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This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects. [ABSTRACT FROM AUTHOR]

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production costs by 4.2% and makespan by 6.8% compared with manual scheduling. The hybrid VNS-NSGA-II model also shows better performance than NSGA-II, both in production cost and makespan. Hybrid algorithms are a good solution for multi-objective optimization issues in flexible flow-shop production scheduling. [ABSTRACT FROM AUTHOR]

In order to improve the effect of intelligent automatic test paper composition, this paper combines the hybrid fuzzy clustering algorithm to study the computer automatic test paper composition algorithm. In this paper, a computer automatic test paper composition system based on hybrid fuzzy clustering algorithm is constructed. Moreover, the hybrid fuzzy clustering method used in this paper is used as the basic algorithm of the system, and the algorithm is improved according to the actual needs of intelligent paper composition. In addition, this paper uses an intelligent algorithm to input the relevant constraint parameters and combines the original parameters to select the most suitable test questions from the database and combine them into test papers. Finally, this paper constructs the system structure based on the requirements of intelligent test paper composition. The experimental research shows that the computer automatic test paper composition system based on the hybrid fuzzy clustering algorithm proposed in this paper has a good test paper composition function, which can effectively promote the progress of the intelligent examination mode in colleges and universities. [ABSTRACT FROM AUTHOR]

This special issue contains six revised and extended versions of tool papers that appeared in the proceedings of TACAS 2021, the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. The issue is dedicated to the realization of algorithms in tools and the studies of the application of these tools for analysing hard- and software systems. [ABSTRACT FROM AUTHOR]

Compared to the conventional boost power factor correction (PFC) converter, a totem-pole bridgeless PFC has high efficiency because it does not have an input diode rectifier stage, but a current spike may occur when the polarity of the grid voltage changes. This paper proposes a digital control algorithm for bridgeless totem-pole PFC with a simple control structure based on the phase angle of grid voltage. The proposed algorithm has a PI-based double-loop control structure and performs DC-link voltage and input inductor current control. Rectifying switches operate based on the proposed rectification algorithm using phase angle information calculated through a single-phase phase-locked loop (PLL) to prevent current spikes. The feed-forward duty ratio value is calculated according to the polarity of the grid voltage and added to the double-loop controller to perform appropriate power factor control. The performance and feasibility of the proposed control algorithm are verified through a 3 kW hardware prototype. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To develop an automated algorithm to detect, quantify, and differentiate between tremor using pen-on-paper spirals. METHODS: Patients with essential tremor (n = 25), dystonic tremor (n = 25), Parkinson’s disease (n = 25), and healthy volunteers (HV, n = 25) drew free-hand spirals. The algorithm derived the mean deviation (MD) and tremor variability from scanned images. MD and tremor variability were compared with 1) the Bain and Findley scale, 2) the Fahn–Tolosa–Marin tremor rating scale (FTM–TRS), and 3) the peak power and total power of the accelerometer spectra. Inter and intra loop widths were computed to differentiate between the tremor. RESULTS: MD was higher in the tremor group (48.9±26.3) than in HV (26.4±5.3; p < 0.001). The cut-off value of 30.3 had 80.9% sensitivity and 76.0% specificity for the detection of the tremor [area under the curve: 0.83; 95% confidence index (CI): 0.75, 0.91, p < 0.001]. MD correlated with the Bain and Findley ratings (rho = 0.491, p = 0 < 0.001), FTM–TRS part B (rho = 0.260, p = 0.032) and accelerometric measures of postural tremor (total power, rho = 0.366, p < 0.001; peak power, rho = 0.402, p < 0.001). Minimum Detectable Change was 19.9%. Inter loop width distinguished Parkinson’s disease spirals from dystonic tremor (p < 0.001, 95% CI: 54.6, 211.1), essential tremor (p = 0.003, 95% CI: 28.5, 184.9), or HV (p = 0.036, 95% CI: -160.4, -3.9). CONCLUSION: The automated analysis of pen-on-paper spirals generated robust variables to quantify the tremor and putative variables to distinguish them from each other. SIGNIFICANCE: This technique maybe useful for epidemiological surveys and follow-up studies on tremor. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) signal describes the heart's electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly, all medical applications can be easily implemented with an engineering approach if the ECGs are treated as signals; secondly, paper ECGs can deteriorate over time, therefore a correct evaluation of the patient's clinical evolution is not always guaranteed. The goal of this paper is the realization of an automatic conversion algorithm from paper-based ECGs (images) to digital ECG signals. The algorithm involves a digitization process tested on an image set of 16 subjects, also with pathologies. The quantitative analysis of the digitization method is carried out by evaluating the repeatability and reproducibility of the algorithm. The digitization accuracy is evaluated both on the entire signal and on six ECG time parameters (R-R peak distance, QRS complex duration, QT interval, PQ interval, P-wave duration, and heart rate). Results demonstrate the algorithm efficiency has an average Pearson correlation coefficient of 0.94 and measurement errors of the ECG time parameters are always less than 1 mm. Due to the promising experimental results, the algorithm could be embedded into a graphical interface, becoming a measurement and collection tool for cardiologists. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal AIMS Mathematics on the topic of discrete optimization. The issue includes 21 papers covering a range of subjects, including molecular trees, network systems, variational inequality problems, scheduling, image restoration, spectral clustering, integral equations, convex functions, graph products, optimization algorithms, air quality prediction, humanitarian planning, inertial methods, neural networks, transportation problems, emotion identification, fixed-point problems, structural engineering design, single machine scheduling, and ensemble learning. The papers present new theoretical results, algorithms, and applications in these areas. The guest editor expresses gratitude to the journal staff and reviewers and hopes that readers will find inspiration for their own research. [Extracted from the article]

