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Abstract A polarity identification is very important for operation of transformers, measurement and protection equipment, where it is useful in analyzing of transformer connections and operation as well as testing of protective systems. Moreover, it’s essential in assessment of power systems performance during both normal and abnormal operation. Ensuring the correct polarity of the primary and secondary windings in voltage and current transformers is of paramount importance for various measurement and protection schemes in power networks. This paper proposes a digital polarity detector and tester using correlation coefficients and nine polarity indices calculated for instrument transformer signals. In order to test the performance of the proposed polarity tester algorithm, MATLAB code is imported to the LABVIEW model, and the numerical data obtained from the synchronous generator terminals via instrument transformers are interfaced with the computer through the Data Acquisition Card (DAC). The experimental system consists of a motor-generator set supplying a three-phase inductive load with instrument transformers connected to measure each phase voltage and current. The obtained results for various operating conditions and different types of abnormal conditions prove that the suggested algorithm is accurate, reliable and applicable to smart grids and substation automation systems. It can be considered as an integrated system incorporated with digital fault recorders, relays and meters.

Abstract Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

Abstract Swarm systems consist of a large number of interacting individuals, which exhibit complex behavior despite having simple interaction rules. However, crafting individual motion policies that can manifest desired collective behaviors poses a significant challenge due to the intricate relationship between individual policies and swarm dynamics. This paper addresses this issue by proposing an imitation learning method, which derives individual policies from collective behavior data. The approach leverages an adversarial imitation learning framework, with a deep attention network serving as the individual policy network. Our method successfully imitates three distinct collective behaviors. Utilizing the ease of analysis provided by the deep attention network, we have verified that the individual policies underlying a certain collective behavior are not unique. Additionally, we have analyzed the different individual policies discovered. Lastly, we validate the applicability of the proposed method in designing policies for swarm robots through practical implementation on swarm robots.

Abstract Recently, a number of vision transformer models for semantic segmentation have been proposed, with the majority of these achieving impressive results. However, they lack the ability to exploit the intrinsic position and channel features of the image and are less capable of multi-scale feature fusion. This paper presents a semantic segmentation method that successfully combines attention and multiscale representation, thereby enhancing performance and efficiency. This represents a significant advancement in the field. Multi-layers semantic extraction and multi-scale aggregation transformer decoder (MEMAFormer) is proposed, which consists of two components: mix-layers dual channel semantic extraction module (MDCE) and semantic aggregation pyramid pooling module (SAPPM). The MDCE incorporates a multi-layers cross attention module (MCAM) and an efficient channel attention module (ECAM). In MCAM, horizontal connections between encoder and decoder stages are employed as feature queries for the attention module. The hierarchical feature maps derived from different encoder and decoder stages are integrated into key and value. To address long-term dependencies, ECAM selectively emphasizes interdependent channel feature maps by integrating relevant features across all channels. The adaptability of the feature maps is reduced by pyramid pooling, which reduces the amount of computation without compromising performance. SAPPM is comprised of several distinct pooled kernels that extract context with a deeper flow of information, forming a multi-scale feature by integrating various feature sizes. The MEMAFormer-B0 model demonstrates superior performance compared to SegFormer-B0, exhibiting gains of 4.8%, 4.0% and 3.5% on the ADE20K, Cityscapes and COCO-stuff datasets, respectively.

Abstract Text-based cross-modal vehicle retrieval has been widely applied in smart city contexts and other scenarios. The objective of this approach is to identify semantically relevant target vehicles in videos using text descriptions, thereby facilitating the analysis of vehicle spatio-temporal trajectories. Current methodologies predominantly employ a two-tower architecture, where single-granularity features from both visual and textual domains are extracted independently. However, due to the intricate semantic relationships between videos and text, aligning the two modalities effectively using single-granularity feature representation poses a challenge. To address this issue, we introduce a Multi-Granularity Representation Learning model, termed MGRL, tailored for text-based cross-modal vehicle retrieval. Specifically, the model parses information from the two modalities into three hierarchical levels of feature representation: coarse-granularity, medium-granularity, and fine-granularity. Subsequently, a feature adaptive fusion strategy is devised to automatically determine the optimal pooling mechanism. Finally, a multi-granularity contrastive learning approach is implemented to ensure comprehensive semantic coverage, ranging from coarse to fine levels. Experimental outcomes on public benchmarks show that our method achieves up to a 14.56% improvement in text-to-vehicle retrieval performance, as measured by the Mean Reciprocal Rank (MRR) metric, when compared against 10 state-of-the-art baselines and 6 ablation studies.

Abstract Applying an error-backpropagation algorithm to spiking neural networks frequently needs to employ fictive derivatives of spike functions (popularly referred to as surrogate gradients) because the spike function is considered non-differentiable. The non-differentiability comes into play given that the spike function is viewed as a numeric function, most popularly, the Heaviside step function of membrane potential. To get back to basics, the spike function is not a numeric but a Boolean function that outputs True or False upon the comparison of the current potential and threshold. In this regard, we propose a method to evaluate the gradient of spike function viewed as a Boolean function for fixed- and floating-point data formats. For both formats, the gradient is considerably similar to a delta function that peaks at the threshold for spiking, which justifies the approximation of the spike function to the Heaviside step function. Unfortunately, the error-backpropagation algorithm with this gradient function fails to outperform popularly employed surrogate gradients, which may arise from the narrow peak of the gradient function and consequent potential undershoot and overshoot around the spiking threshold with coarse timesteps. We provide theoretical grounds of this hypothesis.

Abstract Owing to the potential for fast deployment, containerization technology has been widely used in web applications based on microservice architecture. Online container placement aims to improve resource utilization and meet other service quality requirements of cloud data centers. Most current heuristic and hyper-heuristic methods for container placement rely on single allocation rules, which are inefficient in heterogeneous cluster scenarios. Moreover, some container placement tasks often have similar characteristics (e.g., resource request types and physical machine types), but traditional single-task optimization modeling cannot exploit potential common knowledge, resulting in repeated optimization during resource allocation. Therefore, a new multi-task genetic programming method is proposed to solve the online multi-objective container placement problem (MOCP-MTGP). This method considers selecting appropriate allocation rules according to the types of resource requests and cluster status. MOCP-MTGP can automatically generate multiple groups of allocation rules from historical workload patterns and different cluster states, and capture the similarities between all online tasks to guide the transfer of general knowledge during optimization. Comprehensive experiments show that the proposed algorithm can improve the resource utilization of clusters, reduce the number of physical machines, and effectively meet resource constraints and high availability requirements.

