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

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]

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]

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]

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]

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]

• Treat a problem of the imbalanced customer churn classification. • A new multi-strategy collaborative processing method IADASYN-FLCatBoost is proposed. • The traditional ADASYN algorithm is improved and an IADASYN algorithm is proposed. • The Focal Loss function is embedded into the CatBoost to form a new FLCatBoost. • Empirical analysis shows the proposed method performs better than similar methods. The rapid advancement of big data and artificial intelligence heralds a dual-edged era of opportunities and challenges for the banking sector. Indeed, enhancing a model's capability to accurately classify imbalanced datasets represents a critical challenge within the field of customer churn prediction (CCP). In this paper, to address the challenges presented by the problem of imbalanced customer classification, a new multi-strategy collaborative processing method named IADASYN-FLCatBoost is proposed from dual perspectives: data and algorithm. At the data level, the traditional Adaptive Synthetic (ADASYN) sampling is improved, that is, the LOF (Local Outlier Factor) algorithm is introduced to eliminate outliers, and the classification features are specially processed to synthesize new minority class samples, thus an improved ADASYN (IADASYN) algorithm is obtained. At the algorithm level, the Focal Loss is embedded into the CatBoost ensemble learning framework to form a new Focal Loss-CatBoost (FLCatBoost) to make a focal-aware, cost-sensitive version of imbalanced customer churn prediction. Moreover, the empirical analysis is conducted in conjunction with the credit card customer dataset obtained from the Kaggle platform. The results of the staged comparison experiments show that the proposed method IADASYN-FLCatBoost in this paper shows the best prediction performance. Comparing the proposed method with 5 other imbalanced classification algorithms and 20 classifiers composed of classical sampling methods and ensemble learning algorithms, it is verified that the classification effect of the proposed method performs best, and the values of Recall , F 1 score, G-mean and Area under Precision-Recall curve (AUPRC) have been significantly improved. In addition, further verification of the model also proves that the proposed method has certain generalizability and is still valid for other banks and customer churn datasets of other industries. [ABSTRACT FROM AUTHOR]

• Newbie study using standard ML techniques with HAR Application and Discussions. • Activities found in Literature with the corresponding reference. • Co-occurrences between activities and sensors with the corresponding reference. • Summary and comparison among the different datasets found in Literature. • Summary of the experimentation settings with performance scores found in Literature. Human Activity Recognition (HAR) is an essential area of research related to the ability of smartphones to retrieve information through embedded sensors and recognize the activity that humans are performing. Researchers have recognized people's activities by processing the data received from the sensors with Machine Learning Models. This work is intended to be a hands-on survey with practical's tables capable of guiding the reader through the sensors used in modern smartphones and highly cited developed machine learning models that perform human activity recognition. Several papers in the literature have been studied, paying attention to the preprocessing, feature extraction, feature selection, and classification techniques of the HAR system. In addition, several summary tables illustrating HAR approaches have been provided: most popular human activities in the literature with paper references, the most popular datasets available for download (Analyzing their characteristics, such as the number of subjects involved, the activities recorded, and the sensors with online-availability), co-occurrences between activities and sensors, and a summary table showing the performance obtained by researchers. =The paper's goal is to recommend, through the discussion phase and thanks to the tables, the current state of the art on this topic. [ABSTRACT FROM AUTHOR]

Roundwood sorting is still a manual process in many Swiss sawmills, requiring employees to visually inspect and categorize thousands of logs per day. The heavy workload can be both physically and mentally taxing and can lead to increased rates of human error. State-of-the-art automation systems like X-ray log scanners are expensive and difficult to integrate into existing process lines. This paper proposes a novel recommendation system that leverages recent advances in image classification to automate roundwood classification by quality and species. The system integrates a camera to capture cross-sectional images of logs and record numerical data, such as length, taper, and diameter. The analysis of the resulting dataset highlights the challenges of data imbalance and noise, which makes classification difficult and, in some cases, impossible. However, by using selected datasets with reduced noise, state-of-the-art Convolutional Neural Networks (CNNs) can extract quality and species features. Quality models learn from a manually selected and simplified dataset, featuring samples that experts can clearly classify based on the image's information. Species models are trained on a label-noise-reduced dataset, reflecting real-world complexity. The accuracy on the selected dataset for three quality classes is 80%. The species determination is less challenging and reaches 91% accuracy on a synchronized dataset for the main species spruce and fir. Overall, this paper highlights the potential of Machine Learning in augmenting the roundwood sorting processes and presents a novel system that can improve the efficiency and accuracy of the process. [Display omitted] • Automation of roundwood sorting: Replaces manual sorting with image-based AI. • Integrated camera system in roundwood sorting to collect labeled dataset. • Species prediction: 91% accuracy in spruce–fir distinction. • Quality prediction on complexity reduced dataset: 80% accuracy between three main quality levels. • Efficient, adaptable & scalable system which is easy to integrate into existing process lines. [ABSTRACT FROM AUTHOR]

