technology, and control technology, cyber-physical systems (CPSs) have been widely used and developed. However, there are massive information interactions in CPSs, which lead to an increase in the amount of data transmitted over the network. The data communication, once attacked by the network, will seriously affect the security and stability of the system. In this paper, for the data tampering attack existing in the linear system with multiple binary observations, in the case where the estimation algorithm of the defender is unknown, the optimization index is constructed based on information entropy from the attacker's point of view, and the problem is modeled. For the problem of the multi-parameter optimization with energy constraints, this paper uses particle swarm optimization (PSO) to obtain the optimal data tampering attack solution set, and gives the estimation method of unknown parameters in the case of unknown parameters. To implement the real-time improvement of online implementation, the BP neural network is designed. Finally, the validity of the conclusions is verified through numerical simulation. This means that the attacker can construct effective metrics based on information entropy without the knowledge of the defense's discrimination algorithm. In addition, the optimal attack strategy implementation based on PSO and BP is also effective. [ABSTRACT FROM AUTHOR]

This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan) and the minimization of the mean weighted flow time. Both are particularly important for improving machines' utilization and customer satisfaction level in maintenance and healthcare diagnostic systems, in which the studied Bi-DMOSP is mostly encountered. Since the studied problem is NP-hard for both objectives, fast algorithms are needed to fulfill the requirements of real-life circumstances. Previous attempts have included the development of an exact algorithm and two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective gray wolf optimizer (MOGWO). The exact algorithm is limited to small-sized instances; meanwhile, NSGA-II was found to produce better results compared to MOGWO in both small- and large-sized test instances. The proposed MOSS in this paper attempts to provide more efficient non-dominated solutions for the studied Bi-DMOSP. This is achievable via its hybridization with a novel, bi-objective tabu search approach that utilizes a set of efficient neighborhood search functions. Parameter tuning experiments are conducted first using a subset of small-sized benchmark instances for which the optimal Pareto front solutions are known. Then, detailed computational experiments on small- and large-sized instances are conducted. Comparisons with the previously developed NSGA-II metaheuristic demonstrate the superiority of the proposed MOSS approach for small-sized instances. For large-sized instances, it proves its capability of producing competitive results for instances with low and medium density. [ABSTRACT FROM AUTHOR]

Data-driven models used for predictive classification and regression tasks are commonly computed using floating-point arithmetic and powerful computers. We address constraints in distributed sensor networks like the IoT, edge, and material-integrated computing, providing only low-resource embedded computers with sensor data that are acquired and processed locally. Sensor networks are characterized by strong heterogeneous systems. This work introduces and evaluates a virtual machine architecture that provides ML as a service layer (MLaaS) on the node level and addresses very low-resource distributed embedded computers (with less than 20 kB of RAM). The VM provides a unified ML instruction set architecture that can be programmed to implement decision trees, ANN, and CNN model architectures using scaled integer arithmetic only. Models are trained primarily offline using floating-point arithmetic, finally converted by an iterative scaling and transformation process, demonstrated in this work by two tests based on simulated and synthetic data. This paper is an extended version of the FedCSIS 2023 conference paper providing new algorithms and ML applications, including ANN/CNN-based regression and classification tasks studying the effects of discretization on classification and regression accuracy. [ABSTRACT FROM AUTHOR]