Abstract Stroke is the prevalent cerebrovascular disease characterized by significant incidence and disability rates. To enhance the early perceive and detection of potential stroke patients, the early stroke behavior detection based on improved Video Masked Autoencoders (VideoMAE) for potential patients (EPBR-PS) is proposed. The proposed method begins with novel time interval-based sampling strategy, capturing video frame sequences enriched with sparse motion features. On the basis of establishing the masking mechanism for adjacent frames and pixel blocks within these sequences, The EPBR-PS employes pipeline mask strategy to extract spatiotemporal features effectively. Then, the local convolution attention mechanism is designed to capture local dynamic feature information, and central to the EPBR-PS is the integration of local convolutional attention mechanism with VideoMAE's multi-head attention mechanism. This integration facilitates the simultaneous leveraging of global high-level semantics and local dynamic feature information. Dual attention mechanism-based method for the fusion of these global and local features is proposed. After that, the optimal parameters of EPBR-PS were determined through the experiment of learning rate and fusion weights of different features. On the NTU-ST dataset, comparative analysis with eight models demonstrated the superiority of EPBR-PS, evidenced by the average recognition accuracy of 89.61%, surpassing that 1.67% over the benchmark VideoMAE. On the HMDB51 dataset, EPBR-PS has Top1 of 71.31%, which is 0.73% higher than that of the VideoMAE, providing the viable behavior detection for perception early signs of potential stroke in the home environment. This code is available at https://github.com/wang-325/EPBR-PS/ .

Abstract Medical tests are crucial for treatment decision making. However, over-testing can often occur in any medical speciality or level of expertise. Since over-testing usually results in a financial burden for patients and is also a waste of medical resources, this naturally leads to the question: which medical test items (MTIs) are necessary and should be prioritized for the target patients? It is a nontrivial task to identify the right MTIs due to the diversified health status of patients and the complicated prerequisites of therapies. To this end, in this paper, we propose a data-driven approach to evaluate the priority which should be given to MTIs by modeling the relationships between MTIs and therapies. Specifically, we first develop a dual hierarchical topic model (DHTM), which views the adopted hierarchical therapies as labeled topics and the MTI reports, i.e., the set of hierarchical attribute-value pairs (AVPs), as documents. Then, with the therapy-AVP distribution and the partial MTI reports of the target patient, we can scope the candidate therapies, which are further utilized to evaluate the accumulated gain of MTIs to be tested. Moreover, the next MTI recommendation is conducted based on the gains. Finally, extensive experiments on real-world medical data validate the effectiveness of our approach, and some interesting observations are also provided.

Abstract Thanks to the strong representation capability of the pre-trained language models, supervised grammatical error correction has achieved promising performance. However, traditional model training depends significantly on the large scale of similar distributed samples. The model performance decreases sharply once the distributions of training and testing data are inconsistent. To address this issue, we propose an automatic sampling approach to effectively select high-quality samples from different corpora and filter out irrelevant or harmful ones. Concretely, we first provide a detailed analysis of error type and sentence length distributions on all datasets. Second, our corpus weighting approach is exploited to yield different weights for each sample automatically based on analysis results, thus emphasizing beneficial samples and ignoring the noisy ones. Finally, we enhance typical Seq2Seq and Seq2Edit grammatical error correction models with pre-trained language models and design a model ensemble algorithm for integrating the advantages of heterogeneous models and weighted samples. Experiments on the benchmark datasets demonstrate that the proper utilization of different corpora is extremely helpful in enhancing the accuracy of grammatical error correction. The detailed analysis gains more insights into the effect of different corpus weighting strategies.

Abstract This paper investigates the agile optical satellite scheduling problem, which aims to arrange an observation sequence and observation actions for observation tasks. Existing research mainly aims to maximize the number of completed tasks or the total priorities of the completed tasks but ignores the influence of the observation actions on the imaging quality. Besides, the conventional exact methods and heuristic methods can hardly obtain a high-quality solution in a short time due to the complicated constraints and considerable solution space of this problem. Thus, this paper proposes a two-stage scheduling framework with two-stage deep reinforcement learning to address this problem. First, the scheduling process is decomposed into a task sequencing stage and an observation scheduling stage, and a mathematical model with complex constraints and two-stage optimization objectives is established to describe the problem. Then, a pointer network with a local selection mechanism and a rough pruning mechanism is constructed as the sequencing network to generate an executable task sequence in the task sequencing stage. Next, a decomposition strategy decomposes the executable task sequence into multiple sub-sequences in the observation scheduling stage, and the observation scheduling process of these sub-sequences is modeled as a concatenated Markov decision process. A neural network is designed as the observation scheduling network to determine observation actions for the sequenced tasks, which is well trained by the soft actor-critic algorithm. Finally, extensive experiments show that the proposed method, along with the designed mechanisms and strategy, is superior to comparison algorithms in terms of solution quality, generalization performance, and computation efficiency.

Abstract Gendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital platforms into environments that are hostile and inhospitable to women. Despite the severity of this issue, efforts to persuade digital platforms to strengthen their protections against gendered disinformation are frequently ignored, highlighting the difficult task of countering online misogyny in the face of commercial interests. This growing concern underscores the need for effective measures to create safer online spaces, where respect and equality prevail, ensuring that women can participate fully and freely without the fear of harassment or discrimination. This study addresses the challenge of detecting misogynous content in bilingual (English and Italian) online communications. Utilizing FastText word embeddings and explainable artificial intelligence techniques, we introduce a model that enhances both the interpretability and accuracy in detecting misogynistic language. To conduct an in-depth analysis, we implemented a range of experiments encompassing classic machine learning methodologies and conventional deep learning approaches to the recent transformer-based models incorporating both language-specific and multilingual capabilities. This paper enhances the methodologies for detecting misogyny by incorporating incremental learning for cutting-edge datasets containing tweets and posts from different sources like Facebook, Twitter, and Reddit, with our proposed approach outperforming these datasets in metrics such as accuracy, F1-score, precision, and recall. This process involved refining hyperparameters, employing optimization techniques, and utilizing generative configurations. By implementing Local Interpretable Model-agnostic Explanations (LIME), we further elucidate the rationale behind the model’s predictions, enhancing understanding of its decision-making process.

Abstract Pseudo-random number generators (PRNGs) are deterministic algorithms that generate sequences of numbers approximating the properties of random numbers, which are widely utilized in various fields. In this paper, we present a Genetic Algorithm Optimized Generative Adversarial Network (hereinafter referred to as GAGAN), which is designed for the effective training of discrete generative adversarial networks. In situations where non-differentiable activation functions, such as the modulo operation, are employed and traditional gradient-based backpropagation methods are inapplicable, genetic algorithms are utilized to optimize the parameters of the generator network. Based on this framework, we propose a novel recursive PRNG. Given that a PRNG can be constructed from one-way functions and their associated hardcore predicates, our proposed generator consists of two neural networks that simulate these functions and serve as the state transition function and the output function, respectively. The proposed PRNG has been rigorously tested using stringent benchmarks such as the NIST Statistical Test Suite (SP800-22) and the Chinese standard for random number generation (GM/T 0005-2021). Additionally, it has demonstrated outstanding performance in terms of Hamming distance. The results indicate that the proposed GAN-based PRNG has achieved a high degree of randomness and is highly sensitive to variations in the input.