The authors offer observation on utilizing tables, figures, charts and graphs to help understand the research presented in a simple manner but also engage and sustain the reader's interest. Topics discussed include benefits provided by the use of tables/figures/charts/graphs, general methodology of design and submission, and copyright issues of using material from government publications/public domain.

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. [ABSTRACT FROM AUTHOR]

This paper explores digital marginalization, data marginalization, and algorithmic exclusions in the Souths. To this effect, it argues that underrepresented users and communities continue to be marginalized and excluded by digital technologies, by big data, and by algorithms employed by organizations, corporations, institutions, and governments in various data jurisdictions. Situating data colonialism within the Souths, the paper contends that data ableism, data disablism, and data colonialism are at play when data collected, collated, captured, configured, and processed from underrepresented users and communities is utilized by mega entities for their own multiple purposes. It also maintains that data coloniality, as opposed to data colonialism, is impervious to legal and legislative interventions within data jurisdictions. Additionally, it discusses digital citizenship (DC) and its related emerging regimes. Moreover, the paper argues that digital exclusion transcends the simplistic haves versus the have nots dualism as it manifests itself in multiple layers and in multiple dimensions. Furthermore, it characterizes how algorithmic exclusions tend to perpetuate historical human biases despite the pervasive view that algorithms are autonomous, neutral, rational, objective, fair, unbiased, and non-human. Finally, the paper advances a critical southern decolonial (CSD) approach to datafication, algorithms, and digital citizenship by means of which data coloniality, algorithmic coloniality, and the coloniality embodied in DC have to be critiqued, challenged, and dismantled. [ABSTRACT FROM AUTHOR]

The intrinsic features of documents, such as paper color, texture, aging, translucency, the kind of printing, typing or handwriting, etc., are important with regard to how to process and enhance their image. Image binarization is the process of producing a monochromatic image having its color version as input. It is a key step in the document processing pipeline. The recent Quality-Time Binarization Competitions for documents have shown that no binarization algorithm is good for any kind of document image. This paper uses a sample of the texture of the scanned historical documents as the main document feature to select which of the 63 widely used algorithms, using five different versions of the input images, totaling 315 document image-binarization schemes, provides a reasonable quality-time trade-off. [ABSTRACT FROM AUTHOR]