Amazons is a computerized board game with complex positions that are highly challenging for humans. In this paper, we propose an efficient optimization of the Monte Carlo tree search (MCTS) algorithm for Amazons, fusing the 'Move Groups' strategy and the 'Parallel Evaluation' optimization strategy (MG-PEO). Specifically, we explain the high efficiency of the Move Groups strategy by defining a new criterion: the winning convergence distance. We also highlight the strategy's potential issue of falling into a local optimum and propose that the Parallel Evaluation mechanism can compensate for this shortcoming. Moreover, We conducted rigorous performance analysis and experiments. Performance analysis results indicate that the MCTS algorithm with the Move Groups strategy can improve the playing ability of the Amazons game by 20–30 times compared to the traditional MCTS algorithm. The Parallel Evaluation optimization further enhances the playing ability of the Amazons game by 2–3 times. Experimental results show that the MCTS algorithm with the MG-PEO strategy achieves a 23% higher game-winning rate on average compared to the traditional MCTS algorithm. Additionally, the MG-PEO Amazons program proposed in this paper won first prize in the Amazons Competition at the 2023 China Collegiate Computer Games Championship & National Computer Games Tournament. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is to present a simple algorithm that theoretically and numerically assesses the switching angles of an inverter operated with the SPWM technique. This technique is the most widely used for eliminating harmonics in DC-AC converters for powering motors, renewable energy applications, household appliances, etc. Unlike conventional implementations of the SPWM technique based on the analog or digital comparison of a sinusoidal signal with a triangular signal, this paper mathematically performs this comparison. It proposes a simple solution to solve the transcendental equations arising from the mathematical analysis numerically. The technique is validated by calculating the total harmonic distortion (THD) of the generated signal theoretically and numerically, and the results indicate that the calculated angles produce the same distribution of harmonics calculated analytically and numerically. The algorithm is limited to single-phase inverters with unipolar SPWM. [ABSTRACT FROM AUTHOR]

Current stomach disease detection and diagnosis is challenged by data complexity and high dimensionality and requires effective deep learning algorithms to improve diagnostic accuracy. To address this challenge, in this paper, an improved strategy based on the Adam algorithm is proposed, which aims to alleviate the influence of local optimal solutions, overfitting, and slow convergence rates by controlling the restart strategy and the gradient norm joint clipping technique. This improved algorithm is abbreviated as the CG-Adam algorithm. The control restart strategy performs a restart operation by periodically checking the number of steps and once the number of steps reaches a preset restart period. After the restart is completed, the algorithm will restart the optimization process. It helps the algorithm avoid falling into the local optimum and maintain convergence stability. Meanwhile, gradient norm joint clipping combines both gradient clipping and norm clipping techniques, which can avoid gradient explosion and gradient vanishing problems and help accelerate the convergence of the optimization process by restricting the gradient and norm to a suitable range. In order to verify the effectiveness of the CG-Adam algorithm, experimental validation is carried out on the MNIST, CIFAR10, and Stomach datasets and compared with the Adam algorithm as well as the current popular optimization algorithms. The experimental results demonstrate that the improved algorithm proposed in this paper achieves an accuracy of 98.59%, 70.7%, and 73.2% on the MNIST, CIFAR10, and Stomach datasets, respectively, surpassing the Adam algorithm. The experimental results not only prove the significant effect of the CG-Adam algorithm in accelerating the model convergence and improving generalization performance but also demonstrate its wide potential and practical application value in the field of medical image recognition. [ABSTRACT FROM AUTHOR]

In traditional finance, option prices are typically calculated using crisp sets of variables. However, as reported in the literature novel, these parameters possess a degree of fuzziness or uncertainty. This allows participants to estimate option prices based on their risk preferences and beliefs, considering a range of possible values for the parameters. This paper presents a comprehensive review of existing work on fuzzy fractional Brownian motion and proposes an extension in the context of financial option pricing. In this paper, we define a unified framework combining fractional Brownian motion with fuzzy processes, creating a joint product measure space that captures both randomness and fuzziness. The approach allows for the consideration of individual risk preferences and beliefs about parameter uncertainties. By extending Merton's jump-diffusion model to include fuzzy fractional Brownian motion, this paper addresses the modelling needs of hybrid systems with uncertain variables. The proposed model, which includes fuzzy Poisson processes and fuzzy volatility, demonstrates advantageous properties such as long-range dependence and self-similarity, providing a robust tool for modelling financial markets. By incorporating fuzzy numbers and the belief degree, this approach provides a more flexible framework for practitioners to make their investment decisions. [ABSTRACT FROM AUTHOR]

Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms. Adversarial training is one of the methods used to defend against the threat of adversarial attacks. It is a training schema that utilizes an alternative objective function to provide model generalization for both adversarial data and clean data. In this systematic review, we focus particularly on adversarial training as a method of improving the defensive capacities and robustness of machine learning models. Specifically, we focus on adversarial sample accessibility through adversarial sample generation methods. The purpose of this systematic review is to survey state-of-the-art adversarial training and robust optimization methods to identify the research gaps within this field of applications. The literature search was conducted using Engineering Village (Engineering Village is an engineering literature search tool, which provides access to 14 engineering literature and patent databases), where we collected 238 related papers. The papers were filtered according to defined inclusion and exclusion criteria, and information was extracted from these papers according to a defined strategy. A total of 78 papers published between 2016 and 2021 were selected. Data were extracted and categorized using a defined strategy, and bar plots and comparison tables were used to show the data distribution. The findings of this review indicate that there are limitations to adversarial training methods and robust optimization. The most common problems are related to data generalization and overfitting. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) algorithms are playing a crucial role in transforming healthcare by enhancing the quality, accessibility, and efficiency of medical care, research, and operations. These algorithms enable healthcare providers to offer more accurate diagnoses, predict outcomes, and customize treatments to individual patient needs. AI also improves operational efficiency by automating routine tasks and optimizing resource management. However, there are challenges to adopting AI in healthcare, such as data privacy concerns and potential biases in algorithms. Collaboration among stakeholders is necessary to ensure ethical use of AI and its positive impact on the field. AI also has applications in medical research, preventive medicine, and public health. It is important to recognize that AI should augment, not replace, the expertise and compassionate care provided by healthcare professionals. The ethical implications and societal impact of AI in healthcare must be carefully considered, guided by fairness, transparency, and accountability principles. Several research papers in this special issue explore the application of AI algorithms in various aspects of healthcare, such as gait analysis for Parkinson's disease diagnosis, human activity recognition, heart disease prediction, compliance assessment with clinical protocols, epidemic management, neurological complications identification, fall prevention, leukemia diagnosis, and genetic clinical pathways. These studies demonstrate the potential of AI in improving medical diagnostics, patient monitoring, and personalized care. [Extracted from the article]

Salient object detection (SOD) aims to identify the most visually striking objects in a scene, simulating the function of the biological visual attention system. The attention mechanism in deep learning is commonly used as an enhancement strategy which enables the neural network to concentrate on the relevant parts when processing input data, effectively improving the model's learning and prediction abilities. Existing saliency object detection methods based on RGB deep learning typically treat all regions equally by using the extracted features, overlooking the fact that different regions have varying contributions to the final predictions. Based on the U2Net algorithm, this paper incorporates the split coordinate channel attention (SCCA) mechanism into the feature extraction stage. SCCA conducts spatial transformation in width and height dimensions to efficiently extract the location information of the target to be detected. While pixel-level semantic segmentation based on annotation has been successful, it assigns the same weight to each pixel which leads to poor performance in detecting the boundary of objects. In this paper, the Canny edge detection loss is incorporated into the loss calculation stage to improve the model's ability to detect object edges. Based on the DUTS and HKU-IS datasets, experiments confirm that the proposed strategies effectively enhance the model's detection performance, resulting in a 0.8% and 0.7% increase in the F1-score of U2Net. This paper also compares the traditional attention modules with the newly proposed attention, and the SCCA attention module achieves a top-three performance in prediction time, mean absolute error (MAE), F1-score, and model size on both experimental datasets. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning's role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area. [ABSTRACT FROM AUTHOR]

Current edge devices for neural networks such as FPGA, CPLD, and ASIC can support low bit-width computing to improve the execution latency and energy efficiency, but traditional linear quantization can only maintain the inference accuracy of neural networks at a bit-width above 6 bits. Different from previous studies that address this problem by clipping the outliers, this paper proposes a two-stage quantization method. Before converting the weights into fixed-point numbers, this paper first prunes the network by unstructured pruning and then uses the K-means algorithm to cluster the weights in advance to protect the distribution of the weights. To solve the instability problem of the K-means results, the PSO (particle swarm optimization) algorithm is exploited to obtain the initial cluster centroids. The experimental results on baseline deep networks such as ResNet-50, Inception-v3, and DenseNet-121 show the proposed optimized quantization method can generate a 5-bit network with an accuracy loss of less than 5% and a 4-bit network with only 10% accuracy loss as compared to 8-bit quantization. By quantization and pruning, this method reduces the model bit-width from 32 to 4 and the number of neurons by 80%. Additionally, it can be easily integrated into frameworks such as TensorRt and TensorFlow-Lite for low bit-width network quantization. [ABSTRACT FROM AUTHOR]