Abstract Document-level relation extraction (RE), which requires integrating and reasoning information to identify multiple possible relations among entities. However, previous research typically performed reasoning on heterogeneous graphs and set a global threshold for multiple relations classification, regardless of interaction reasoning information among multiple relations and positive–negative samples imbalance on databases. This paper proposes a novel framework for Document-level RE with two techniques, dual attention fusion and dynamic asymmetric loss. Concretely, to obtain more interdependency feature learning, we construct entity pairs and contextual matrixes using multi-head axial attention and co-attention mechanism to learn the interaction among entity pairs deeply. To alleviate the hard-thresholds influence from positive–negative imbalance samples, we dynamically adjust weights to optimize the probabilities of different labels. We evaluate our model on two benchmark document-level RE datasets, DocRED and CDR. Experimental results show that our DASL (Dual Attention fusion and dynamic aSymmetric Loss) obtains superior performance on two public datasets, we further provide extensive experiments to analyze how dual attention fusion and dynamic asymmetric loss guide the model for better extracting multi-label relations among entities.

Abstract Traffic density is a crucial indicator for evaluating the level of service, as it directly reflects the degree of road congestion and driving comfort. However, accurately predicting real-time traffic density has been a significant challenge in Intelligent Transportation Systems (ITS) due to the nonlinear and spatial-temporal dynamic complexity of traffic density. In this paper, we propose a novel Model-enhanced Spatial-Temporal Attention Network (MSTAN), which constructs a spatial-temporal traffic kernel density model using the Kernel Density Estimation (KDE) method to process the spatiotemporal data and calculate the probabilities of various spatiotemporal events. These probabilities are input into the attention mechanism, enabling the model to recognize the inherent connection between dynamic and distant events. Through this fusion, the network can deeply learn and analyze the spatial-temporal properties of traffic features. Furthermore, this paper utilizes the attention mechanism to dynamically model spatial-temporal dependencies, capturing real-time traffic conditions and density, and constructs a spatial-temporal attention module for learning. To validate the performance of the proposed MSTAN model, experiments are conducted on two public datasets of California highways (PeMS04 and PeMS08). The experimental results demonstrate that the MSTAN model outperforms existing state-of-the-art baseline models in terms of prediction accuracy, thus proving the effectiveness of the model both theoretically and practically.

Abstract As a critical technology, anomaly detection ensures the smooth operation of cloud systems while maintaining the market competitiveness of cloud service providers. However, the resource data in real-world cloud systems is predominantly unannotated, leading to insufficient supervised signals for anomaly detection. Moreover, complicated topological associations existed between cloud servers (e.g., computation, storage, and communication). While acquiring resource information, correlating the system topology is challenging. To this end, we propose the GCAD for cloud system anomaly detection, which integrates data augmentation, GraphGRU, contrastive learning, and reconstruction. First, GCAD constructs positive and negative sample pairs through the masking and Gaussian noise data augmentation. Then, the GraphGRU processes extended temporal graph data, extracting and fusing spatiotemporal features from resource status and system topology. In addition, GCAD introduces linear attention for encoding spatiotemporal representations to capture their global correlation information. The weight parameters of the encoder are optimized using a contrastive learning mechanism. Finally, GCAD utilizes a reconstruction technique to calculate anomaly scores, facilitating the evaluation of the state of the cloud system at each time point. Experimental results indicate that GCAD outperforms state-of-the-art compared methods on two real-world datasets that contain topology information.

Abstract Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a challenge in building an efficient method to improve the accuracy of cancer-specific survival risk prediction, such as data noise problem. In order to solve the above problem, we propose a diversity reweighted deep survival neural network method with grid optimization (DRGONet) to improve the accuracy of cancer-specific survival risk prediction. Specifically, reweighting can be employed to adjust the weights assigned to each data point in the dataset based on their importance or relevance, thereby mitigating the impact of noisy or irrelevant data and improving model performance. Incorporating diversity into the goal of multiple learning models can help minimize bias and improve learning outcomes. Furthermore, hyperparameters can be optimized with grid optimization. Experimental results have demonstrated that our proposed approach has significant advantages (improved about 5%) in real-world medical scenarios, outperforming state-of-the-art comparison methods by a large margin. Our study highlights the significance of using DRGONet to overcome the limitations of building accurate survival prediction models. By implementing our technique in cancer research, we hope to reduce the suffering experienced by cancer patients and improve the effectiveness of treatment.

Abstract Influence Maximization (IM) stands as a central challenge within the domain of complex network analysis, with the primary objective of identifying an optimal seed set of a predetermined size that maximizes the reach of influence propagation. Over time, numerous methodologies have been proposed to address the IM problem. However, one certain network referred to as Imbalanced Heterogeneous Networks (IHN), which widely used in social situation, urban and rural areas, and merchandising, presents challenges in achieving high-quality solutions. In this work, we introduce the Lightweight Reinforcement Learning algorithm with Prior knowledge (LRLP), which leverages the Struc2Vec graph embedding technique that captures the structural similarity of nodes to generate vector representations for nodes within the network. In details, LRLP incorporates prior knowledge based on a group of centralities, into the initial experience pool, which accelerates the reinforcement learning training for better solutions. Additionally, the node embedding vectors are input into a Deep Q Network (DQN) to commence the lightweight training process. Experimental evaluations conducted on synthetic and real networks showcase the effectiveness of the LRLP algorithm. Notably, the improvement seems to be more pronounced when the the scale of the network is larger. We also analyze the effect of different graph embedding algorithms and prior knowledge on algorithmic results. Moreover, we conduct an analysis about some parameters, such as number of seed set selections T, embedding dimension d and network update frequency C. It is significant that the reduction of number of seed set selections T not only keeps the quality of solutions, but lowers the algorithm’s computational cost.

Abstract Given the anonymity and complexity of illegal transactions, traditional deep-learning methods struggle to establish correlations between transaction addresses, cash flows, and physical users. Additionally, the limited number of labels for illegal transactions results in severe class imbalance and other challenges. To overcome these limitations, we propose a reinforcement learning-enhanced, multi-relational, attention graph-aware framework to detect anti-money laundering and illegal trading activities. On the one hand, a data-driven, graph-aware layer establishes long-term dependencies and correlations between transaction graph nodes. Similarity among graph nodes divides the topological graph into three subgraphs. Learning from these subgraphs and converging nodes enriches local, global, and contextual details. Simultaneously, using repeated nodes across the subgraphs enhances interactivity between them, reduces intra-class ambiguity, and accentuates inter-class differences. On the other hand, a reinforcement learning module embedded in the graph-aware layer compensates for the missing details in node features caused by masking operations. Furthermore, the reconstructed loss function addresses significant classification inaccuracies by reducing the weight assigned to easily classified samples. Balancing these issues and individually supervising each component enables the detection framework to achieve optimal performance. The evaluation results demonstrate that our proposed model exhibits optimal detection performance and robustness, such as F1 of 93.85% and 94.39%.