The present paper is in two folds. In the first fold, we prove the Lipschitz continuity of the proximal mapping associated with a general strongly H-monotone mapping and compute an estimate of its Lipschitz constant under some mild assumptions imposed on the mapping H involved in the proximal mapping. We provide two examples to show that a maximal monotone mapping need not be a general H-monotone for a single-valued mapping H from a Banach space to its dual space. A class of multi-valued nonlinear variational inclusion problems is considered, and by using the notion of proximal mapping and Nadler's technique, an iterative algorithm with mixed errors is suggested to compute its solutions. Under some appropriate hypotheses imposed on the mappings and parameters involved in the multi-valued nonlinear variational inclusion problem, the strong convergence of the sequences generated by the proposed algorithm to a solution of the aforesaid problem is verified. The second fold of this paper investigates and analyzes the notion of Cn-monotone mappings defined and studied in [S.Z. Nazemi, A new class of monotone mappings and a new class of variational inclusions in Banach spaces, J. Optim. Theory Appl. 155(3)(2012) 785-795]. Several comments related to the results and algorithm appeared in the above mentioned paper are given. [ABSTRACT FROM AUTHOR]

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical. [ABSTRACT FROM AUTHOR]

Smart communication has significantly advanced with the integration of the Internet of Things (IoT). Many devices and online services are utilized in the network system to cope with data gathering and forwarding. Recently, many traffic-aware solutions have explored autonomous systems to attain the intelligent routing and flowing of internet traffic with the support of artificial intelligence. However, the inefficient usage of nodes' batteries and long-range communication degrades the connectivity time for the deployed sensors with the end devices. Moreover, trustworthy route identification is another significant research challenge for formulating a smart system. Therefore, this paper presents a smart Random walk Distributed Secured Edge algorithm (RDSE), using a multi-regression model for IoT networks, which aims to enhance the stability of the chosen IoT network with the support of an optimal system. In addition, by using secured computing, the proposed architecture increases the trustworthiness of smart devices with the least node complexity. The proposed algorithm differs from other works in terms of the following factors. Firstly, it uses the random walk to form the initial routes with certain probabilities, and later, by exploring a multi-variant function, it attains long-lasting communication with a high degree of network stability. This helps to improve the optimization criteria for the nodes' communication, and efficiently utilizes energy with the combination of mobile edges. Secondly, the trusted factors successfully identify the normal nodes even when the system is compromised. Therefore, the proposed algorithm reduces data risks and offers a more reliable and private system. In addition, the simulations-based testing reveals the significant performance of the proposed algorithm in comparison to the existing work. [ABSTRACT FROM AUTHOR]

The paper "Well-behaved Online Load Balancing Against Strategic Jobs" by Li, Li and Wu considers a truthful online load-balancing problem with the objective of the makespan minimization on related machines. The 2019 workshop on models and algorithms for planning and scheduling problems was held in Renesse (The Netherlands). [Extracted from the article]

The traditional method for transformer moisture diagnosis is to establish empirical equations between feature parameters extracted from frequency domain spectroscopy (FDS) and the transformer’s moisture content. However, the established empirical equation may not be applicable to a novel testing environment, resulting in an unreliable evaluation result. In this regard, it is acknowledged that FDS combined with machine learning is more suitable for estimating moisture content in a variety of test environments. Nonetheless, the accuracy of the estimation results obtained using the existing method is limited by the algorithm’s inability to generalize. To address this issue, we propose an AdaBoost algorithm-enhanced back-propagation neural network (BP_AdaBoost). This study creates a database by extracting feature parameters from the FDS that characterize the insulation states of the prepared samples. Then, using the BP_AdaBoost algorithm and the newly constructed database, the moisture estimation models are trained. Finally, the results of the estimation are discussed in terms of laboratory and field transformers. By comparing the proposed BP_AdaBoost algorithm to other intelligence algorithms, it is demonstrated that it not only performs better in generalization, but also maintains a high level of accuracy. [ABSTRACT FROM AUTHOR]

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing. [ABSTRACT FROM AUTHOR]