Nowadays, digital transformation (DX) is the key concept to change and improve the operations in governments, companies, and schools. Therefore, any data should be digitized for processing by computers. Unfortunately, a lot of data and information are printed and handled on paper, although they may originally come from digital sources. Data on paper can be digitized using an optical character recognition (OCR) software. However, if the paper contains a table, it becomes difficult because of the separated characters by rows and columns there. It is necessary to solve the research question of "how to convert a printed table on paper into an Excel table while keeping the relationships between the cells?" In this paper, we propose a printed table digitization algorithm using image processing techniques and OCR software for it. First, the target paper is scanned into an image file. Second, each table is divided into a collection of cells where the topology information is obtained. Third, the characters in each cell are digitized by OCR software. Finally, the digitalized data are arranged in an Excel file using the topology information. We implement the algorithm on Python using OpenCV for the image processing library and Tesseract for the OCR software. For evaluations, we applied the proposal to 19 scanned and 17 screenshotted table images. The results show that for any image, the Excel file is generated with the correct structure, and some characters are misrecognized by OCR software. The improvement will be in future works. [ABSTRACT FROM AUTHOR]

Consider a collection of entities moving continuously with bounded speed, but otherwise unpredictably, in some low-dimensional space. Two such entities encroach upon one another at a fixed time if their separation is less than some specified threshold. Encroachment, of concern in many settings such as collision avoidance, may be unavoidable. However, the associated difficulties are compounded if there is uncertainty about the precise location of entities, giving rise to potential encroachment and, more generally, potential congestion within the full collection. We adopt a model in which entities can be queried for their current location (at some cost) and the uncertainty region associated with an entity grows in proportion to the time since that entity was last queried. The goal is to maintain low potential congestion, measured in terms of the (dynamic) intersection graph of uncertainty regions, at specified (possibly all) times, using the lowest possible query cost. Previous work in the same uncertainty model addressed the problem of minimizing the congestion potential of point entities using location queries of some bounded frequency. It was shown that it is possible to design query schemes that are O (1) -competitive, in terms of worst-case congestion potential, with other, even clairvoyant query schemes (that exploit knowledge of the trajectories of all entities), subject to the same bound on query frequency. In this paper, we initiate the treatment of a more general problem with the complementary optimization objective: minimizing the query frequency, measured as the reciprocal of the minimum time between queries (granularity), while guaranteeing a fixed bound on congestion potential of entities with positive extent at one specified target time. This complementary objective necessitates quite different schemes and analyses. Nevertheless, our results parallel those of the earlier papers, specifically tight competitive bounds on required query frequency. [ABSTRACT FROM AUTHOR]

A major area of application of multiobjective path problems and resolution algorithms is telecommunication network routing design, taking into account the extremely rapid technological and service evolutions. The need for explicit consideration of heterogeneous Quality of Service metrics makes it advantageous for the development of routing models where various technical–economic aspects, often conflicting, should be tackled. Our work is focused on multiobjective path problem formulations and resolution methods and their applications to routing methods. We review basic concepts and present main formulations of multiobjective path problems, considering different types of objective functions. We outline the different types of resolution methods for these problems, including a classification and overview of relevant algorithms concerning different types of problems. Afterwards, we outline background concepts on routing models and present an overview of selected papers considered as representative of different types of applications of multiobjective path problem formulations and algorithms. A broad characterization of major types of path problems relevant in this context is shown regarding the overview of contributions in different technological and architectural network environments. Finally, we outline research trends in this area, in relation to recent technological evolutions in communication networks. [ABSTRACT FROM AUTHOR]

The interconnection of Operational Technology (OT) and Information Technology (IT) has created new opportunities for remote management, data storage in the cloud, real-time data transfer over long distances, or integration between different OT and IT networks. OT networks require increased attention due to the convergence of IT and OT, mainly due to the increased risk of cyber-attacks targeting these networks. This paper focuses on the analysis of different methods and data processing for protocol recognition and traffic classification in the context of OT specifics. Therefore, this paper summarizes the methods used to classify network traffic, analyzes the methods used to recognize and identify the protocol used in the industrial network, and describes machine learning methods to recognize industrial protocols. The output of this work is a comparative analysis of approaches specifically for protocol recognition and traffic classification in OT networks. In addition, publicly available datasets are compared in relation to their applicability for industrial protocol recognition. Research challenges are also identified, highlighting the lack of relevant datasets and defining directions for further research in the area of protocol recognition and classification in OT environments. [ABSTRACT FROM AUTHOR]