Abstract Zero-shot relation extraction (ZSRE) is essential for improving the understanding of natural language relations and enhancing the accuracy and efficiency of natural language processing methods in practical applications. However, the existing ZSRE models ignore the importance of semantic information fusion and possess limitations when used for zero-shot relation extraction tasks. Thus, this paper proposes a dual contrastive learning framework and a cross-attention network module for ZSRE. First, our model designs a dual contrastive learning framework to compare the input sentences and relation descriptions from different perspectives; this process aims to achieve better separation between different relation categories in the representation space. Moreover, the cross-attention network of our model is introduced from the computer vision field to enhance the attention paid by the input instance to the relevant information of the relation description. The experimental results obtained on the Wiki-ZSL and FewRel datasets fully demonstrate the effectiveness of our approach.

Abstract The field of social network analysis has identified User Alignment (UA) as a crucial area of investigation. The objective of UA is to identify and connect user accounts across diverse social networks, even when there are no explicit interconnections. UA plays a pivotal role in synthesising coherent user profiles and delving into the intricacies of user behaviour across platforms. However, traditional approaches have encountered limitations. Singular embedding techniques have been found to fall short in fully capturing the semantic essence of user profile attributes. Furthermore, classification-based embedding methods lack definitive criteria for categorisation, thereby constraining both the efficacy and applicability of these models. This paper presents a novel unsupervised Gradient Semantic Model for User Alignment (GSMUA) for the purpose of identifying common user identities across social networks. GSMUA categorises user profile information into weak, sub, and strong gradients based on the semantic intensity of attributes. Different gradient semantic levels direct attention to literal features, semantic features, or a combination of both during feature extraction, thereby achieving a full semantic representation of user attributes. In the case of strongly semantic long texts, GSMUA employs Named Entity Recognition (ENR) technology in order to enhance the inefficient handling of such texts. Furthermore, GSMUA compensates for missing user profile attributes by utilising profile information from user neighbours, thereby reducing the negative impact of missing user profile attributes on model performance. Extensive experiments conducted on four pairs of real datasets demonstrate the superiority of our approach. In comparison to the most effective previously developed unsupervised methods, GSMUA demonstrates improvements in hit-precision ranging from 5.32 to 12.17%. When compared to supervised methods, the improvements range from 0.71 to 11.79%.

Abstract The rapid development of drone technology has made drones one of the essential tools for acquiring aerial information. The detection and localization of text information through drones greatly enhance their understanding of the environment, enabling tasks of significant importance such as community commercial planning and autonomous navigation in intelligent environments. However, the unique perspective and complex environment during drone photography lead to various challenges in text detection, including diverse text shapes, large-scale variations, and background interference, making traditional methods inadequate. To address this issue, we propose a drone-based text detection method based on boundary adaptation. We first conduct an in-depth analysis of text characteristics from a drone’s perspective. Using ResNet50 as the backbone network, we introduce the proposed Hybrid Text Attention Mechanism into the backbone network to enhance the perception of text regions in the feature extraction module. Additionally, we propose a Spatial Feature Fusion Module to adaptively fuse text features of different scales, thereby enhancing the model’s adaptability. Furthermore, we introduce a text detail transformer by incorporating a local feature extractor into the transformer of the text detail boundary iteration optimization module. This enables the precise optimization and localization of text boundaries by reducing the interference of complex backgrounds, eliminating the need for complex post-processing. Extensive experiments on challenging text detection datasets and drone-based text detection datasets validate the high robustness and state-of-the-art performance of our proposed method, laying a solid foundation for practical applications.

Abstract Medical image segmentation constitutes a crucial step in the analysis of medical images, possessing extensive applications and research significance within the realm of medical research and practice. Convolutional neural network achieved great success in medical image segmentation. However, acquiring large labeled datasets remains unattainable due to the substantial expertise and time required for image labeling, as well as heightened patient privacy concerns. To solve scarce medical image data, we propose a powerful network Domain Tuning SAM for Medical images (DT-SAM). We construct an encoder utilizing a parameter-effective fine-tuning strategy and SAM. This strategy selectively updates a small fraction of the weight increments while preserving the majority of the pre-training weights in the SAM encoder, consequently reducing the required number of training samples. Meanwhile, our approach leverages only SAM encoder structure while incorporating a decoder similar to U-Net decoder structure and redesigning skip connections to concatenate encoder-extracted features, which effectively decode the features extracted by the encoder and preserve edge information. We have conducted comprehensive experiments on three publicly available medical image segmentation datasets. The combined experimental results show that our method can effectively perform few shot medical image segmentation. With just one labeled data, achieving a Dice score of 63.51%, a HD of 17.94 and an IoU score of 73.55% on Heart Task, on Prostate Task, an average Dice score of 46.01%, a HD of 10.25 and an IoU score of 65.92% were achieved, and the Dice, HD, and IoU score reaching 88.67%, 10.63, and 90.19% on BUSI. Remarkably, with few training samples, our method consistently outperforms various based on SAM and CNN.

Abstract Offline reinforcement learning (RL) has been widely used in safety-critical domains by avoiding dangerous and costly online interaction. A significant challenge is addressing uncertainties and risks outside of offline data. Risk-sensitive offline RL attempts to solve this issue by risk aversion. However, current model-based approaches only extract state transition information and reward information using dynamics models, which cannot capture risk information implicit in offline data and may result in the misuse of high-risk data. In this work, we propose a model-based offline policy optimization approach with a risk dynamics model (MOOR). Specifically, we construct a risk dynamics model using a quantile network that can learn the risk information of data, then we reshape model-generated data based on errors of the risk dynamics model and the risk information of data. Finally, we use a risk-averse algorithm to learn the policy on the combined dataset of offline and generated data. We theoretically prove that MOOR can identify risk information of data and avoid utilizing high-risk data, our experiments show that MOOR outperforms existing approaches and achieves state-of-the-art results in risk-sensitive D4RL and risky navigation tasks.

Abstract Recently, nonrevisiting genetic algorithms have demonstrated superior capabilities compared with classic genetic algorithms and other single-objective evolutionary algorithms. However, the search efficiency of nonrevisiting genetic algorithms is currently low for some complex optimisation problems. This study proposes a nonrevisiting genetic algorithm with a multi-region guided search to improve the search efficiency. The search history is stored in a binary space partition (BSP) tree, where each searched solution is assigned to a leaf node and corresponds to a search region in the search space. To fully exploit the search history, several optimal solutions in the BSP tree are archived to represent the most potential search regions and estimate the fitness landscape in the search space. Except for the conventional genetic operations, the offspring can also be generated through multi-region guided search strategy, where the current solution is first navigated to one of the candidate search regions and is further updated towards the direction of the optimal solution in the search history to speedup convergence. Thus, multi-region guided search can reduce the possibility of getting trapped in local optima when solving problems with complex landscapes. The experimental results on different types of test suites reveal the competitiveness of the proposed algorithm in comparison with several state-of-the-art methods.