The emergence of online social networks (OSN) platforms removes communication barriers that are essential to human life, catalyzing social networking growth. However, this emergence has given rise to a negative impact when someone abuses the platform to commit cyberbullying activities. Hence, it is crucial to work on automated cyberbullying-related classification to mitigate the societal phenomena in OSN. The research on the automated classification model for cyberbullying was pioneered over the last decade with growing interest among researchers. It is helpful to track its growth over the decades to elucidate the state-of-arts techniques applied in this field. This paper presents a large amount of literature germane to cyberbullying classification from past to present to provide a comprehensive review. A total of 126 papers were reviewed. This paper emphasizes text-based cyberbullying and multi-modal cyberbullying. The review was presented around the machine learning workflow, encompassing four core sections: dataset analysis, pre-processing analysis, feature analysis, and technique analysis. Based on the critical analysis, limitations are addressed along with the future works that can be conducted to fill the gap in previous research. Furthermore, the review also examined the ethical implications associated with the implementation of these techniques. This review paper is expected to assist readers in fully comprehending the current trend, architecture, and techniques applied to the field. [ABSTRACT FROM AUTHOR]

Truck drivers are essential to modern supply chains. However, the companies that employ them are having trouble keeping them. At many trucking firms, annual turnover rates approach 100 percent or higher. This paper develops a method for predicting individual truck driver turnover events before they happen by applying supervised machine learning classifiers to a new source of operational truck driver data. Our paper uses Electronic Logging Device (ELD) data, which comprise newly federally-mandated, time-stamped work logs collected from approximately 1200 American long-haul truck drivers over 3 years. We train three supervised machine learning classifiers (logistic regression, random forests, and support vector machines) on this data and achieve 60 to 70 percent prediction accuracy and 50 to 60 percent recall across two 5-fold cross-validated experiments. We observe that the quantity and consistency of week-day driving assignments explain most of our models' predictive power. We offer these results as both a new technical tool, as well as novel managerial insights for improving global truck driver retention problems for the benefit of supply chains worldwide. [Display omitted] • Newly available operational data from long haul truck drivers is analyzed. • Three machine learning classifiers are trained on the operational data. • Predictive models achieve 60–70 percent accuracy, 50–60 percent recall. • Models show potential to enable interventions to retain truck drivers. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

In this paper the performance of different Machine Learning and Deep Learning approaches is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of bike sharing demand in Madrid and Barcelona (Spain) is approached, for different prediction time-horizons, and also a problem of cable car demand forecasting in Madrid city. An important number of predictive variables are considered, which are grouped into four different sets (categorical/calendrical, persistence-based, meteorological and, as a novelty of the paper, information about analogue past instances), whose relevance is studied for all cases. A feature selection mechanism is also incorporated in order to improve the prediction accuracy of the proposed algorithms. A total of 12 different multivariate regression techniques are implemented, covering from Machine Learning methods to time-series Deep Learning approaches. Excellent results in all the prediction problems approached are reported. Finally, the consequences of obtaining accurate prediction in these three problem of green mobility in big cities are discussed. In addition, it is studied how the results could be exported to other similar cases in more general urban mobility studies. Novelties of the work include: (1) Addressing the forecast problem of passenger flow on a cable car using ML and DL multivariate techniques; (2) using the demand of analogous past instances as an additional feature to solve the demand prediction problems; and (3) the extraction of global conclusions about feature relevance when addressing a demand forecasting problem in green mobility. [ABSTRACT FROM AUTHOR]

The paper presents a machine learning based Smart Intrusion and Fault Identification (SIFI) method to identify the cyber-physical abnormalities in an inverter-based cyber-physical microgrid (CPM). The SIFI method utilizes an ensemble classifier (EC) to assimilate the decisions from three distinct classifiers (C4.5 decision tree, random forest (RF), and forest by penalizing attributes (FPA)). The proposed method employs a voting mechanism to classify and localize abnormal events for improved accuracy. The paper investigates the effects of cyberattacks (Denial-of-Service (DoS) and Malicious Data Injection (MDI) attacks) and physical abnormalities caused by line faults in microgrids. To train the classifiers, SIFI employs dataset with statistical attributes extracted from measurements. The renewable alternative power systems simulation (RAPSim) software tool is utilized to model the proposed system. The effectiveness of the presented model is assessed with respect to mean value error (MVE). The efficiency of the classifier is demonstrated by comparing its performance with other classifiers in terms of the MVE. For physical abnormalities identification and localization, the approach provides enhanced outputs with the MVE of 0.157 % and 0.162 % correspondingly. For identifying MDI anomalies, the model provides better results with a lower error rate. For DoS anomaly detection, the model provides better classification performance with 0.136 % error. The extensive empirical analysis proves that the anticipated ensemble classifier-based SIFI model yields the minimum MVE for identifying physical faults and cyberattacks. Furthermore, the effectiveness of the proposed method is identified by considering a case study in which the SIFI method outperforms well for the MDI and DoS cyber-attacks in the CPM. [ABSTRACT FROM AUTHOR]