The Parker-Oldenburg method, as a classical frequency-domain algorithm, has been widely used in Moho topographic inversion. The method has two indispensable hyperparameters, which are the Moho density contrast and the average Moho depth. Accurate hyperparameters are important prerequisites for inversion of fine Moho topography. However, limited by the nonlinear terms, the hyperparameters estimated by previous methods have obvious deviations. For this reason, this paper proposes a new method to improve the existing ParkerOldenburg method by taking advantage of the invasive weed optimization algorithm in estimating hyperparameters. The synthetic test results of the new method show that, compared with the trial and error method and the linear regression method, the new method estimates the hyperparameters more accurately, and the computational efficiency performs excellently, which lays the foundation for the inversion of more accurate Moho topography. In practice, the method is applied to the Moho topographic inversion in the South China Sea. With the constraints of available seismic data, the crust-mantle density contrast and the average Moho depth in the South China Sea are determined to be 0.535 g/cm3 and 21.63 km, respectively, and the Moho topography of the South China Sea is inverted based on this. The results of the Moho topography show that the Moho depth in the study area ranges from 5.7 km to 32.3 km, with more obvious undulations. Among them, the shallowest part of the Moho topography is mainly located in the southern part of the Southwestern sub-basin and the southern part of the Manila Trench, with a depth of about 6 km. Compared with the CRUST 1.0 model and the model calculated by the improved Bott's method, the RMS between the Moho model and the seismic point difference in this paper is smaller, which proves that the method in this paper has some advantages in Moho topographic inversion. [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]

Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The folded paper-size illusion is as easy to demonstrate as it is powerful in generating insights into perceptual processing: First take two A4 sheets of paper, one original sized, another halved by folding, then compare them in terms of area size by centering the halved sheet on the center of the original one! We perceive the larger sheet as far less than double (i.e., 100%) the size of the small one, typically only being about two thirds larger--this illusion is preserved by rotating the inner sheet and even by aligning it to one or two sides, but is dissolved by aligning both sheets to three sides, here documented by 88 participants' data. A potential explanation might be the general incapability of accurately comparing more than one geometrical dimension at once--in everyday life, we solve this perceptual-cognitive bottleneck by reducing the complexity of such a task via aligning parts with same lengths. [ABSTRACT FROM AUTHOR]

In order to improve the teaching effect of sports dance, this paper analyzes the traditional dance teaching motion capture, uses sensor motion perception algorithms to capture sports dance motion perception, and designs an intelligent sensor system that can be used for sports dance motion capture. Moreover, this paper combines the digital filter algorithm to design the hardware system structure of the sports dance motion capture system and builds a motion capture system for sports dance teaching based on the digital filter algorithm according to actual needs. In addition, this paper combines the simulation test to evaluate the performance of the system designed in this paper. The research results show that the motion capture system for sports dance teaching based on the digital filtering algorithm proposed in this paper can play an important role in sports dance teaching and effectively improve the efficiency of sports dance teaching. [ABSTRACT FROM AUTHOR]

At present, the monitoring of concrete cracks is still mainly carried out by engineering personnel using simple mechanical monitoring instruments. The human inspection will undoubtedly be interfered by the individual's psychological, physical, and external conditions, and there may also be unobjective emotions, so it is impossible to ensure that the quality of the detection is up to standard and accurate. This paper combines digital image processing technology and multiscale feature analysis automatic detection algorithm to construct an intelligent building structure crack detection system. Moreover, this paper proposes an enrichment scheme for the unknown partially entangled states of building microparticles and utilizes the entanglement exchange process based on the Raman interaction of two building microparticles. The experimental results show that the automatic detection method of building cracks based on digital image processing technology and multiscale feature analysis has a good effect. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device's data are secluded. The paper systematically reviewed the available literature using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guiding principle. The study presents a systematic review of appliable ML approaches for FL, reviews the categorization of FL, discusses the FL application areas, presents the relationship between FL and Blockchain Technology (BT), and discusses some existing literature that has used FL and ML approaches. The study also examined applicable machine learning models for federated learning. The inclusion measures were (i) published between 2017 and 2021, (ii) written in English, (iii) published in a peer-reviewed scientific journal, and (iv) Preprint published papers. Unpublished studies, thesis and dissertation studies, (ii) conference papers, (iii) not in English, and (iv) did not use artificial intelligence models and blockchain technology were all removed from the review. In total, 84 eligible papers were finally examined in this study. Finally, in recent years, the amount of research on ML using FL has increased. Accuracy equivalent to standard feature-based techniques has been attained, and ensembles of many algorithms may yield even better results. We discovered that the best results were obtained from the hybrid design of an ML ensemble employing expert features. However, some additional difficulties and issues need to be overcome, such as efficiency, complexity, and smaller datasets. In addition, novel FL applications should be investigated from the standpoint of the datasets and methodologies. [ABSTRACT FROM AUTHOR]