Abstract Accurate forecasting of traffic flow in the future period is very important for planning traffic routes and alleviating traffic congestion. However, traffic flow forecasting still faces serious challenges. Most of the existing traffic flow forecasting methods are static graph convolutional networks based on prior knowledge, ignoring the special spatial–temporal dynamics of spatial–temporal data. Using only adaptive dynamic graphs completely discards the objective and real spatial connectivity information in static graphs. To this end, we propose a novel information enhancement and dynamic-static fusion attention network (IEDSFAN). Firstly, the Multi-Graph Fusion Gating mechanism (MGFG) designed in IEDSFAN effectively fuses dynamic and static graphs to dynamically capture the hidden spatial–temporal correlation. Secondly, we construct a novel Gated Multi-head Self-Attention (GMHSA), which maps the input through the MGFG module to capture the complex spatial–temporal interactions in the features. Finally, we generate adaptive parameters to solve the problem that shared parameters cannot learn multiple traffic patterns, and enhance the expression of sequence information through the peak flag module. We conducted extensive experiments on five real-world traffic datasets, and the experimental results show that the performance of IEDSFAN is significantly better than all baselines.

Abstract By recognizing students’ facial expressions in actual classroom situations, the students’ emotional states can be quickly uncovered, which can help teachers grasp the students’ learning rate, which allows teachers to adjust their teaching strategies and methods, thus improving the quality and effectiveness of classroom teaching. However, most previous facial expression recognition methods have problems such as missing key facial features and imbalanced class distributions in the dateset, resulting in low recognition accuracy. To address these challenges, this paper proposes LCANet, a model founded on a fused attention mechanism and a joint loss function, which allows the recognition of students’ emotions in real classroom scenarios. The model uses ConvNeXt V2 as the backbone network to optimize the global feature extraction capability of the model, and at the same time, it enables the model to pay closer attention to the key regions in facial expressions. We incorporate an improved Channel Spatial Attention (CSA) module as a way to extract more local feature information. Furthermore, to mitigate the class distribution imbalance problem in the facial expression dataset, we introduce a joint loss function. The experimental results show that our LCANet model has good recognition rates on both the public emotion datasets FERPlus, RAF-DB and AffectNet, with accuracies of 91.43%, 90.03% and 64.43%, respectively, with good robustness and generalizability. Additionally, we conducted experiments using the model in real classroom scenarios, detecting and accurately predicting students’ classroom emotions in real time, which provides an important reference for improving teaching in smart teaching scenarios.

Abstract Object detection using UAV images is a current research focus in the field of computer vision, with frequent advancements in recent years. However, many methods are ineffective for challenging UAV images that feature uneven object scales, sparse spatial distribution, and dense occlusions. We propose a new algorithm for detecting small objects in UAV images, called ATBHC-YOLO. Firstly, the MS-CET module has been introduced to enhance the model’s focus on global sparse features in the spatial distribution of small objects. Secondly, the BHC-FB module is proposed to address the large-scale variance of small objects and enhance the perception of local features. Finally, a more appropriate loss function, WIoU, is used to penalise the quality variance of small object samples and further enhance the model’s detection accuracy. Comparison experiments on the DIOR and VEDAI datasets validate the effectiveness and robustness of the improved method. By conducting experiments on the publicly available UAV benchmark dataset Visdrone, ATBHC-YOLO outperforms the state-of-the-art method(YOLOv7) by 3.5%.

Abstract Human pose estimation has a wide range of applications. Existing methods perform well in conventional domains, but there are certain defects when they are applied to sports activities. The first is lack of estimation of the extremity posture, making it impossible to comprehensively evaluate the movement posture; the second is insufficient occlusion handling. Therefore, we propose a human pose compensation network based on incremental learning, which obtains shared weights to extract detailed features under the premise of limited extremity training data. We propose a higher-order feature compensator (HOF-compensator) to embed the attributes of the extremity into the torso and limbs topology structure, building a complete higher-order feature. In addition, to improve the occlusion handling performance, we propose an occlusion feature enhancement attention mechanism (OFE-attention) that can identify occluded keypoints and enhance attention to occlusion areas. We design comparative experiments on three public datasets and a self-built sports dataset, achieving the highest mean accuracy among all comparative methods. In addition, we design a series of ablation analysis and visualization displays to verify that our method performs best in sports pose estimation.

Abstract The interaction data used for training recommender systems often exhibit a long-tail distribution. Such highly imbalanced data distribution results in an unfair learning process among items. Contrastive learning alleviates the above issue by data augmentation. However, it lacks consideration of the significant disparity in popularity between items and may even introduce false negatives during the data augmentation, misleading user preference prediction. To address this issue, we combine contrastive learning with a weighted model for negative validation. By penalizing identified false negatives during training, we limit their potential harm within the training process. Meanwhile, to tackle the scarcity of supervision signals for unpopular items, we design Popularity Associated Modeling to mine the correlation among items. Then we guide unpopular items to learn hidden features favored by specific users from their associated popular items, which provides effective supplementary information for their representation modeling. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms state-of-the-art baselines in recommendation performance, with Recall@20 improvements of 4.2%, 2.4% and 3.6% across the datasets, but also shows significant effectiveness in relieving popularity bias.

Abstract The intermittency and randomness of photovoltaic power present different characteristics due to seasonal variations, which greatly affects the reliability of power supply. To boost the prediction accuracy of photovoltaic power, a short-term prediction combination model named GSK–BiGRU–XGboost is proposed. First, the Pearson correlation coefficient is adopted to determine highly-correlated meteorological factors to photovoltaic power to construct the input features. Second, the prediction errors of different single models are compared, and the two, i.e., Bidirectional Gated Recurrent Unit (BiGRU) and Extreme Gradient Boosting (XGboost) that have the smallest errors and lowest correlation are selected to construct the combination model. Third, to achieve an appropriate weight coefficient of the model, an improved gaining sharing knowledge-based algorithm (GSK) based on parameter adaption is designed to optimize it effectively. Fourth, seasonal models and year-round model based on GSK–BiGRU–XGboost are compared to reveal the effect of seasonal characteristics. Finally, the influence of historical meteorological data window with different steps is investigated. To verify the performance of GSK–BiGRU–XGboost, it is compared with different single and combination models under different weather conditions. GSK–BiGRU–XGboost achieves a high prediction accuracy of 97.85%, which is 9.46% and 12.43% higher than its member models, respectively. Besides, GSK can lead to a 1.71% improvement in the accuracy.

