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Automatically generating a survey draft is a challenge to text summarization research because it needs to select important sentences from important references in a large set of candidate papers for composing sections that are in line with section titles and different sections discuss the most relevant reference papers of different number, which are beyond the capability of previous text summarization approaches as they assume that all candidate papers should be included into one summary. This paper proposes an approach to generating survey draft according to a pattern consisting of sections with titles given by the user who requests the survey. The problem of generating each section can be divided into the following sub-problems: (1) rank the input scientific documents (in short documents) according to the title of a section, (2) determine the number of documents that are most relevant to the title, and (3) rank and select sentences from the selected documents according to the title. A position closeness distance of key word is proposed to rank a set of documents by measuring how closely two key words within section title are distributed within each document, which is used to rank the documents. The rationale is that the positions of the neighboring key words of a section title should be closer in more relevant documents than other words. As different sections have different number of selected documents, a method is proposed to determine the number of documents to be included into the current section based on the slope shape of the sorted rank curve of documents according to the section title. Based on the duality property of the closeness, ranks of sentences within a document can be directly obtained when the document is ranked according to the title of section, and both the importance and coherence of selected sentences can be reflected without extra calculation for ranking sentences. Experiments and manual evaluation show that the proposed methods achieve significant improvements compared with other approaches. The proposed approach is significant in applications as different surveys can be generated according to different patterns given by different users. [ABSTRACT FROM AUTHOR]

Cloud Computing (CC) and virtualization concepts are two advanced technologies introduced to empower distance and blended learning. Besides, they play a crucial role in equipping learners with practical skills and fostering hands-on experience to defend against cyber-attacks. Many Higher Education Institutions (HEIs) in developed countries have already embraced the promise of CC to raise educational standards. However, the pace of its adoption in developing countries has stagnated. Moreover, existing solutions in the literature are not sustainable. They either rely on on-premise infrastructure or are bound by a single cloud service provider. Consequently, they are likely prone to failures and a sudden outage. To fill this gap, this paper is a first that comprehensively addresses the above issues and introduces a federated hybrid CC system based on an extension of Apache Virtual Computing Lab (VCL). The proposed system provides an independent open-source implementation, greater configuration flexibility, and methodological improvements as compared to existing studies in the literature. In addition, it promotes the sustainability of the CC services, extensible cloud architecture, and fault tolerance. VCL is primarily focused on provisioning Virtual Laboratories (VL) for remote cybersecurity and computer networks education, with potential expansion to other domains of engineering education. In addition, this paper introduces GPT-TerminalPro, a terminal-based tool driven by OpenAI's Generative Pretrained Transformer (GPT-3.5) that provides intelligent assistance to users while performing lab tasks. To experimentally evaluate the VCL's performance, the standard Linux tools as well as the Apache benchmark and httperf HTTP load generators are utilized. VCL has been tested with 30 users and 61 virtual user computing environments provisioning to validate the overall performance. The results are fascinating: the provisioning time including all VCL background tasks is always less than a minute and utilizes fewer computing resources while providing a better user experience. This paper will encourage the adoption of CC in low-income countries. • Breaking barriers for cloud adoption in developing countries education institutions. • Proposing a sustainable and open federated hybrid cloud computing system. • Effective provisioning of remote cybersecurity virtual laboratories. • Proposing GPT3-TerminalPro, an innovative chat-enabled AI tool based on GPT-3.5. • The VM ready time including all background tasks is less than one minute. [ABSTRACT FROM AUTHOR]

Hybridizing multiple mutation strategies has shown much effectiveness in helping differential evolution (DE) algorithms achieve good optimization performance. Though abundant adaptive ensemble strategies have been developed to adaptively employ multiple mutation strategies to evolve the population, most of them ignore to make full use of the properties and characteristics of the multiple mutation strategies. To fill this gap, this paper devises a bi-directional ensemble scheme for DE to adaptively assemble totally 8 mutation strategies with different properties and characteristics. As a result, a novel DE, which we call bi-directional ensemble DE (BDEDE), is developed. Specifically, this paper sorts the 8 mutation strategies roughly from two opposite perspectives, namely the convergence and the diversity. Then, we assign each mutation strategy with two different non-linear probabilities, which are calculated on the basis of its two rankings obtained from the two perspectives. Subsequently, to make full use of these mutation schemes, we first partition the whole population into two separate parts, namely elite individuals and non-elite individuals. Then, for each elite individual, we randomly select a mutation strategy from the 8 candidates based on the probabilities calculated by the convergence rankings, while for each non-elite individual, we stochastically choose a mutation scheme from the same 8 candidates but based on the probabilities computed by the diversity rankings. In this manner, the elite individuals prefer to exploit the located optimal areas, while the non-elite individuals tend to explore the solution space. Therefore, it is likely that BDEDE expectedly maintains a good balance between search diversity and search convergence. To further help BDEDE achieve such a purpose, this paper devises an adaptive partition strategy to dynamically separate the whole population into the two categories. With the above two techniques, BDEDE anticipatedly obtains good optimization performance. To verify its effectiveness and efficiency, we conduct experiments on the CEC2014 and the CEC2017 benchmark sets by comparing BDEDE with totally 14 well-known and state-of-the-art DE variants. Experimental results have shown that BDEDE performs competitively with or even significantly better than the 14 compared DE variants. The source code of BDEDE can be downloaded from https://gitee.com/mmmyq/BDEDE. [ABSTRACT FROM AUTHOR]

• Theoretical mechanism of the non-uniqueness optimal solution for cross-efficiency is revealed. • Weight priority is constructed to reflect the comparative advantage of inputs and outputs. • Weight priority strategy can serve as both an independent strategy and a supplementary strategy. • The uniqueness of optimal result obtained by weight priority strategy is theoretically proved. Due to a large number of studies on cross-evaluation strategies, the theoretical problem of DEA cross-efficiency approach has been transformed from the non-uniqueness of the optimal solution to the relative uniqueness, because the existing cross-evaluation strategies can only reduce the possibility of non-unique solutions, but cannot prove the existence of a unique optimal solution. This paper illustrates the relative uniqueness of optimal solution of the cross-efficiency approach with traditional cross-evaluation strategies, and the graphic method and theoretical analysis are used to explain the reasons of relative uniqueness. Based on the data characteristic of inputs and outputs, the weight priority rule is made for constructing the new cross-evaluation strategy, and its optimal solution is proved to be unique. In addition, the weight priority strategy can also be introduced into traditional strategies as a supplementary strategy, and then the unique optimal solution can be obtained on the premise of preserving the characteristics of traditional strategies. In general, this paper solves the non-uniqueness and relative uniqueness problem of optimal solution in the DEA cross-efficiency approach, and then the reliability and robustness of the approach are thus improved. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) of civil infrastructure is critically important due to its direct influence on public safety and economic activities. Exiting Building Information Modeling (BIM)-based SHM systems often use offline data processing techniques to analyze and visualize structural health data. Despite some of them adopting Internet of Things (IoT) to enable real-time sensor data collection, sensor data quality still remains uncertain due to a lack of sensor signal preprocessing in those existing systems. Additionally, the IoT-based SHM systems often disregard structural analysis domain knowledge, which is important for accurate and precise SHM. Therefore, there is still a need to improve existing systems and practices by enabling more efficient and reliable data collection and processing as well as providing more representative SHM data visualization in BIM. As such, this paper proposes an intelligent BIM-enabled digital twin (DT) framework that integrates wireless IoT sensing and communication, digital signal processing (DSP), and structural analysis domain knowledge. The proposed system (1) leverages IoT sensing and wireless communication to enable autonomous and real-time SHM sensor data collection and transmission, (2) applies and compares multiple DSP techniques to preprocess the sensor data/signals, and (3) innovatively embraces structural analysis expertise into structural behavior visualization in BIM by performing sensor data interpolation for enabling the visualization of structural behaviors at different locations of a structural element/component. The proposed BIM-enabled DT framework was demonstrated and tested for monitoring and visualizing the structural deformations of critical structural components using a prototyped structural frame subject to bending forces. The developed framework could be used and extended for any structural elements (such as beams, columns, trusses, slabs, arches, bracings, walls, footings, foundations, and girders, among others) and could be applied to any kind of structure. Experimental results showed that the proposed framework could effectively monitor and intuitively visualize the structural deformations under different load configurations with a high DT updating frequency of 5 Hz. The innovation of this study is reflected by integrating structural analysis expertise with IoT-enabling sensing data analytics in order to improve the representativeness of real-time structural behavior visualization in BIM and to advance the DT-based SHM systems in a faster and more adaptive direction. Ultimately, this paper contributes to the body of knowledge by developing a generic and easily extendable BIM-enabled DT framework for SHM with high sensor data quality and improved visualization to advance the existing practices of BIM-based SHM for civil infrastructure asset management. [ABSTRACT FROM AUTHOR]