An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R-Track algorithm; 75% (n = 15) were qRT-PCR positive. The S-Facts Algorithm identified 7.75% (n = 11) positive oncology staff members, and 81.82% (n = 9) were qRT-PCR positive. Oncology patients (n = 369) were evaluated, and 1.36% (n = 5) were positive for the Algorithm used. The five patients (100%) were confirmed by qRT-PCR. (4) Conclusions: The proposed early detection tools have proved to be a low-cost and efficient tool in a country where qRT-PCR tests and vaccines are insufficient for the population. [ABSTRACT FROM AUTHOR]

Presents a study which showed how curve and surface representations of fiber network, fiber wall and fiber lumen can be computed using distance transform based algorithms. Motive for analyzing volume images of paper; Uses of the representations; Levels of detail in which the analysis of paper and paper fibers can be divided.

With the development of the times, figurative expressions no longer meet the creative needs of artists and the aesthetic demands of the people. In order to express art in a more profound way, the perfect use of abstract graphics plays a crucial role in the success of the work. In recent years, there has been a surge in the creation of digital art, but there is relatively little theoretical literature on the combination of abstract graphics as a visual language and digital art. In addition, research on the theoretical aspects of digital art design is also relatively weak, so it is essential to analyse the formal aesthetics and innovative applications. In fact, digital art design has a very important role to play in promoting the development of creative cultural industries. In other words, the healthy development of digital art design can influence the future prospects of a country's creative and cultural industries. Digital art design is an integrated and complex production process and labour outcome. In addition to its human, aesthetic, and social value, digital art also has an economic value. Digital art is a new art form that combines digital technology and artistic aesthetics. As such, digital art is characterised by high technology, diverse forms, popularised art, and the advantages of high communication, interactivity, and influence, which can provide more assistance for the innovation and application of abstract graphics. Digital art is multifaceted and has an artistic expression that cannot be matched by other forms of technology. Abstract graphics, driven by digital art, are full of novelty and interest and can greatly enrich people's emotions and senses. Abstract graphics bring the experience of digital art to its fullest potential. The combination of digital art and abstract graphics offers more innovation and possibilities for the development of art and will bring great prosperity to art communication. With the widespread use of computer and network technology, the Internet has developed rapidly. In this context, digital art, as art created in a digital way and concept, has gained widespread attention. As a result, how to integrate existing computer resources in the new environment to build a model of digital art design effectiveness will cause a direct influence on the quality of digital art design with digital content innovation as the core. At the same time, as digital art becomes more and more popular, the demand for digital talents becomes very urgent. As a result, the cultivation of high-quality digital talents has become a major concern for society. Therefore, in order to explore the success of digital art design and the cultivation of digital art talents, and to better serve the innovation of digital art, this paper proposes a digital art effectiveness model based on the K-medoids algorithm. This model can provide a deeper and more comprehensive understanding of digital art and abstract graphics and provide theoretical support for professional design creation. [ABSTRACT FROM AUTHOR]