In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts. [ABSTRACT FROM AUTHOR]

Intelligent ship navigation scheduling and planning is of great significance for ensuring the safety of maritime production and life and promoting the development of the marine economy. In this paper, an intelligent ship scheduling and path planning method is proposed for a practical application scenario wherein the emergency rescue center receives rescue messages and dispatches emergency rescue ships to the incident area for rescue. Firstly, the large-scale sailing route of the task ship is pre-planned in the voyage planning stage by using the improved A* algorithm. Secondly, the full-coverage path planning algorithm is used to plan the ship's search route in the regional search stage by updating the ship's navigation route in real time. In order to verify the effectiveness of the proposed algorithm, comparative experiments were carried out with the conventional algorithm in the two operation stages of rushing to the incident sea area and regional search and rescue. The experimental results show that the proposed algorithm can adapt to emergency search and rescue tasks in the complex setting of the sea area and can effectively improve the efficiency of the operation, ensure the safety of the operation process, and provide a more intelligent and efficient solution for the planning of maritime emergency rescue tasks. [ABSTRACT FROM AUTHOR]

This paper addresses the following problem: given m potential observations to determine n parameters, m > n , what is the best choice of n observations. The problem can be formulated as finding the n × n submatrix of the complete m × n observation matrix that has maximum determinant. An algorithm by Gu and Eisenstat for a determining a strongly rank-revealing QR factorisation of a matrix can be adapted to address this latter formulation. The algorithm starts with an initial selection of n rows of the observation matrix and then performs a sequence of row interchanges, with the determinant of the current submatrix strictly increasing at each step until no further improvement can be made. The algorithm implements rank-one updating strategies, which leads to a compact and efficient algorithm. The algorithm does not necessarily determine the global optimum but provides a practical approach to designing an effective measurement strategy. In this paper, we describe how the Gu–Eisenstat algorithm can be adapted to address the problem of optimal experimental design and used with the QR algorithm with column pivoting to provide effective designs. We also describe implementations of sequential algorithms to add further measurements that optimise the information gain at each step. We illustrate performance on several metrology examples. [ABSTRACT FROM AUTHOR]

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development. [ABSTRACT FROM AUTHOR]

The early diagnosis of diabetic retinopathy (DR) can effectively prevent irreversible vision loss and assist ophthalmologists in providing timely and accurate treatment plans. However, the existing methods based on deep learning have a weak perception ability of different scale information in retinal fundus images, and the segmentation capability of subtle lesions is also insufficient. This paper aims to address these issues and proposes MLNet for DR lesion segmentation, which mainly consists of the Multi-Scale Attention Block (MSAB) and the Lesion Perception Block (LPB). The MSAB is designed to capture multi-scale lesion features in fundus images, while the LPB perceives subtle lesions in depth. In addition, a novel loss function with tailored lesion weight is designed to reduce the influence of imbalanced datasets on the algorithm. The performance comparison between MLNet and other state-of-the-art methods is carried out in the DDR dataset and DIARETDB1 dataset, and MLNet achieves the best results of 51.81% mAUPR, 49.85% mDice, and 37.19% mIoU in the DDR dataset, and 67.16% mAUPR and 61.82% mDice in the DIARETDB1 dataset. The generalization experiment of MLNet in the IDRiD dataset achieves 59.54% mAUPR, which is the best among other methods. The results show that MLNet has outstanding DR lesion segmentation ability. [ABSTRACT FROM AUTHOR]