To accurately forecast crude oil futures price and volatility, this article presents a novel deep learning combined model using secondary decomposition with West Texas Intermediate crude oil futures (WTI) and North Sea Brent crude oil futures (Brent) as examples. Firstly, a trend subsequence and several noise subsequences are obtained by decomposing the crude oil futures price or volatility using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the secondary decomposition is performed on the highest frequency noise subsequence using the variational mode decomposition (VMD). Secondly, the remaining subsequences obtained from CEEMDAN and the subsequences obtained from VMD are predicted separately using the BiGRU-Attention-CNN model. Finally, the predicted crude oil futures price or volatility is calculated by linearly integrating the predicted values of each subsequence. The empirical analysis shows that the novel combined model using secondary decomposition proposed in this paper has the best forecasting performance among many models, both in the comparison experiments and in the ablation experiments. The model is also shown to have good robustness by predicting the volatility at different maturities, varying the ratio of the training set and the test set for crude oil futures price prediction, and predicting the price of crude oil futures after extreme events. Overall, the novel combined forecasting model using secondary decomposition proposed in this paper can help countries grasp the direction of the crude oil market and improve national economic and political security. [ABSTRACT FROM AUTHOR]

[Display omitted] Artificial intelligence (AI) in education (AIED) has evolved into a substantial body of literature with diverse perspectives. In this review paper, we seek insights into three critical questions: (1) What are the primary categories of AI applications explored in the education field? (2) What are the predominant research topics and their key findings? (3) What is the status of major research design elements, including guiding theories, methodologies, and research contexts? A bibliometric analysis of 2,223 research articles followed by a content analysis of selected 125 papers reveals a comprehensive conceptual structure of the existing literature. The extant AIED research spans a wide spectrum of applications, encompassing those for adaptive learning and personalized tutoring, intelligent assessment and management, profiling and prediction, and emerging products. Research topics delve into both the technical design of education systems and the examination of the adoption, impacts, and challenges associated with AIED. Furthermore, this review highlights the diverse range of theories applied in the AIED literature, the multidisciplinary nature of publication venues, and underexplored research areas. In sum, this research offers valuable insights for interested scholars to comprehend the current state of AIED research and identify future research opportunities in this dynamic field. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) have enormous potential to enhance traffic safety, efficiency, and emissions reduction. However, in the initial phases of CAV development, mixed traffic comprising CAVs and human-driven vehicles (HDVs) will inevitably coexist in the traffic system. To fully exploit the benefits of CAVs, dedicated lanes with independent rights of way will be established. This paper proposes an optimal control strategy for coordinating the mandatory lane-changing of CAVs from ordinary lanes to dedicated lanes. The strategy develops a centralized two-stage cooperative optimal control model to optimize the lane-changing sequence and trajectories of CAVs. In the first stage, a dynamic programming formulation is designed to determine the lane-changing sequence decisions. The model predictive control (MPC) controller is adopted to dynamically solve the optimal control problem with a fixed terminal state. In the second stage, we dynamically and cooperatively designed the longitudinal trajectories of related CAVs. The lateral trajectories of lane-changing CAVs are planned with a cubic polynomial. The objective function considers driving comfort and state tracking to ensure traffic smoothness. Simulation results show that: (1) the proposed strategy can improve the negative impact of lane-changing behavior under different traffic demand levels. (2) Compared to the benchmark approach, the proposed strategy can significantly enhance traffic efficiency and driving comfort, particularly in medium-traffic demand. The strategy can improve the average speed of CAVs by approximately 12 % and decrease the average acceleration by over 45 %. (3) The average fuel consumption is positively correlated with traffic demands and the difference in arrival speeds between lane-changing and dedicated lane CAVs. (4) The effectiveness of the strategy increases with the length of the lane-changing segment. However, the marginal benefit becomes negligible when the segment exceeds 300 m. Therefore, the findings of this paper can provide theoretical support for the cooperative control of CAVs in dedicated lanes of highways in the future. [ABSTRACT FROM AUTHOR]

In the increasingly complex urban transportation landscape, the necessity for accurate, multi-step forecasting has never been more apparent to help plan for long-term, strategic transportation initiatives like Intelligent Transportation Systems. This work focuses on resolving uncertainties around the proper training step size for machine learning models performing multi-step traffic flow forecasting. Two strategies are considered: one-step and horizon-step training sizes, which extend from the single-step forecasting method. This paper compares and evaluates two machine learning models: the Support Vector Regression and Long Short-Term Memory (LSTM). Data from two country road locations, including urban roads in Kuala Lumpur and the I5-North freeway in California, are employed to forecast traffic flow in 1-hour increments, projecting from 2 h up to 24 h ahead. The results reveal a significant difference in performance between the two training step sizes, with the one-step training size emerging as the more consistent and optimal strategy for both Direct and Multi-Input Multi-Output forecasting strategies. The proposed model, the Direct Particle Swarm Optimization Grid Search Support Vector Regression (Direct-PSOGS-SVR) model, exhibited comparable or slightly better performance than the LSTM model in both environments. In the I5-North freeway dataset, the Direct-PSOGS-SVR achieved similar Root Mean Squared Error values across most forecasting tasks compared to the LSTM, indicating its effectiveness in stable traffic flow patterns. Notably, in the more dynamic urban environment of Kuala Lumpur, the Direct-PSOGS-SVR model also demonstrated stability and resilience in forecasting accuracy, effectively handling the inherent noise and fluctuations in traffic patterns. These findings serve as a valuable guide for practitioners in selecting the most efficacious combination of training strategies for specific time series forecasting tasks utilizing machine learning models, particularly in traffic flow forecasting. Introducing the Direct-PSOGS-SVR model enriches the landscape of machine learning solutions for traffic forecasting, underscoring the paper's contributions to the broader understanding of time series forecasting dynamics. [ABSTRACT FROM AUTHOR]