Context: Software product lines (SPLs) have reached a considerable level of adoption in the software industry. The most commonly used models for managing the variability of SPLs are feature models (FMs). The analysis of FMs is an error-prone, tedious task, and it is not feasible to accomplish this task manually with large-scale FMs. In recent years, much effort has been devoted to developing reasoning algorithms for FMs. Aim: To synthesize the evidence on the use of reasoning algorithms for feature modeling. Method: We conducted a systematic mapping study, including six research questions. This study included 66 papers published from 2010 to 2020. Results: We found that most algorithms were used in the domain stage (70%). The most commonly used technologies were transformations (18%). As for the origins of the proposals, they were mainly rooted in academia (76%). The FODA model continued to be the most frequently used representation for feature modeling (70%). A large majority of the papers presented some empirical validation process (90%). Conclusion: We were able to respond to the RQs. The FODA model is consolidated as a reference within SPLs to manage variability. Responses to RQ2 and RQ6 require further review. [ABSTRACT FROM AUTHOR]

Achieving community immunity against the coronavirus disease 2019 (COVID-19) depends on vaccinating the largest number of people within a specific period while taking all precautionary measures. To address this problem, this paper presents a smart parking system that will help the health crisis management committee to vaccinate the largest number of people with the minimum period of time while ensuring that all precautionary measures are followed, through a set of algorithms. These algorithms seek to ensure a uniform distribution of persons in parking. This paper proposes a novel complex system for smart parking and nine algorithms to address the NP-hard problem. The experimental results demonstrate the performance of the proposed algorithms in terms of gap and time. Applying these algorithms to smart cities to ensure precautionary measures against COVID-19 can help fight against this pandemic. [ABSTRACT FROM AUTHOR]

In 2018, the position paper 'Imaging the adult with congenital heart disease: a multimodality imaging approach' was published. The paper highlights, in the first part, the different imaging modalities applied in adult congenital heart disease patients. In the second part, these modalities are discussed more detailed for moderate to complex anatomical defects. Because of the length of the paper, simple lesions were not touched on. However, imaging modalities to use for simple shunt lesions are still poorly known. One is looking for structured recommendations on which they can rely when dealing with an (undiscovered) shunt lesion. This information is lacking for the initial diagnostic process, during repair and at follow-up. Therefore, this paper will focus on atrial septal defect, ventricular septal defect, and persistent arterial duct. Pre-, intra-, and post-procedural imaging techniques will be systematically discussed. This position paper will offer algorithms that might help at a glance. The document is prepared for general cardiologists, trainees, medical students, imagers/technicians to select the most appropriate imaging modality and to detect the requested information for each specific lesion. It might serve as reference to which researchers could refer when setting up a (imaging) study. [ABSTRACT FROM AUTHOR]

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments. [ABSTRACT FROM AUTHOR]

Learning programming logic remains an obstacle for students from different academic fields. Considered one of the essential disciplines in the field of Science and Technology, it is vital to investigate the new tools or techniques used in the teaching and learning of Programming Language. This work presents a systematic literature review (SLR) on approaches using Mobile Learning methodology and the process of learning programming in introductory courses, including mobile applications and their evaluation and validation. We consulted three digital libraries, considering articles published from 2011 to 2022 related to Mobile Learning and Programming Learning. As a result, we found twelve mobile tools for learning or teaching programming logic. Most are free and used in universities. In addition, these tools positively affect the learning process, engagement, motivation, and retention, providing a better understanding, and improving content transmission. [ABSTRACT FROM AUTHOR]

In order to improve the performance of UAV’s autonomous maneuvering decision-making, this paper proposes a decision-making method based on situational continuity. The algorithm in this paper designs a situation evaluation function with strong guidance, then trains the Long Short-Term Memory (LSTM) under the framework of Deep Q Network (DQN) for air combat maneuvering decision-making. Considering the continuity between adjacent situations, the method takes multiple consecutive situations as one input of the neural network. To reflect the difference between adjacent situations, the method takes the difference of situation evaluation value as the reward of reinforcement learning. In different scenarios, the algorithm proposed in this paper is compared with the algorithm based on the Fully Neural Network (FNN) and the algorithm based on statistical principles respectively. The results show that, compared with the FNN algorithm, the algorithm proposed in this paper is more accurate and forwardlooking. Compared with the algorithm based on the statistical principles, the decision-making of the algorithm proposed in this paper is more efficient and its real-time performance is better. [ABSTRACT FROM AUTHOR]