Abstract Background Glomerulonephritis (GN) encompasses a heterogeneous group of kidney diseases, often presenting with subclinical manifestations in children, leading to frequent missed diagnoses. Renal biopsy, while considered the gold standard, is invasive, prone to sampling errors, and time-consuming, thus hindering rapid diagnosis. This study aimed to develop a noninvasive diagnostic model for childhood GN using renal ultrasound images through the integration of deep learning and radiomics techniques. Methods Ultrasound images were acquired from children undergoing ultrasound-guided biopsy. A total of 469 renal ultrasound images were selected and divided into training and validation sets at a ratio of 8:2 to train a U-Net model for precise kidney image segmentation. Using radiomics, a comprehensive set of radiomic features were extracted from the segmented kidney regions. The extracted features were categorized based on GN types: IgA nephropathy (127 cases), minimal change disease (83 cases), and Henoch–Schönlein purpura nephritis (103 cases). These categories were further randomly split into training and validation sets at a ratio of 8:2. Within the training set, analysis of variance (ANOVA) was used for feature selection, followed by supervised Least Absolute Shrinkage and Selection Operator (LASSO) regression for dimensionality reduction, resulting in the selection of 37 features. These features were then integrated with a random forest algorithm to develop a GN classification model. The model's performance was comprehensively evaluated using the validation set. Results The segmentation model exhibited remarkable performance during training, achieving an accuracy of 95.19% in the validation set. Thirty-seven features were identified through feature selection, leading to the development of a robust classification model. Evaluation on the validation set revealed high accuracy and predictive power across different GN categories, with Area Under the Curve (AUC) values ranging from 0.91 to 0.98. Conclusions The combined use of deep learning and radiomics techniques utilizing renal ultrasound images demonstrates significant potential for classifying childhood GN subtypes. This noninvasive approach holds promise for improving diagnostic efficiency and patient outcomes in GN.

Abstract This survey overviews recent Graph Convolutional Networks (GCN) advancements, highlighting their growing significance across various tasks and applications. It underscores the need for efficient hardware architectures to support the widespread adoption and development of GCNs, particularly focusing on platforms like FPGAs known for their performance and energy efficiency. This survey also outlines the challenges in deploying GCNs on hardware accelerators and discusses recent efforts to enhance efficiency. It encompasses a detailed review of the mathematical background of GCNs behind inference and training, a comprehensive review of recent works and architectures, and a discussion on performance considerations and future directions.

Abstract In today’s world, software systems are extensively used across industries like medicine, commerce, education, entertainment, and industry. The development process includes design, development, and testing, where software testing is vital for quality assurance. Manual testing is laborious and resource-heavy, especially in large projects. Researchers are investigating methods to predict software defects by analyzing source code metrics, with Machine Learning techniques showing promise. The Machine learning models typically employs three approaches to predict defects: presence, number, and density. One cutting-edge approach in such models is LTR (Learning To Rank), which considers the order and values of predictions, aiding enterprises in efficiently allocating resources to more defective components. This article aims to enhance the performance of 5 machine learning models by leveraging 5 metaheuristics to optimize their hyperparameters. The study was conducted using 28 common datasets from the literature, with the FPA metric as the evaluation criteria. The results demonstrate that the proposed method achieved a 13% increase compared to state-of-the-art models with Random Forest, achieving an average FPA of 0.84 across the datasets.

Abstract This paper introduces the novel Adaptive Peak Price with Lazy Updates (APPLU) approach for short-term portfolio optimization (SPO), a method that innovatively combines the Radial Basis Function (RBF) and a new lazy update approach to address the unique challenges of SPO. Our approach is tailored to balance the dual objectives of maximizing returns and minimizing transaction costs, which are critical concerns in dynamically allocating wealth among various assets over time. Unlike conventional methods that primarily rely directly on peak price information, APPLU introduces an adaptive peak price using RBFs to capture continuous depreciation information of assets, thereby alleviating the aggressive nature of traditional peak price strategies and avoiding frequent trades. Furthermore, we propose a new lazy update approach that employs unsquared $$l^{2}$$ l 2 -norm regularization to represent portfolio changes. This approach contrasts with squared $$l^{2}$$ l 2 -norm regularization, which disproportionately penalizes larger portfolio changes while being more lenient towards smaller changes. Thereby, our methodology offers a more balanced and effective approach to portfolio adjustment. Extensive experiments conducted on seven real datasets demonstrate that APPLU outperforms existing strategies in terms of cumulative return and risk-adjusted return, while effectively controlling transaction costs and maintaining a moderate wealth accumulation strategy.

Abstract Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred’s modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a “plug-and-play” manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred’s functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred . Scientific Contribution QSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.

Abstract The accurate identification of protein–ligand binding sites is of critical importance in understanding and modulating protein function. Accordingly, ligand binding site prediction has remained a research focus for over three decades with over 50 methods developed and a change of paradigm from geometry-based to machine learning. In this work, we collate 13 ligand binding site predictors, spanning 30 years, focusing on the latest machine learning-based methods such as VN-EGNN, IF-SitePred, GrASP, PUResNet, and DeepPocket and compare them to the established P2Rank, PRANK and fpocket and earlier methods like PocketFinder, Ligsite and Surfnet. We benchmark the methods against the human subset of our new curated reference dataset, LIGYSIS. LIGYSIS is a comprehensive protein–ligand complex dataset comprising 30,000 proteins with bound ligands which aggregates biologically relevant unique protein–ligand interfaces across biological units of multiple structures from the same protein. LIGYSIS is an improvement for testing methods over earlier datasets like sc-PDB, PDBbind, binding MOAD, COACH420 and HOLO4K which either include 1:1 protein–ligand complexes or consider asymmetric units. Re-scoring of fpocket predictions by PRANK and DeepPocket display the highest recall (60%) whilst IF-SitePred presents the lowest recall (39%). We demonstrate the detrimental effect that redundant prediction of binding sites has on performance as well as the beneficial impact of stronger pocket scoring schemes, with improvements up to 14% in recall (IF-SitePred) and 30% in precision (Surfnet). Finally, we propose top-N+2 recall as the universal benchmark metric for ligand binding site prediction and urge authors to share not only the source code of their methods, but also of their benchmark. Scientific contributions This study conducts the largest benchmark of ligand binding site prediction methods to date, comparing 13 original methods and 15 variants using 10 informative metrics. The LIGYSIS dataset is introduced, which aggregates biologically relevant protein–ligand interfaces across multiple structures of the same protein. The study highlights the detrimental effect of redundant binding site prediction and demonstrates significant improvement in recall and precision through stronger scoring schemes. Finally, top-N+2 recall is proposed as a universal benchmark metric for ligand binding site prediction, with a recommendation for open-source sharing of both methods and benchmarks.

Abstract Background Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. Results We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development. Conclusion This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5’s HydraScreen and Strateos’ automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs. Scientific contribution We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery.