• We conducted a quantitative analysis of intelligent SCA technology over the past 20 years using bibliometric methods and data portrait techniques and traced earlier literature for a systematic review. • We identified the hotspots and research frontiers of intelligent ship collision avoidance in maritime transportation. • We discussed the contributions, innovations, and trends of future research in intelligent ship collision avoidance. With the increasing promotion of digital technology, safety issues faced by rush ships in the maritime industry have once again received attention. Ship collision avoidance (SCA) is not only a hot topic in the shipping industry but also an eye-catching issue in the development of intelligent ships. This paper provides a bibliometric and systematic overview of the literature on SCA to assist researchers in understanding the frontiers and recent trends of SCA. Based on the bibliographic portrait, a classification and grading study was conducted on the literature to provide a systematic review of it. A screening process was conducted on 851 relevant articles related to SCA and published in 2004–2023 in the Web of Science (WoS) database, and 526 highly relevant and high-quality papers were selected. Then, CiteSpace, VOSviewer software, and data visualization techniques were used to conduct a bibliographic portrait analysis on the selected papers. The evidence from these systematic literature reviews revealed the close collaboration relationships among researchers, research institutions, and countries or regions in the field of SCA studying shortly. Furthermore, the frontiers of the research on SCA were focused on three aspects, i.e. research subjects, technological methods, and novel algorithms in intelligent SCA. COLREG is a problem that must be considered in intelligent SCA. The future trends in intelligent SCA were explored and SCA technology was introduced from artificial intelligence (AI) so as to meet the safety requirement of maritime autonomous surface ships. AI algorithms such as machine learning and deep learning are key technologies for SCA. This research provides a theoretical basis and implementation directions of the research on SCA. The hybrid encounters between traditional ships and intelligent ships, as well as multi-ship encounters in narrow waterways, will be the focal points of attention in the future. [ABSTRACT FROM AUTHOR]

Reducing greenhouse gas emissions is urgent for the global community with rising climates. Considering the importance of renewable energy in mitigating climate warming, forecasting renewable energy generation is vital for the Chinese government's future low-carbon and green development plan. This paper proposes a novel multivariable grey model based on historical data on China's renewable energy generation and three industries. A novel information accumulation mechanism with two adaptive factors is designed to improve the traditional multivariable grey modeling defect. Based on the proposed mechanism, this paper optimizes the initial and background values and nonlinear model structure with the whale optimization algorithm. The forecasting results show that the fitting MAPE is 1.13%, comprehensive MAPE is 2.60%, MSE is 50.86, and RMSE is 7.13, which significantly improve the forecasting accuracy of traditional GM(1,N) and are better than other compared models. The forecasting results show that China's renewable energy generation will gradually increase to 5834.02 TWh. The Chinese government should keep the previous Five-Year Plans rising trend of the three industries in the future Five-Year Plans to support renewable energy industries. In China's future energy system, it is necessary to promote incentive policies and capital investment for actively accelerated development to make renewable energy the leading force. [ABSTRACT FROM AUTHOR]

In this paper, we developed a dynamic Bayesian network (DBN) model to quantify uncertainties on battlefields. The model consists of the enemy's intention prediction model and the intelligence, surveillance, and reconnaissance (ISR) reliability model quantified by using the sensor detection probability. We calibrated and validated the DBN using a historical dataset based on actual provocations by comprehensively referencing open-source intelligence, including news articles from mass media and official announcements of the Ministry of National Defense. The calibrated model was then used to predict the enemy's intention in the near future, and the accuracy of the model was 84.6%. We suggested an appropriate course of action (COA) based on the enemy's intention to expedite decision-making. We further studied the interaction between the predicted enemy's intention and the selected COA. The main results of this paper are that various information regarding enemy actions, either equality or inequality, can be incorporated into the decision-making process. [ABSTRACT FROM AUTHOR]

Machine learning methods are widely used in Android malware detection, but carefully scrambled adversarial samples can effectively evade detection. Adversarial defence methods such as retraining and ensemble learning have high training costs and significant errors. This paper proposes a novel Android malware detection system, KFFPDet, to detect adversarial samples. It can detect adversarial samples efficiently. Firstly, this paper uses K-Nearest Neighbor (KNN) as the primary detection algorithm. In addition, this paper proposes a three-stage adversarial sample detection method, which saves the detection cost and reduces the error through prior knowledge. In the first stage, the frequency difference between benign typical features and malicious typical features is used to filter adversarial samples. In the second stage, the feature continuous values of benign typical features are used for adversarial sample filtering. The third stage filters adversarial samples by detecting isolation permissions and application programming interface (API). Experiments on various benchmark datasets verify the detection effect of KFFPDet. KFFPDet achieves 96% accuracy of adversarial sample detection on the VirusShare dataset. Compared with other advanced methods, KFFPDet 's adversarial sample detection accuracy is improved by 4.43% to 94.53%. The experimental results show that the proposed three-stage adversarial sample detection method is useful and will help design a more effective adversarial sample detection scheme. [ABSTRACT FROM AUTHOR]

• Gas turbine life management monitors and assesses turbine status and conditions. • Inlet and exit air passages impact gas turbine performance, not physical attributes. • Study gap: An incomplete gas-turbine model limits big data's predictive analysis. • Big data analytics drive optimized informed integrated health monitoring decisions for gas turbines. • Data mining extends gas turbine life by forecasting faults for the future. In addition to power generation, gas turbines (GT) are crucial components in many engineering applications, including those for aircraft, ships, and other machinery that needs a prime mover. One of the main concerns was predicting a comprehensive GT model, including intake, exit, and power-generator characteristics. Hence, this study provides a comprehensive review of GT's main components, parameters, faults, and predicting models using machine learning and data mining applications. Forty-four publications between 2014 and 2023 were chosen for this review study based on the primary keywords associated with the paper title. This study focuses solely on the GT model prediction aspects, considering the significance of field distribution, applications of data mining, research goals, benefits, constraints, and contributions to aviation, the oil and gas industry, and marine propulsion fields. Based on the examined studies, ANN, ELM, and KF applications show promise for potential GT life management. The typical limitations of GT life prediction, as observed in 93.2% of the review papers, involved the absence of a comprehensive GT model that incorporates all vibration factors and the use of substantial amounts of real data for predictive modelling. Another notable finding that underscores the knowledge gap in this area is the utilization of complete GT units to monitor unit life and facilitate life management. Apparently, the development of GT life management is still riddled with several limitations, which call for more improvement. [ABSTRACT FROM AUTHOR]

This paper introduces a distributed dynamic event-triggered (DDET) controller meticulously crafted to achieve resilient consensus control in singular Markov jumping multi-agent systems (SMJMAs) even in the face of periodic Denial-of-Service (DoS) jamming attacks. With a dual focus on optimizing network resource utilization and fortifying against cyber threats, we propose a distributed dynamic event-triggered scheme (DDETS) grounded in the principles of sampled data consensus. In the presence of cyber attacks, we demonstrate the transformation of the original consensus control challenge within the singular multi-agent system into a robust stability control problem, centering around a singular switching error system governed by the DoS attack signal. Acknowledging the disturbances that typify real-world scenarios, this paper establishes criteria for attaining exponential stability and stochastic admissibility within the singular Markov jumping switching error system under the extended dissipative performance index. Furthermore, we present a hybrid design for the consensus controller and DDETS, delineated through a set of solvable linear matrix inequalities (LMIs). Finally, a comprehensive array of numerical examples meticulously illustrates the compelling effectiveness and unequivocal superiority of our proposed methodology. • A novel distributed dynamic event-triggered scheme under DoS attacks is proposed. • Exponential consensus for singular Markov multi-agent systems is achieved. • An extended dissipative consensus controller is designed. [ABSTRACT FROM AUTHOR]