Rhetorical sentence classification is an interesting approach for making extractive summaries but this technique still needs to be developed because the performance of automatic rhetorical sentence classification is still poor. Rhetorical sentences are sentences that contain rhetorical words or phrases. Rhetorical sentences not only appear in the contents of a paper but also in the title. In this study, features related to section class and title class that have been proposed in a previous research were further developed. Our method uses different techniques to reach automatic section class extraction for which we introduce new, format-based features. Furthermore, we propose automatic rhetoric phrase extraction from the title. The corpus we used was a collection of technical-experimental scientific papers. Our method uses the Support Vector Machine (SVM) algorithm and the Naïve Bayesian algorithm for classification. The four categories used were: Problem, Method, Data, and Result. It was hypothesized that these features would be able to improve classification accuracy compared to previous methods. The F-measure for these categories reached up to 14%. [ABSTRACT FROM AUTHOR]

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by small objects in remote sensing images, such as low resolution, complex backgrounds, and severe occlusions, this paper proposes a lightweight improved model based on YOLOv8n. During the detection of small objects, the feature fusion part of the YOLOv8n algorithm retrieves relatively fewer features of small objects from the backbone network compared to large objects, resulting in low detection accuracy for small objects. To address this issue, firstly, this paper adds a dedicated small object detection layer in the feature fusion network to better integrate the features of small objects into the feature fusion part of the model. Secondly, the SSFF module is introduced to facilitate multi-scale feature fusion, enabling the model to capture more gradient paths and further improve accuracy while reducing model parameters. Finally, the HPANet structure is proposed, replacing the Path Aggregation Network with HPANet. Compared to the original YOLOv8n algorithm, the recognition accuracy of mAP@0.5 on the VisDrone data set and the AI-TOD data set has increased by 14.3% and 17.9%, respectively, while the recognition accuracy of mAP@0.5:0.95 has increased by 17.1% and 19.8%, respectively. The proposed method reduces the parameter count by 33% and the model size by 31.7% compared to the original model. Experimental results demonstrate that the proposed method can quickly and accurately identify small objects in complex backgrounds. [ABSTRACT FROM AUTHOR]

To overcome the problem of the high initial position of the point cloud required by the traditional Iterative Closest Point (ICP) algorithm, in this paper, we propose a point cloud registration method based on normal vector and directional histogram features (SHOT). Firstly, a hybrid filtering method based on the voxel idea is proposed and verified using the measured point cloud data, and the noise removal rates of 97.5%, 97.8%, and 93.8% are obtained. Secondly, in terms of feature point extraction, the original algorithm is optimized, and the optimized algorithm can better extract the missing part of the point cloud. Finally, a fine alignment method based on normal vector and directional histogram features (SHOT) is proposed, and the improved algorithm is compared with the existing algorithm. Taking the Stanford University point cloud data and the self-measured point cloud data as examples, the plotted iteration-error plots can be concluded that the improved method can reduce the number of iterations by 40.23% and 37.62%, respectively. [ABSTRACT FROM AUTHOR]