This paper explores the concept of proportional lumpability as an extension of the original definition of lumpability, addressing the challenges posed by the state space explosion problem in computing performance indices for large stochastic models. Lumpability traditionally relies on state aggregation techniques and is applicable to Markov chains demonstrating structural regularity. Proportional lumpability extends this idea, proposing that the transition rates of a Markov chain can be modified by certain factors, resulting in a lumpable new Markov chain. This concept facilitates the derivation of precise performance indices for the original process. This paper establishes the well-defined nature of the problem of computing the coarsest proportional lumpability that refines a given initial partition, ensuring a unique solution exists. Additionally, a polynomial time algorithm is introduced to solve this problem, offering valuable insights into both the concept of proportional lumpability and the broader realm of partition refinement techniques. The effectiveness of proportional lumpability is demonstrated through a case study that consists of designing a model to investigate selfish mining behaviors on public blockchains. This research contributes to a better understanding of efficient approaches for handling large stochastic models and highlights the practical applicability of proportional lumpability in deriving exact performance indices. [ABSTRACT FROM AUTHOR]

This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking. [ABSTRACT FROM AUTHOR]

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth's surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model's output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry. [ABSTRACT FROM AUTHOR]

This paper presents a time-dependent reliability analysis created for a critical energy infrastructure use case, which consists of an interconnected urban power grid and a communication network. By utilizing expert knowledge from the energy and communication sectors and integrating the renewal theory of multi-component systems, a representative reliability model of this interconnected energy infrastructure, based on real network located in the Czech Republic, is established. This model assumes reparable and non-reparable components and captures the topology of the interconnected infrastructure and reliability characteristics of both the power grid and the communication network. Moreover, a time-dependent reliability assessment of the interconnected system is provided. One of the significant outputs of this research is the identification of the critical components of the interconnected network and their interdependencies by the directed acyclic graph. Numerical results indicate that the original design has an unacceptable large unavailability. Thus, to improve the reliability of the interconnected system, a slightly modified design, in which only a limited number of components in the system are modified to keep the additional costs of the improved design limited, is proposed. Consequently, numerical results indicate reducing the unavailability of the improved interconnected system in comparison with the initial reliability design. The proposed unavailability exploration strategy is general and can bring a valuable reliability improvement in the power and communication sectors. [ABSTRACT FROM AUTHOR]

A well-functioning smart grid is an essential part of an efficient and uninterrupted power supply for the key enablers of smart cities. To effectively manage the operations of a smart grid, there is an essential requirement for a seamless wireless communication system that provides high data rates, reliability, flexibility, massive connectivity, low latency, security, and adaptability to changing needs. A contemporary review of the utilization of emerging 6G wireless communication for the major applications of smart grids, especially in terms of massive connectivity and monitoring, secured communication for operation and resource management, and time-critical operations, are presented in this paper. This article starts with the key enablers of the smart city, along with the necessity of the smart grid for the key enablers of it. The fundamentals of the smart city, smart grid, and 6G wireless communication are also introduced in this paper. Moreover, the motivations to integrate 6G wireless communication with the smart grid system are expressed in this article as well. The relevant literature overview, along with the novelty of this paper, is depicted to bridge the gap of the current research works. We describe the novel technologies of 6G wireless communication to effectively perform the considered smart grid applications. Novel technologies of 6G wireless communication have significantly improved the key performance indicators compared to the prior generation of the wireless communication system. A significant part of this article is the contemporary survey of the considered major applications of a smart grid that is served by 6G. In addition, the anticipated challenges and interesting future research pathways are also discussed explicitly in this article. This article serves as a valuable resource for understanding the potential of 6G wireless communication in advancing smart grid applications and addressing emerging challenges. [ABSTRACT FROM AUTHOR]

This paper studies various deep learning models for word-level lip-reading technology, one of the tasks in the supervised learning of video classification. Several public datasets have been published in the lip-reading research field. However, few studies have investigated lip-reading techniques using multiple datasets. This paper evaluates deep learning models using four publicly available datasets, namely Lip Reading in the Wild (LRW), OuluVS, CUAVE, and Speech Scene by Smart Device (SSSD), which are representative datasets in this field. LRW is one of the large-scale public datasets and targets 500 English words released in 2016. Initially, the recognition accuracy of LRW was 66.1%, but many research groups have been working on it. The current the state of the art (SOTA) has achieved 94.1% by 3D-Conv + ResNet18 + {DC-TCN, MS-TCN, BGRU} + knowledge distillation + word boundary. Regarding the SOTA model, in this paper, we combine existing models such as ResNet, WideResNet, WideResNet, EfficientNet, MS-TCN, Transformer, ViT, and ViViT, and investigate the effective models for word lip-reading tasks using six deep learning models with modified feature extractors and classifiers. Through recognition experiments, we show that similar model structures of 3D-Conv + ResNet18 for feature extraction and MS-TCN model for inference are valid for four datasets with different scales. [ABSTRACT FROM AUTHOR]