Long time series forecasting is widely used in areas such as power dispatch, traffic control, and weather forecasting. The pattern of seasonality and trends in long time series are often complex, especially when they are presented at different time scales. Existing methods typically focus on only one scale or randomly select scales, which leads to a significant loss of valuable information. Additionally, current methods often transform multi-channel data into a single-channel format, ignoring interactions and complex relationships between channels. The paper proposes MultiWaveNet, a novel long time series forecasting framework that addresses seasonality as well as trends separately. For the seasonal component, the framework uses multi-scale wavelet decomposition to generate subseries at multiple scales. A learnable optimization factor is introduced simultaneously to separate high-frequency components mixed in low-frequency series after wavelet decomposition. In order to reduce information redundancy and model complexity, the paper develops a wavelet domain sampling encoder that consists of just one Transformer encoder, ensuring effective modeling of long-term dependencies while maintaining feature extraction effectiveness. As for the trend component, unlike previous research, the weights of channels are adjusted based on their importance, allowing the more crucial channels to have a greater impact and thereby addressing the limitations of individual processing methods. The paper performs extensive experiments on nine standard datasets, demonstrating that MultiWaveNet is the most competitive method. • Learnable optimization factor eliminate high-frequency information mixed in the low-frequency subseries. • Developed a wavelet-domain sampling encoder incorporating only the Transformer Encoder. • An attention-based channel-aware module was designed. [ABSTRACT FROM AUTHOR]

In deep reinforcement learning, experience replay is usually used to improve data efficiency and alleviate experience forgetting. However, online reinforcement learning is often influenced by the index of experience, which usually makes the phenomenon of unbalanced sampling. In addition, most experience replay methods ignore the differences among experiences, and cannot make full use of all experiences. Especially many "near"-policy experiences relatively relevant to the current policy are wasted, despite of the fact that they are beneficial for improving sample efficiency. This paper theoretically analyzes the influence of various factors on experience sampling, and then proposes a sampling method for experience replay based on frequency and similarity (FSER) to alleviate unbalanced sampling and increase the value of the sampled experiences. FSER prefers experiences that are rarely sampled or highly relevant to the current policy. FSER plays a critical role to balance the experience forgetting and wasting problems. Finally, FSER is combined with TD3 to achieve the state-of-the-art results in multiple tasks. • This paper formulates the sampling probability, and analyzes the monotonic influence. • Three optional sampling strategies based on sampling frequency are designed. • A sampling strategy based on similarity is designed. • FSER (ER based on frequency and similarity) algorithm is proposed. • FSER outperforms the state-of-the-art results on multiple tasks. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of secure synchronization for coupled dynamic networks subject to two types of deception attacks. Deception attacks are considered in both the controller-to-actuator and sensor-to-controller communication channels and modeled as Bernoulli distributions. The same and different attack probabilities of each node in dynamic networks are considered, respectively. A novel secure impulsive controller is introduced, mitigating malicious information from deception attacks and ensuring bounded synchronization. The average impulsive interval (AII) method is employed to relax the constraints on the upper and lower bounds of consecutive impulses, which is not adequately addressed by the previous secure impulsive control methods. Additionally, the paper establishes an upper bound for synchronization errors and proposes a method to design the AII based on attack probabilities. Finally, two numerical examples are provided to show the validity of our control approach. • Two types of attacks are simultaneously considered. • The results ensure the uniform boundedness synchronization of coupled DNs. • No requirement on the lower/upper bounds of consecutive impulsive signals. [ABSTRACT FROM AUTHOR]

Representation based classification (RC) algorithms have been successfully applied to pattern classification. However, most existing RC algorithms are not robust to bad training samples, since they ignore learning more discriminative dictionary atoms and instead directly use training samples as dictionary atoms. In addition, in order to improve expression ability and recognition accuracy, RC algorithms often need to expand the number of dictionary atoms, resulting in a surge in storage and computing costs. To obtain a more discriminative and compact dictionary, this study proposes discriminative dictionary learning for nonnegative representation based classification (DDLNRC). Specifically, in the DDLNRC, this paper utilizes nonnegative constraint to obtain a nonnegative representation for each training sample on the dictionary. In the dictionary learning stage, for a training sample, the DDLNRC minimizes the intra-class reconstruction error of the training sample and simultaneously enlarges the distance between the training sample and the atom that has the most influence on inter-class reconstruction error. Experiments demonstrate the effectiveness of the DDLNRC. Combined with the deep neural network features, it also can achieve higher accuracy than using the Softmax. • This paper proposes a DDLNRC for pattern recognition. • The DDLNRC can minimize intra-class reconstruction error. • The DDLNRC also can enlarge the inter-class reconstruction error. • By DDLNRC, a discriminative and compact dictionary can be obtained. • Experiments demonstrate the effectiveness of the DDLNRC on pattern recognition. [ABSTRACT FROM AUTHOR]

The detection of epileptic seizures is key for neurologists to initiate the right treatment at the earliest. However, the traditional methods are dependent on manual diagnosis which are error-prone. Hence, the current article presents an automated machine learning-based approach with competing results to detect seizures and its types using traditional features. Towards this goal, the present paper explores the performance of two fractal parameters namely Higuchi and, Kat'z fractal dimension, along with power spectral density and spectral entropy to detect EEG signals with seizure and non-seizure characteristics. To validate the performance via cross-validation technique, the features are extracted and experimented from three publically available databases such as Bern-Barcelona (focal, non-focal), Khas (preictal, interictal, ictal), and Bonn. Thereafter evaluated with different machine learning algorithms like Random Forest classifier, Ada Boost classifier, Gradient Boosting classifier, Extra Tree classifier, SVM classifier, and Naive Bayes classifier. The experiments showed 100% F1 score and accuracy in classifying focal and non-focal signals for random forest and extra tree classifier methods. To the best of our knowledge, this is the first paper reporting this score for the Bern-Barcelona dataset. The same features are also experimented for the detection of interictal, ictal and preictal scenarios. In the process, 100% accuracy is obtained in classifying interictal and ictal EEG signals, and an accuracy of 94% is attained in classifying interictal-preictal signals. This score is 14% higher than the current state-of-the-art methods using the same database. The features are also used in the Bonn database, where the proposed approach gave an accuracy of 100% same as the state-of-the-art methods. • Obtained competing evaluation score relative to SOTA methods using ML methods. • Competing score reported for interictal-preictal based classification of EEG data. • EEG classification is performed in the sensing time domain itself. • The proposed approach is validated using three benchmark datasets. • The performance is evaluated using cross-validation technique. [ABSTRACT FROM AUTHOR]