With the surge in astronomical data volume, modern astronomical research faces significant challenges in data storage, processing, and access. The I/O bottleneck issue in astronomical data processing is particularly prominent, limiting the efficiency of data processing. To address this issue, this paper proposes a tiered storage algorithm based on the access characteristics of astronomical data. The C4.5 decision tree algorithm is employed as the foundation to implement an astronomical data access correlation algorithm. Additionally, a data copy migration strategy is designed based on tiered storage technology to achieve efficient data access. Preprocessing tests were conducted on 418GB NSRT (Nanshan Radio Telescope) formaldehyde spectral line data, showcasing that tiered storage can potentially reduce data processing time by up to 38.15%. Similarly, utilizing 802.2 GB data from FAST (Five- hundred-meter Aperture Spherical radio Telescope) observations for pulsar search data processing tests, the tiered storage approach demonstrated a maximum reduction of 29.00% in data processing time. In concurrent testing of data processing workflows, the proposed astronomical data heat correlation algorithm in this paper achieved an average reduction of 17.78% in data processing time compared to centralized storage. Furthermore, in comparison to traditional heat algorithms, it reduced data processing time by 5.15%. The effectiveness of the proposed algorithm is positively correlated with the associativity between the algorithm and the processed data. The tiered storage algorithm based on the characteristics of astronomical data proposed in this paper is poised to provide algorithmic references for large-scale data processing in the field of astronomy in the future. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are revolutionising diverse computer vision use case domains, from public safety surveillance to Search and Rescue (SAR), and other emergency management and disaster relief operations. The growing need for accurate face verification algorithms has prompted an exploration of synergies between UAVs and face verification. This promises cost-effective, wide-area, non-intrusive person verification. Real-world human-centric use cases such as a ”Drone Guard Angel” for vulnerable people can contribute to public safety management and offload significant police resources. These scenarios demand efficient face verification to distinguish correctly the end users for authentication, authorisation and customised services. This paper investigates the suitability of existing solutions, and analyses five state-of-the-art candidate face verification algorithms. Informed by the advantages and disadvantages of existing solutions, the paper proposes an extended dataset and a refined face verification pipeline. Subsequently, it conducts empirical evaluation of these algorithms using the proposed pipeline and dataset in terms of inference times and the distribution of the similarity indexes. Furthermore, this paper provides essential guidance for algorithm selection and deployment in UAV-based applications. Two candidate algorithms, ArcFace and FaceNet512, have emerged as the top performers. The choice between them will depend on the specific use case requirements. [ABSTRACT FROM AUTHOR]

To achieve effective and accurate segmentation of photovoltaic panels in various working contexts, this paper proposes a comprehensive image segmentation strategy that integrates an improved Meanshift algorithm and an adaptive Shi-Tomasi algorithm. This approach effectively addresses the challenge of low precision in segmenting target regions and boundary contours in routine photovoltaic panel inspection. Firstly, based on the image information of photovoltaic panels collected under different environments by cameras, an improved Meanshift algorithm based on platform histogram optimization is used for preliminary processing, and images containing target information are cut out; then, the adaptive Shi-Tomasi algorithm is used to extract and screen feature points from the target area; finally, the extracted feature points generate the segmentation contour of the target photovoltaic panel, achieving accurate segmentation of the target area and boundary contour of the photovoltaic panel. Experiments verified that in photovoltaic panel images under different background environments, the method proposed in this paper enhances the accuracy of segmenting the target area and boundary contour of photovoltaic panels. [ABSTRACT FROM AUTHOR]

When facing problems in the aerial pursuit game, most of the current unmanned aerial vehicles (UAVs) have good maneuverability performance, but it is difficult to utilize the overload maneuverability of UAVs properly; further, UAVs tend to be more costly, and it is often difficult to effectively prevent the enemy from reaching the tailgating position behind the UAV in the aerial pursuit game. Therefore, there is a pressing need for a maneuvering algorithm that can effectively allow a UAV to quickly protect itself in a disadvantageous position, stably and effectively select a maneuver with the maneuvering algorithm, and stably and effectively establish an advantage by moving to an advantageous position. Therefore, this paper establishes a cloud model-based UAV-maneuvering aerial pursuit decision-making model based on pursuit-and-evasion game positions. Based on the evaluation of the latter, when the UAV is at a disadvantage, we use the constructed defensive maneuver expert pool to abandon the disadvantageous position. When the UAV is at an advantage, we use cloud model-based pursuit-and-evasion game maneuvering decision making to establish an advantageous position. According to the results of the simulation examples, the maneuvering decision-making method designed in this paper confirms that the UAV can quickly abandon its position and establish an advantage in case of parity or disadvantage and that it can also stably establish a tail-chasing position in case of advantage. [ABSTRACT FROM AUTHOR]

This special issue of Software Tools for Technology Transfer comprises extended versions of selected papers from the 26th edition of the International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2020). The focus of this conference series is tools and algorithms for the rigorous analysis of software and hardware systems, and the papers in this special cover the spectrum of current work in this field. [ABSTRACT FROM AUTHOR]