Due to the diversity of image sources, content-based multi-source image fusion and retrieval have shown promising capabilities in computer vision tasks, and especially when applied in Computer-Aided Diagnosis (CAD) to automate and improve the accuracy of medical image analysis. The combination of computer vision and CAD systems has the potential to revolutionize healthcare by augmenting the expertise of clinicians, improving overall diagnostic accuracy and helping experts in the clinical decision-making process by classifying and retrieving similar annotated clinical images to a given query. In the context of multi-view mammography interpretation, the concept of multi-view feature fusion has recently been studied to improve retrieval performance while effectively guaranteeing the complementarity of both MLO and CC views. However, conventional multi-view feature fusion makes descriptors long and lacks to take into consideration the relationship between descriptors. To deal with this issue, we propose two hierarchical multi-view feature fusion methods, for multi-view mammogram retrieval, based on the Canonical Correlation Analysis (CCA), which is the most commonly used multivariate parametric test. In fact, we have adapted CCA to determine the relationship between two descriptors by processing latent correlation factors. Moreover, after extracting descriptors for each view, a comparative study of texture and shape fusion descriptors is proposed in order to identify the more discriminative features for multi-view mammogram retrieval. Then, a query-dependent distance metric preserving both visual resemblance and semantic similarity is carried out to dynamically determine the more appropriate distance measure for each query image. Extensive experiments on the challenging Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) have demonstrated the effectiveness of the proposed hierarchical multi-view feature fusion for mammogram retrieval, which outperforms the performance achieved either by conventional fused information or by single view information. To improve the transparency of our paper, the source code of the proposed method and the related dataset (including readme files) are publicly accessible through the following GitHub link: https://github.com/ABDERRAHIMMAR/Multi-View-Feature-Fusion-for-Mammogram-Retrieval. This open-access resource empowers researchers and practitioners to delve deeper into our methodology, fostering collaboration and advancements in the field of computer-aided diagnosis and medical image analysis.

Key information extraction and recognition from rich text images are crucial for various applications. There are two main tasks involved in this process: Line Item Recognition (LIR) and Key Information Localization and Extraction (KILE). LIR aims at identifying and interpreting data line items in a document. The essential information in each line item is then classified or extracted, a task known as KILE. A widely used approach for this problem is sequence based, which relies on the generalization of a language model and requires a significant amount of training time. We present an effective and reliable solution to the problem by using RoBERTa, a transformer model trained on a large corpus, along with the LION optimizer to improve the training process. A comprehensive evaluation was conducted on two different benchmarks, emphasizing two different languages, English and Vietnamese. Experimental results on DocILE indicate that the proposed framework significantly improves the KILE task with a 7.24% increase in accuracy compared to the baseline and also enhances the correct recognition rate at the LIR stage. On MCOCR, the method achieved a Character Error Rate (CER) of 28.6%, which is competitive with the state-of-the-art on this dataset.

Prostate cancer is increasingly common among men. However, the process of diagnosing malignant disease is relatively complicated and time-consuming. Identifying benign or malignant tumors early can assist medical professionals in choosing appropriate treatment methods. Consequently, we introduce a soft-voting ensemble model comprising several single machine learning models such as Logistic Regression, Random Forest, XGBoost, LGBM, and Support Vector Machine for the classification task with the prostate cancer dataset. The dataset was divided into two parts for training and testing with a ratio of 67:33. The confusion matrix was used to evaluate the performance of both the individual and ensemble models. Experimental results show that ensemble models achieve performance ranging from 87.88% to 96.97%, which is 3% to 9% better than individual models, surpassing recent research. Integrating the strengths of individual models helps minimize errors, resulting in optimal classification with high accuracy and overall performance in the field of machine learning.

Healthcare Recommendation Systems (HRSs) primarily aim to offer advice, recommendations, or suggestions related to human healthcare. Similar to other information systems, datasets affect HRSs’ efficiency. The larger datasets can make the information more diverse and complete. Therefore, the recommendation systems can be more accurate and deliver better performance. In addition, several recent studies have revealed that to enhance the accuracy of systems, there should be a switch from a model-centered method to data-centricity. Therefore, several datasets have been introduced over the past few years to assess the effectiveness of HRSs. However, building new datasets and selecting suitable datasets to evaluate HRSs is an issue. Another problem is that there are presently no articles surveying the datasets that have been published to offer the requirements that a dataset uses to judge the HRSs needs to have. These are the reasons that motivated us to conduct this research. To address these problems, we systematically focus on a comprehensive survey of the datasets used to evaluate HRSs published in recent years. Some of the primary contributions of this study are as follows: (i) Surveying 34 datasets commonly used in methods for building and developing HRSs. (ii) Extracting 10 common characteristics and comparing datasets with those 10 common characteristics. (iii) Providing the 13 requirements regarding a dataset used in HRSs (iv) Comparing surveyed datasets and state-of-the-art HRSs based on 13 constructed requirements (v) Discussing the five main challenges and set out some directions for building new datasets in the future. The results of this research analysis are valuable to many new researchers in choosing or constructing suitable datasets to evaluate how they build their HRSs for the present and future.

The security of digital communication and information systems is mostly dependent on block ciphers. ARX-based ciphers are widely used due to their simplicity and efficiency. This paper provides an exhaustive cryptanalysis of a subset of ARX-based block ciphers, with particular emphasis on SIMON, SPECK, and IDEA. These ciphers need to be exposed for their weaknesses in algebraic attack resistance and cryptographic properties such as key sensitivity. In addition, we assess the resource utilization and speed of these ciphers, both of which are critical for practical implementation. SMT (Satisfiability Modulo Theories) framework is utilized to tackle constraint fulfillment problems based on first-order logic. The following cryptographic steps use SMT solvers: differential cryptanalysis, collision attack, pre-image attack, modular root-finding, and cryptographic primitive verification. We show that SMT solvers can solve block cipher cryptanalysis constraints. In a cryptanalytic attack, we convert block cipher boolean equations to Z3py. The proposed cryptanalysis method evaluates ARX cipher performance. This method recovers the partial secret key using plaintext and ciphertext pairs, partial key bits, and a predetermined number of rounds. To determine whether SIMON, SPECK, or IDEA are appropriate for distinct security requirements, we conducted a comparative analysis of the results and presented them in tabulated form. This research builds a better understanding of ARX-based block ciphers and allows us to develop more robust and efficient cryptographic algorithms to protect sensitive data in modern communication systems.

As the complexity of software systems is ever increasing, so is the need for practical tools for formal verification. Among these are automatic theorem provers, capable of solving various reasoning problems automatically, and proof assistants, capable of deriving more complex results when guided by a mathematician/programmer. In this paper we consider using the latter to build the former. In the proof assistant Isabelle/HOL we combine functional programming and logical program verification to build a theorem prover for propositional logic. We also consider how such a prover can be used to solve a reasoning task without much mental labor. The development is extended with a formalized proof system for writing machine-checked sequent calculus proofs. We consider how this can be used to teach computer science students about logic, automated reasoning and proof assistants.