Information granulation theory has been widely used in short-term time-series forecasting research and holds significant weight. However, the error accumulation due to the lack of granular accuracy, along with information redundancy or deficiency in predictions, significantly affects short-term prediction accuracy. To compensate for these shortcomings, this paper proposes a double-level optimal fuzzy association rules prediction model for short-term time-series forecasting, which can strengthen the performance of information granulation in prediction. Firstly, this paper proposes a concept of breakpoints, which can accurately segment complex linear trends in time series and thus obtain a granular time series with highly accurate linear fuzzy information granules (LFIGs). Secondly, a improved distance is proposed to more accurately reflect the similarity between LFIGs by addressing counter-intuitive problems in the original distance. Theoretical analysis shows that the improved distance can effectively reduce errors in granular calculation. Then, a granule-suited fuzzy c-means algorithm is proposed for clustering LFIGs. Finally, this paper proposes a double-level optimal fuzzy association rules prediction model, which establishes the optimal rules for each cluster and selects the optimal two rules for prediction by the contribution of the clusters. The experimental results show that the prediction method effectively avoids the problems of information redundancy and information deficiency, and increases forecast accuracy. The model's exceptional performance is demonstrated through comparative analysis with existing models in experimental investigations. • A new concept of breakpoints is proposed to partition time series linear trends. • A DTW-iL1 distance is proposed to measure the similarity between granules. • DTW-iL1 Fuzzy C-Means(DFCM) is proposed for clustering granules. • Propose double-level optimal fuzzy association rules prediction model based on DFCM. [ABSTRACT FROM AUTHOR]

The emergence of artificial intelligence (AI) and its related capabilities has led industries to rethink the existing practices of conventional supply chain management and data analysis. Machine learning (ML), Deep Learning (DL) and their unique ability to predict future data and classify data have led to important research in the supply chain (SC) domain, particularly in identifying and prioritizing supply chain risks. This paper proposes several DL methodologies to exploit the benefit of DL, particularly to identify whether any product will be delivered late due to any unforeseen reason in a complex SC system. Four different DL architectures (Simple-LSTM, Deep-LSTM, 1D-CNN, and TCN-1DSPCNN models) are proposed to extract features, while six variant classifiers: Softmax, random trees (RT), random forest (RF), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM), were used to classify delay or non-delay information. By seamlessly capturing intricate temporal dependencies, these DL models enhance accuracy in robustly identifying supply chain late orders. Leveraging their hierarchical feature learning, these proposed DL models excel in recognizing subtle patterns and correlations, making them ideal for classifying late orders within the supply chain. Their parallel processing prowess facilitates real-time decision support, allowing organizations to address potential delays and allocate resources effectively and proactively. Five-fold cross-validation is presented to avoid over-fitting and to prove the efficiency of the proposed DL models. The total accuracies of the six ML classifiers are 74.03, 75.81, 93.35, 87.72, 93.59, and 95.10, respectively, while the maximum accuracies obtained from four proposed DL methodologies obtained an accuracy of 97.6, 98.63, 100, 100% respectively using the SVM classifier for predicting late orders based on five-fold cross-validation. • This paper investigates a few DL approaches to extract features of SC data. • Both RNN and CNN are applied in the same model. • An improved CNN model has been proposed for feature extraction. • An online dataset is employed to validate the proposed DL architectures. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of multi-institutional regionalization of primary health care units. The problem consists of deciding where to place new facilities, capacity expansions for existing facilities, and demand allocation in a multi-institutional system to minimize the total travel distance from demand points to health care units. It is known that traditional exact methods as branch-and-bound are limited to solving small- to medium-size instances of the problem. Given that real world-instances can be large, in this paper we propose an iterated greedy algorithm with variable neighborhood descent search for handling large-scale instances. Within this solution framework, several methods are developed. A greedy constructive method and two deconstruction strategies are developed. Another interesting component is the exact optimization of a demand allocation subproblem that is obtained when the location of facilities is previously fixed. An empirical assessment using real-world data from the State of Mexico's Public Health Care System is carried out. The results demonstrate the effectiveness of the proposed metaheuristic in handling large-scale instances. [ABSTRACT FROM AUTHOR]

Multiple Instance Learning (MIL) is a learning paradigm, where training instances are arranged in sets, called bags, and only bag-level labels are available during training. This learning paradigm has been successfully applied in various real-world scenarios, including medical image analysis, object detection, image classification, drug activity prediction, and many others. This survey paper presents a comprehensive analysis of MIL, highlighting its significance, recent advancements, methodologies, applications, and evolving trends across diverse domains. The survey begins by explaining the core principles that form the basis of MIL and how it differs from traditional learning approaches. This sets the foundation for comprehending the distinct challenges and techniques of solving MIL problems. Next, we discuss how supervised learning algorithms are tailored to support MIL and combine this discussion with a review of seminal MIL algorithms as well as the latest innovations that incorporate neural networks, deep learning architectures, and attention techniques. This comprehensive analysis helps to understand the strengths, limitations, and adaptability of these methods across diverse data modalities, complexities, and applications. In summary, this survey paper provides an essential resource for researchers, practitioners, and enthusiasts seeking a comprehensive understanding of Multiple Instance Learning. It covers foundational concepts, traditional methods, recent advancements, and future directions. By providing a holistic view of MIL's dynamic landscape, this paper aims to inspire further innovation and exploration in this ever-evolving field. • A survey on the current state of the Multiple Instance Learning. • We provide applications of Multiple instance learning in various domains. • We discuss how existing supervised learning algorithms are modified to support MIL. • Publicly available datasets and open research challenges in MIL are discussed. [ABSTRACT FROM AUTHOR]

The surveillance or monitoring of places is crucial to ensuring security, protecting people and assets, preventing crimes, and detecting emergencies, to mention some. Unmanned Aerial Vehicles (UAVs) play a vital role in these applications, offering versatility, agility, and aerial vision. A crucial step for such tasks is to protect the UAV path ahead. This paper focuses on a methodology harnessing the unpredictable nature of chaotic systems to generate trajectories around a closed area or contour. However, although a vast quantity of research papers mention the use of chaotic path generation, they have yet to learn about the control system and the dynamics affecting the UAV, where developing the control theory is challenging. In this paper, we design controllers based on predetermined-time stability, ensuring the achievement of the desired trajectory before a specified time. Additionally, adjusting control parameters is a crucial step during the control design, impacting the control performance. Hence, we present a method to optimize and adapt controller parameters through evolutionary optimization, demonstrating precision enhancement. We validate the proposed system's performance and the controllers through numerical simulations, indicating that the UAV effectively and accurately follows some types of chaotic trajectories like a square contour, aiming at the feasibility of this methodology in real UAV surveillance applications. • Design of Predefined-Time Control (PTC) for chaotic trajectory tracking with UAVs. • Generation of complex and unpredictable trajectories based on chaotic systems. • Optimization of controller parameters by Differential Evolution metaheuristic. • Lyapunov analysis for the design and convergence of Predefined-Time Controllers. [ABSTRACT FROM AUTHOR]