Machine-learning-based text classification is one of the leading research areas and has a wide range of applications, which include spam detection, hate speech identification, reviews, rating summarization, sentiment analysis, and topic modelling. Widely used machine-learning-based research differs in terms of the datasets, training methods, performance evaluation, and comparison methods used. In this paper, we surveyed 224 papers published between 2003 and 2022 that employed machine learning for text classification. The Preferred Reporting Items for Systematic Reviews (PRISMA) statement is used as the guidelines for the systematic review process. The comprehensive differences in the literature are analyzed in terms of six aspects: datasets, machine learning models, best accuracy, performance evaluation metrics, training and testing splitting methods, and comparisons among machine learning models. Furthermore, we highlight the limitations and research gaps in the literature. Although the research works included in the survey perform well in terms of text classification, improvement is required in many areas. We believe that this survey paper will be useful for researchers in the field of text classification. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to aid authors in choosing alternative keywords that help their papers gain visibility. These alternative keywords must have a certain level of popularity in the scientific community and, simultaneously, be keywords with fewer competitors. The competitors are derived from other papers containing the same keywords. Having fewer competitors would allow an author's paper to have a higher consult frequency. In order to recommend keywords, we must first determine an attention–survival score. The attention score is obtained using the popularity of a keyword. The survival score is derived from the number of manuscripts using the same keyword. With these two scores, we created a new algorithm that finds alternative keywords with a high attention–survival score. We used ontologies to ensure that alternative keywords proposed by our method are semantically related to the original authors' keywords that they wish to refine. The hierarchical structure in an ontology supports the relationship between the alternative and input keywords. To test the sensibility of the ontology, we used two sources: WordNet and the Computer Science Ontology (CSO). Finally, we launched a survey for the human validation of our algorithm using keywords from Web of Science papers and three ontologies: WordNet, CSO, and DBpedia. We obtained good results from all our tests. [ABSTRACT FROM AUTHOR]

This paper presents a filter generating method that modifies sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. For our earlier study, we developed a prototype that incorporates bed-leaving detection sensors using piezoelectric films and a machine-learning-based behavior recognition method using counter-propagation networks (CPNs). Our method learns topology and relations between input features and teaching signals. Nevertheless, CPNs have been insufficient to address individual differences in parameters such as weight and height used for bed-learning behavior recognition. For this study, we actualize automatic calibration of sensor signals for invariance relative to these body parameters. This paper presents two experimentally obtained results from our earlier study. They were obtained using low-accuracy sensor signals. For the preliminary experiment, we optimized the original sensor signals to approximate high-accuracy ideal sensor signals using generated filters. We used fitness to assess differences between the original signal patterns and ideal signal patterns. For application experiments, we used fitness calculated from the recognition accuracy obtained using CPNs. The experimentally obtained results reveal that our method improved the mean accuracies for three datasets. [ABSTRACT FROM AUTHOR]

Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in GMOD. Hence, the imputation of missing data is a prevalent issue during the pre-processing of GMOD. Although a large number of machine-learning methods have been applied to the field of meteorological missing value imputation and have achieved good results, they are usually aimed at specific meteorological elements, and few studies discuss imputation when multiple elements are randomly missing in the dataset. This paper designed a machine-learning-based multidimensional meteorological data imputation framework (MMDIF), which can use the predictions of machine-learning methods to impute the GMOD with random missing values in multiple attributes, and tested the effectiveness of 20 machine-learning methods on imputing missing values within 124 meteorological stations across six different climatic regions based on the MMDIF. The results show that MMDIF-RF was the most effective missing value imputation method; it is better than other methods for imputing 11 types of hourly meteorological elements. Although this paper applied MMDIF to the imputation of missing values in meteorological data, the method can also provide guidance for dataset reconstruction in other industries. [ABSTRACT FROM AUTHOR]