In today's rapidly evolving manufacturing landscape, the efficient allocation of human resources is crucial for optimizing production systems. This paper addresses the dual resource-constrained flexible job shop scheduling problem (DRCFJSP) by implementing a Multi-Start Tabu Search-based Multi-Agent Model (MuSTAM). MuSTAM considers a set of initial solutions that are run in parallel using an intensification technique. The main objective of MuSTAM is to reduce the completion time (makespan), which is an important metric in workshops. The proposed model is composed of two classes of agents: a supervisor agent named MainAgent and a set of tabu search agents called TabuAgents. The MainAgent launches the system, collects inputs, generates the initial population, creates TabuAgents based on the number of solutions in the initial population (PopSize), and displays the best solution. Each TabuAgent receives a solution from the initial population and then conducts a concentrated search around its neighbourhood space by applying the intensification technique of tabu search. TabuAgents cooperate and communicate with each other to enhance search quality. Numerical tests are performed during the experimental phase to compare MuSTAM with ITS based on well-known benchmark instances of the DRCFJSP. The results demonstrate that MuSTAM is effective in terms of makespan and CPU time.

Abstract This paper introduces two novel self-induced basis strategies, interior cevian initialization for orthogonal matching pursuit (ICe) and its $$l_2$$ l 2 -sense optimized version, optimized interior cevian initialization (OICe). These methods address the complexity and convergence issues of the previously proposed self-inspired bases (SIBS) approach. Both algorithms enhance sparse image representation by generating adaptive auxiliary atoms tailored to the image, thereby improving reconstruction quality and computational efficiency. Experimental results show that ICe achieves up to a 35.6% improvement in Normalized Mean Squared Error (NMSE) and a 16.4% increase in Structural Similarity Index (SSIM) compared to conventional OMP. OICe, as the $$l_2$$ l 2 -sense optimized version of ICe, delivers even greater performance, with improvements of up to 40% in NMSE and 18.5% in SSIM. Additionally, both ICe and OICe significantly reduce bits per pixel (BPP) for image encoding, outperforming SIBS when paired with state-of-the-art dictionaries such as KSVD, MOD, RBDL, and ODL. These results highlight the effectiveness of ICe and OICe in addressing key challenges in sparse representation, offering enhanced accuracy and efficiency across a variety of applications.

Abstract In the English language, when a sentence contains all letters of the alphabet, such as “The/A quick brown fox jumps over the lazy dog”, this is known as a pangram. Curiously, despite its odd meaning, this fox-dog pangram has found practical usage in some applications, mainly in text typography related to computer graphics, in linguistics, as a writing tutorial, or for testing computer keyboards. Even more curiously, it has been detected as a residual of template text in some academic papers. This unique linguistic curiosity was examined by screening Scopus and Web of Science, as well as Google Scholar from 2019–2023, to appreciate its practical use, as well as to assess whether there were any cases of residual template text, the main target of this exercise. In the latter case, this letter advocates for the removal of this residual text, in the form of a literary correction.

Abstract Customer churn is a common problem faced by many industries, including telecommunication industries. This has resulted in the development of advanced techniques for the prediction and prevention of customer churn. The availability of stored customer data in the form of big data, together with the use of advanced and tuned machine learning (ML) algorithms, have paved the way for the realisation and extraction of useful features associated with customer behaviour and consequently the prediction of customer churn. An effective way to further improve churn prediction capability of different ML algorithms is through the employment of topological data analysis (TDA). TDA is a framework that applies topological methods to uncover the underlying hidden structural features in complex, high-dimensional data. Here, a TDA summary of 0- and 1-dimensional holes of the data, called barcode statistics, was extracted and used as an additional feature to the preprocessed customer data. To address issues such as the effective preprocessing and analysis of large customer datasets and the effective tuning of ML hyperparameters, we implement an advanced data preprocessing technique that consists of different stages such as handling of missing data, feature engineering, encoding of categorical features using the hashing encoding method, and feature selection. Without including barcode statistics in the model, the XGBoost algorithm with tuned hyperparameters achieved the best results, with accuracy of 92.71%, precision of 85.95%, recall of 92.71%, and F-measure of 89.20%. Including barcode statistics as an additional feature, the XGBoost algorithm with tuned hyperparameters achieved the best and much improved results, with accuracy of 98.50%, precision of 98.50%, recall of 98.50%, and F-measure of 98.50%. The use of TDA barcode statistics significantly improved the churn prediction capability of the ML algorithms. In addition, hyperparameter tuning is not needed when an effective data preprocessing technique is used, or when barcode statistics is used. The best accuracy of 98.5% from this work was in line with the best accuracy of 98.7% from a related work, but interestingly, the best precision of 98.5% from this work was superior to the 94.3% precision from the same related work with higher accuracy.

Abstract Cardiovascular diseases (CVD) have been found to be prevalent in society, frequently ending in death. According to the findings of a recent survey, the mortality rate is increasing due to the prevalence of adult cigarette consumption, elevated blood pressure, high cholesterol levels, and obesity. The previously mentioned causes are exacerbating the severity of the condition. A pressing necessity exists for a study on the variability of these factors and their impact on cardiovascular disease (CVD). This involves the use of advanced tools to detect the disease early on and aid in the reduction of fatality rates. With their extensive methodologies that would help in the early CVD prediction and recognition of behavioral patterns in large amounts of data, artificial intelligence, and data mining disciplines offer a broad study potential. The results of these predictions will help physicians make decisions and early diagnoses, decreasing the risk of patient death. This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. For this study, articles from 2012 to 2023 were considered; after filtering, 82 articles were chosen for primary research. Future researchers will benefit from this review on cardiovascular disorders by better understanding the Deep Learning and Machine Learning models now in the healthcare sector. The review encompasses commonly employed methodologies such as support vector machine, decision tree, random forest, and convolutional neural networks (CNNs). Additionally, this survey aggregates and presents information on the performance metrics used to report accuracy. It also goes over the most popular datasets used by various diagnostic models (ECG and PCG signals datasets). In addition, it emphasizes prominent publishers, journals, and conferences that serve as platforms for the evaluation of scholarly works. Additionally, it will facilitate their understanding of the unresolved challenges or hurdles experienced by past researchers. A lack of more extensive and consistent datasets was the most common issue, followed by the need to improve existing models.

Abstract In this study, we focused on the temporal rate of change of headwind, which is one of the recorded parameters in the aircraft’s in-flight quick access recorder data. We selected the Laplace distribution and utilized the scaling parameter to construct a low-level turbulence risk assessment model. Using this model, we calculated the risk of low-level turbulence occurrence at five airports in Japan based on the month, time of day, and wind speed. We visualized how the geographical conditions at each airport influenced risk in relation to airport wind speeds. We developed a low-level turbulence visualization site linked to weather conditions using these results to enable pilots to easily verify low-level turbulence risk and incorporate this information into their flight routines. These findings are anticipated to significantly enhance aircraft safety.