The existing feature selection algorithms often utilize local structure of data, but do not fully mine internal structure and ignore potential correlation information between samples. To address the problems and fully utilize manifold information of data, this paper proposes non-convex feature selection based on feature correlation representation and dual manifold optimization (FDNFS). Firstly, FDNFS constructs feature graph of original data, which can obtain feature correlation representation to represent the interconnection information. Based on the obtained interconnection information, FDNFS unifies feature correlation representation learning and feature selection through feature transformation matrix, so that the interconnection information between data guides feature selection. Secondly, to make multivariate frameworks guide feature selection, FDNFS introduces self-representation on the improved sparse regression model. Using self-representation can make basis matrix and reconstruction coefficient matrix reconstruct original data more accurately. Next, in order to preserve local structure information abundantly, FDNFS has two-part manifold regularization on the pseudo-label matrix in sparse regression model and the reconstructed coefficient matrix in self-representation framework. This can fully use the manifold information of data. In addition, FDNFS imposes the non-convex constraints. It can ensure the sparsity of feature selection matrix. In turn, this can select features with lower redundancy, and then select a better feature subset. Finally, this paper adopts an iterative optimization method. FDNFS is compared with nine algorithms on seven datasets. The clustering results reflect better performance of FDNFS. • Feature correlation representation and dual manifold optimization is proposed. • It constructs feature graph to represent the interconnection information. • It unifies feature correlation representation learning and feature selection. • It introduces self-representation on the improved sparse regression model. • It has two-part manifold regularization on the pseudo-label matrix. [ABSTRACT FROM AUTHOR]

The research on bipedal robots with limited foot area is gaining increasing attention. To tackle the challenge of dealing with unknown disturbances in the environment, the adjustment of footstep placement plays a vital role in maintaining stable motion during bipedal walking. This paper introduces an innovative approach based on the relationship between the Divergent Component of Motion (DCM) and footstep. It utilizes a DCM prediction model to optimize the optimal speed for recovering the foothold position. The goal is to enable quick and relevant footstep selection for bipedal robots, thereby facilitating the swift recovery of robot speed. The paper provides insights into the process of designing the desired DCM for achieving an optimal average walking speed without relying on predefined footstep sequences. By establishing a state equation between the DCM and footstep placement, this approach enables the prediction of multiple footstep positions within a fixed walking cycle, thereby facilitating the desired average motion speed. Extensive numerical simulations are conducted to compare the proposed method with various conventional footstep adjustment algorithms. The results emphasize our method's ability to converge more rapidly to the target speed, even with minor step adjustments. To validate the feasibility and robustness of the algorithm, we conduct experiments on the bipedal robot BHR-B2. These experiments further confirm the algorithm's effectiveness. Given its promising performance, this algorithm holds potential for applications in legged robots with point feet. • The adjustment of the footstep is crucial for the speed recovery of bipedal walking. • A multi-step prediction footsteps adjustment algorithm based on DCM is proposed. • The desired speed of motion is mathematically related to the target DCM state. • Three adjustment algorithms were compared through simulation. • Algorithms have been applied and effectively validated on a bipedal platform. [ABSTRACT FROM AUTHOR]

In 2019, a chaotic image encryption scheme based on a variant of the Hill cipher (VHC-CIES) was proposed by the Moroccan scholars. VHC-CIES introduces a Hill cipher variant and three improved one-dimensional chaotic maps to enhance the security. In this paper, we conduct a comprehensive cryptanalysis, and find that VHC-CIES can resist neither chosen-plaintext attack nor chosen-ciphertext attack due to its inherent flaws. When it comes to chosen-plaintext attack, firstly, we select a plaintext with the pixel values are all 0 and its corresponding ciphertext, and then use algebraic analysis to obtain the equivalent key stream for cracking VHC-CIES. Secondly, we select a plaintext which the pixel values are invariably 1 and obtain its corresponding ciphertext to obtain some Hill cipher variant parameters of VHC-CIES. Finally, we use the resulting steps of the first two to recover the original plain image from a given target cipher image. Similarly, a chosen-ciphertext attack method can also break VHC-CIES. Theoretical analysis and experimental results show that both chosen-plaintext attack and chosen-ciphertext attack can effectively crack VHC-CIES with data complexity of only O (2). For color images of size 256 × 256 × 3 , when our simulation encryption time is 0.3150 s, the time for complete breaking by chosen-plaintext attack and chosen-ciphertext attack is about 0.6020 s and 0.9643 s, respectively. To improve its security, some suggestions for further improvement are also given. The cryptanalysis work in this paper may provide some reference for the security enhancement of chaos-based image cryptosystem design. [ABSTRACT FROM AUTHOR]

In the context of early childhood education, students need to acquire fundamental writing skills for their lifelong development. Public schools, especially in low- and middle-income countries, should monitor individual student progress to mitigate the detrimental effects of socioeconomic vulnerabilities in education. Furthermore, the volume of students often overwhelms teachers responsible for assessing handwriting texts and providing feedback. This article conducts a Systematic Literature Review (SLR) focusing on solutions for automatically evaluating students' handwriting, discussing their performance, future research directions, and areas needing further investigation. We used a mixed-methods approach to conduct our SLR, encompassing a search across four databases (ACM Digital Library, IEEE Xplore, ScienceDirect, and SpringerLink) and employed the snowballing technique. We used the initial set of papers from the database search as the foundation for the subsequent snowballing search. Findings revealed that the studies introduced computational techniques, examined or enhanced existing methods, and developed assessment tools. These solutions predominantly employed techniques such as artificial neural networks and used available datasets comprising handwritten images, answers, or student essays. Only some studies provide low-cost solutions for automatically assessing the writing abilities of underserved public school students. • We reviewed 491 papers, extracting data from 22 focusing on handwriting assessment. • We applied a mixed method using database search and the snowballing technique. • Studies frequently adopt deep learning methods to tackle the handwriting recognition problem. • Studies employ various methods to automatically assess text production, such as ANN, latent semantic analysis, content vector analysis, and CNN. • The mean accuracy for handwriting assessment was notably high at 93.64%, suggesting that the models in this group exhibited good performance. [ABSTRACT FROM AUTHOR]

Adversarial attacks can mislead deep learning models to make false predictions by implanting small perturbations to the original input that are imperceptible to the human eye, which poses a huge security threat to computer vision systems based on deep learning. Physical adversarial attacks, which is more realistic, as the perturbation is introduced to the input before it is captured and converted to a image inside the vision system, when compared to digital adversarial attacks. In this paper, we focus on physical adversarial attacks and further classify them into invasive and non-invasive. Optical-based physical adversarial attack techniques (e.g. using light irradiation) belong to the non-invasive category. The perturbations can be easily ignored by humans as the perturbations are very similar to the effects generated by a natural environment in the real world. With high invisibility and executability, optical-based physical adversarial attacks can pose a significant or even lethal threat to real systems. This paper focuses on optical-based physical adversarial attack techniques for computer vision systems, with emphasis on the introduction and discussion of optical-based physical adversarial attack techniques. [ABSTRACT FROM AUTHOR]

This paper proposes physics-informed meta-learning-based surrogate modeling (PI-MLSM), a novel approach that combines meta-learning and physics-informed deep learning to train surrogate models with limited labeled data. PI-MLSM consists of two stages: meta-learning and physics-informed task adaptation. The proposed approach is demonstrated to outperform other methods in four numerical examples while reducing errors in prediction and reliability analysis, exhibiting robustness, and requiring less labeled data during optimization. Moreover, compared to other approaches, the proposed approach exhibits better performance in solving out-of-distribution tasks. Although this paper acknowledges certain limitations and challenges, such as the subjective nature of physical information, it highlights the key contributions of PI-MLSM, including its effectiveness in solving a wide range of tasks and its ability in handling situations wherein physical laws are not explicitly known. Overall, PI-MLSM demonstrates potential as a powerful and versatile approach for surrogate modeling. • A proposed approach uses meta-learning and PIDL to train surrogates with small data. • A model-agnostic meta-learning model is introduced to learn surrogate model weights. • A PIDL approach is introduced to adapt to target tasks with meta-learned weights. • Our approach outperformed other methods in numerical examples given limited data. • Proposed approach showed robustness in solving out-of-distribution tasks. [ABSTRACT FROM AUTHOR]

• Comprehensive CHP unit scheduling formulation. • Quality assurance through optimal gap evaluation. • Two-stage stochastic approach with MINLP. • Uncertainty management in unit commitment. • Modeling of electrical and thermal ramp constraints. Renowned for their remarkable overall efficiencies ranging from 70% to 90%, combined heat and power systems stand as a pivotal strategy for optimizing energy consumption by capitalizing on the synergistic relationship between electricity and thermal energy production. However, achieving optimal performance in combined heat and power systems remains a formidable challenge due to the intricate interplay of numerous variables. This paper presents a novel approach to daily scheduling optimization for combined heat and power units, focusing on the integration of electrical and thermal storage systems and meticulous consideration of security constraints. The optimization of combined heat and power unit scheduling introduces a mixed-integer nonlinear programming challenge, replete with deterministic and random variables. Addressing this complexity requires resilient solutions. In this study, we employ a multilevel optimization technique, transforming the problem into a bilevel structure. The initial step involves mapping operating parameters and minimizing costs through a water cycle optimization algorithm, laying a robust foundation for combined heat and power unit operation. Subsequently, we immerse ourselves in the realm of stochastic contingency scenarios, acknowledging the myriad uncertainties inherent in real-world systems. To empirically validate the efficacy of our proposed algorithm, extensive simulations are conducted on IEEE 18-bus and 24-bus test systems that emulate practical power networks. The results unequivocally showcase the power of our approach in navigating the complexities of optimal CHP unit planning. This paper's contributions lie in its innovative multilevel optimization technique, adeptly addressing both deterministic and stochastic aspects, ultimately paving the way for increased energy efficiency and enhanced system reliability in practical applications. [ABSTRACT FROM AUTHOR]

With the wide application of vision in the fields of autonomous driving and medical imaging, the demand for overexposure image correction algorithms is becoming increasingly urgent. However, existing overexposure image correction algorithms can lead to problems such as blurring, color bias, and over-enhancement of the generated images. Optimizing overexposure image quality has a significant impact on improving system performance, accuracy, and safety. In this paper, we propose an overexposure image correction network. First, we built the Detail Enhancement Module (DEM). It adopts global average pooling on each channel of the input feature map. After pooling, an activation function is used for nonlinear mapping to generate a channel attention weight vector. And it is multiplied with the original input feature map to achieve the purpose of enhancing the details of the overexposed image. Second, we construct a context-aware backbone (CAB) to extract features such as color and texture. The linear attention gating mechanism replaces the multi-head attention module in Transformer, and reduces the computational complexity in high-resolution images while maintaining performance by learning linear transformation and attention gating. Finally, we design an attention-guided feature fusion (AGFF) to fuse shallow and deep features. It computes weight vectors for shallow features through an attention module. The calculated result is converted to the same dimension as the input feature by bilinear interpolation, so as to enrich the semantic information and detailed information of the generated image. In addition to designing the network structure, we design a hybrid loss function to improve the quality of the generated image from the spatial and structural aspects, and the exposure function can correct the exposure degree of the generated image. Experiments are conducted on two public datasets and the dataset in this paper. Specifically, the PSNR and SSIM of images generated on the dataset MSEC increased by 1.3813% and 5.56%. The PSNR and SSIM of images generated on the dataset SICE increased by 1.545% and 4.64%. The proposed method can effectively generate clear and high-fidelity images. • An end-to-end overexposure correction algorithm. • The context-aware networks is constructed to extract exposed feature information. • The exposure loss is employed to prevent over-enhancement. • The proposed model aims to generator normally exposed images. [ABSTRACT FROM AUTHOR]

The human-centric philosophy is considered a crucial development direction for smart Product-Service Systems (smart PSS). Smart PSS has effectively advanced the circular economy by enhancing user experience, incorporating servitization and digital servitization, and extending product lifecycle, among other key aspects of sustainable development. However, there is currently a lack of a reasonable, accurate, and actionable framework and methodology to express the human element, namely user profiling, within this philosophy. To address this gap, this paper utilizes statistical methods such as factor analysis, cluster analysis, and regression analysis, building upon the concept of traditional user profiling. The aim is to integrate the three prominent approaches of goal-oriented, scenario-based, and data-driven user profiling, with the goal of complementing each other and designing a top-level user profiling framework for smart PSS. Furthermore, Industry 4.0 technologies and text mining techniques are employed to collect data on users' product and service usage. As these data contain real-time information about user needs, behaviors and goals at different time periods, they can be used to construct dynamic features of user profiling, and ultimately achieve the construction of dynamic user profiling for smart PSS. To validate the proposed user profiling framework and dynamic user features of smart PSS, this paper presents a case study focusing on the user group of maternal women. This case study promotes the in-depth exploration of smart PSS research in expressing the human element. [ABSTRACT FROM AUTHOR]

Hot-rolled strip products have diverse applications, and enhancing the detection, diagnostics, and prognostics of product quality during hot rolling is essential. Nevertheless, the multivariable, strong coupling, nonlinear, and time-varying nature of the production process poses a rigorous challenge for accurate hot-rolled prognostics. This paper implements a progressive layered extraction (PLE) multi-task learning (MTL) framework to simultaneously estimate multiple quality indicators, such as strip crown, center line deviation, exit temperature, wedge, width, and symmetry flatness. Additionally, the paper proposes the implements of Hybrid Bayesian Neural Network (HBNN) experts and a gating network with attention mechanism to integrate private and shared task features. It also puts forth an auxiliary task involving a Variational Autoencoder with Generative Adversarial Networks (VAE-GAN) to extract latent states from the original sequence. Moreover, an adaptive joint loss optimization is employed to update the weight of individual task losses for MTL training problems, and three sets of field hot-rolled datasets are used for model evaluation. In the experimental validation, considering the noisy field data and limited conditions in the real hot rolled production, comparative experiments are conducted to demonstrate the improved generalization and robustness of the proposed MTL approach. These experiments involve different percentages of the total data, ranging from 5% to 20%, and various prediction horizons ranging from 1 to 50 steps for model establishment. In addition, the paper discusses the interpretation of the model and strategies for further enhancing model performance. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to aid game developers in finding the most suitable pathfinding algorithm for their games. Despite recent advancements in this field, there are few available studies that can be compared due to the absence of a standard benchmark set for weighted environments. This paper presents a new dataset for pathfinding in weighted environments. Furthermore, an investigation was conducted into the impact of node weights on pathfinding speed, and a correlation between them was identified. The complexity added to the maps due to node weights was defined as weight complexity, and two metrics were introduced to estimate it. The weight correlation factor has been identified as the most effective metric for estimating the weight complexity of the map. Another contribution of this paper pertains to the development of a model for estimating the pathfinding speed of algorithms based on weight complexity. This was accomplished through the utilization of the non-linear least squares method, which was applied to create a model for each algorithm, considering both its average search time and weight correlation factor values associated with the map. Finally an overall score metric was developed by using the integral of the models, enabling the evaluation of different algorithms in various scenarios. [Display omitted] • Testing JPSW (weighted jump point search) algorithm search speed. • Introducing the first standardized benchmark for weighted gridmaps. • Introducing a new metric for analyzing gridmap complexity. • Using edge detection techniques for weighted gridmap analysis. • Introducing a scoring system for pathfinding algorithms in weighted environments. [ABSTRACT FROM AUTHOR]

Keyphrase Extraction is an automatic task that involves identifying the key words or phrases that capture the main content of an article. It is useful for various downstream tasks, including text search, text clustering, and text classification. Embedding-based methods for keyphrase extraction have shown promising results by utilizing pre-trained language models to represent candidate phrases and documents separately. These methods then rank the candidate phrases based on the cosine similarity between the document and the candidate phrases embeddings. However, there are mainly two shortcomings in such methods: I) Redundancy errors, when there are partial repetitions of candidate keyphrases, the methods tend to use redundant long phrases as keyphrases; II) Low keyphrase coverage, such as some keyphrases used to describe locally important information are ignored. In this paper, we propose an unsupervised keyphrase extraction method called "MICRank", which evaluates the importance of candidate keyphrases from three perspectives: global information, local information, and attribute information, and solved the aforementioned issues. The experimental results on six benchmarks demonstrate that the proposed MICRank method outperforms the state-of-the-art unsupervised keyphrase extraction methods. In addition, this paper improves the judgment criterion of correct keyphrase extraction and introduces a new evaluation metric called S1@M (M ∈ {5,10,15}) to address the issue of synonyms being considered incorrect predictions. [ABSTRACT FROM AUTHOR]

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user's movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user's mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user's mobility traces, avoiding the random generation of users' traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices' batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider. [ABSTRACT FROM AUTHOR]

• The interval data effectively aggregated by the induced continuous aggregation operator. • The Shanghai composite index can be accurately predicted by the combination prediction model. • Thorough study of effectiveness theory of the combination prediction model has been completed. This paper proposes an interval combination prediction model for Shanghai composite index, utilizing the Theil coefficient and the induced continuous generalized ordered weighted logarithmic harmonic averaging (ICGOWLHA) operator. The effectiveness of the proposed model under specific weight conditions and the existence of its analytical solution are demonstrated. Shanghai composite index's case analysis demonstrates that, in terms of interval root mean squared error (IRMSE), interval mean absolute error (IMAE), interval mean absolute percentage error (IMAPE), and interval mean squared percentage error (IMSPE), the proposed model's predictive performance improvements over the best-performing single prediction model are 29.33%, 25.72%, 26.10%, and 28.86%, respectively. At the same time, the theoretical properties of the model are verified in the results of the case analysis, and the model's convergence is reflected in sensitivity analysis. Through extensive model comparisons, it is observed that the model proposed in this paper exhibits strong generalization, without specific limitations on data size or feature count. It demonstrates good aggregation prediction performance for interval data. Moreover, it is applicable to various fields, including finance, environment, and others. [ABSTRACT FROM AUTHOR]

Automatic humor detection has compelling use cases in modern technologies, such as humanoid robots, chatbots, and virtual assistants. In this paper, we propose a novel approach for detecting and rating humor in short texts based on a popular linguistic theory of humor. The proposed technical method initiates by separating sentences of the given text and utilizing the BERT model to generate embeddings for each one. The embeddings are fed to a neural network as parallel lines of hidden layers in order to determine the congruity and other latent relationships between the sentences, and eventually, predict humor in the text. We accompany the paper with a novel dataset consisting of 200,000 short texts, labeled for the binary task of humor detection. In addition to evaluating our work on the novel dataset, we participated in a live machine-learning competition to rate humor in Spanish tweets. The proposed model obtained F1 scores of 0.982 and 0.869 in the performed experiments which outperform general and state-of-the-art models. The evaluation results confirm the model's strength and robustness and suggest two important factors in achieving high accuracy in the current task: (1) usage of sentence embeddings and (2) utilizing the linguistic structure of humor in designing the proposed model. • A novel method for humor detection and rating based on a general linguistic theory of humor. • Introduced a very large novel dataset with 200k short texts for humor detection. • Achieved 98% accuracy and outperformed five strong baselines on the new dataset. • Demonstrated accuracy and robustness in a data science competition for Spanish texts. • The novel approach and dataset contributed to tens of research projects. [ABSTRACT FROM AUTHOR]

• The 2S attack matrix on the actual time sequence with the two loopings. • The precise forensics' timeline on the real case attack. • The in-depth technical details of the attacks within the energy sector. • The insight into the attacker's behaviors for network admins. • The protection begins with a clear understanding of the ransomware attack pattern. Due to the Coronavirus pandemic (COVID-19) throughout 2020–22, remote working has played an important part in organizations, businesses, and agencies worldwide. This situation makes the various cybersecurity threats the Internet poses, especially ransomware. Ransomware will remain the top cybersecurity threat, and the energy sector is the prime target. Previously, research papers only focused on the analytical and protection frameworks. These papers rarely provide real evidence and detailed digital forensics. Interestingly, the ransomware gangs developed new methods but still used similar attack patterns. The authors envisage that a precise understanding of the ransomware attack characteristics is a starting point for the correct detection process. This paper presents a true case study demonstrating the actual occurrence of digital forensics and in-depth technical details of the attacks within the energy sector. The significant attack patterns, which have never been emphasized in research papers, can be proposed for the two loops. The results led to a novel ransomware attack matrix with two loop patterns (dwell time factor) applicable to other ransomware gangs for the detection stage of the NIST. [ABSTRACT FROM AUTHOR]

Disruptions occurring at an urban rail transit (URT) system can severely affect its normal operations, and an effective bus bridging service (BBS) is able to help to reduce the negative effects. Transit operators usually arrange BBS to depart from the disrupted stations to evacuate the stranded passengers. However, the overload of passengers at the disrupted stations, especially at turnover and transfer stations, may incur the secondary operation disruptions such as stampede accidents. To mitigate the negative effects of disruptions and reduce the number of passengers stranded at the disrupted stations, the strategy based on the passenger guidance and extended BBS (E-BBS) is introduced in this paper. Different from the widely applied standard BBS running within the disrupted links, E-BBS runs among the normal operating stations or between the normal operating stations and the disrupted stations. A mixed integer linear programming model is developed in this paper to guide the stranded passengers and design an optimal E-BBS solution to transport them. Considering the bi-directional running trains along the disrupted links, a dynamic decision framework is developed to manage the disruptions. Given the impacts of the high uncertainty on bus running time which could affect the performance of E-BBS, a robust model is proposed to obtain more reliable travel guidance and E-BBS schemes. Numerical experiments based on Chengdu subway system in China are conducted. The results indicate that the proposed model can obtain optimal travel guidance and E-BBS solutions in a timely manner. When the uncertainty on bus running time is ignored, the total travel cost for affected passengers is reduced 14.20 % on average with the aid of the optimal travel guidance and E-BBS solutions. Moreover, the number of passengers gathering at the disrupted stations decreases by 36.80 %. The robust model can obtain more reliable travel guidance and E-BBS schemes in consideration of uncertain bus running time. The proposed model shows great potential to effectively mitigate the negative effects of disruptions and help to enhance the capability of a URT system to respond to disruptions. [ABSTRACT FROM AUTHOR]