In recent years, the application of the internet of things (IoT) in areas such as intelligent transportation, smart cities, and the industrial internet has become increasingly widespread. As a crucial supporting infrastructure, IoT devices are utilized in various fields to construct IoT networks. However, due to the inherent limitations of IoT devices, such as limited computing resources and low memory capacity, security concerns have become increasingly prominent. Among these concerns are Denial-of-Service (DoS) and botnet attacks, which are difficult to prevent due to their large-scale and covert nature. To address these challenges, this paper proposes a Hybrid DoS Attack Intrusion Detection System (HDA-IDS) that combines signature-based detection with anomaly-based detection to effectively identify both known and unknown DoS/botnet attacks. Additionally, this paper introduces a novel anomaly-based detection model called CL-GAN. It integrates CNN-LSTM with GAN to establish a baseline for normal behavior and detect malicious traffic. In contrast to other semi-supervised models, the CL-GAN exhibits superior accuracy, as well as shorter training and testing times, in detecting DoS and botnet attacks. In addition, experimental results demonstrate that the HDA-IDS outperforms other IDSs in detecting DoS and botnet attacks. When tested on datasets such as NSL-KDD, CICIDS2018, and Bot-IoT, the HDA-IDS achieved an average of 5% overall improvement superior performance in terms of accuracy, precision, recall, and F1-Score compared to other works. These results highlight the effectiveness of the proposed system in addressing security issues in IoT networks, and presents a general framework that addresses the challenge of large-scale attacks constructed through the dissemination of false information. [ABSTRACT FROM AUTHOR]

In the era of rapid paper publications in various venues, automatic citation recommendations would be highly useful to researchers when they write papers. Local citation recommendation aims to recommend possible papers to cite given local citation contexts. Previous work mainly computes the similarity score between citation contexts and cited papers on a one-to-one basis, which is quite time-consuming. We train a pair of neural network encoders that map citation contexts and all possible cited papers to the same vector space, respectively. After that, we index the positions of all cited papers in the vector space. This makes our process for searching recommended papers considerably faster. On the other hand, existing methods tend to recommend papers that are highly similar to each other, which makes recommendations lack diversity. Therefore, we extend our algorithm to perform multi-topic recommendations. We generate multi-topic training examples based on the index we mentioned earlier. Furthermore, we specially design a multi-group contrastive learning method to train our model so that it can distinguish different topics. Empirical experiments show that our model outperforms previous methods by a wide margin. Our model is also light weighted and has been deployed online so that researchers can use it to obtain recommended citations for their own paper in real-time. • Proposed FLCR algorithm with sentence-transformer & k-d tree. • Introduced multi-topic citation recommendation for diverse contexts. • Developed large-scale dataset with 1.7 million citation contexts for evaluation. • Demonstrated significant performance improvement over previous methods. • Deployed demo for real-time citation recommendations. [ABSTRACT FROM AUTHOR]

Constructing a deformation prediction model for dams that can accurately capture deformation trends is crucial to ensure their operational safety. The accurate monitoring data are critical to the presentation of the variation patterns of the monitored quantities. However, due to various disturbances, there can be a lot of noise in the raw deformation monitoring data acquired by the sensors. Therefore, the reduction of the noise level in the data sequence is necessary for the improvement of the accuracy and reliability of the prediction model. This paper presents data denoising techniques for the removal of outliers from data sequences, including complete ensemble empirical mode decomposition with adaptive noise, garrote threshold functions, and sample entropy. The ensemble learning algorithm was then employed to complete the deformation prediction of concrete dams. Through example analysis and comparison with a benchmark model, the proposed model's performance was validated, highlighting its effectiveness and superiority. Additionally, this paper enhances the model's interpretability by visualizing the optimization process. The degree of influence of the input factors on the deformation prediction results is also analyzed using the feature importance metric. The proposed model is expected to be applied to deformation monitoring of other civil and hydraulic structures, as it can effectively reduce the impact of noise on prediction. [ABSTRACT FROM AUTHOR]

Citation counts is a crucial factor in evaluating the quality of research papers. Therefore, it is vital to accurately predict citation counts and explore the mechanisms underlying citations. In this study, we focus on predicting the citation counts in the field of statistics. We collect 55,024 academic papers published in 43 statistics journals between 2001 and 2018. Furthermore, we collect and clean a high-quality dataset and then construct multi-layer networks from different perspectives, including journal networks, author citation networks, co-citation networks, co-authorship networks, and keyword co-occurrence networks. Additionally, we extract 77 factors for citation counts prediction, including 22 traditional and 55 network-related factors. To address the issues of zero-inflated and over-dispersed citation counts, a neural network model is designed to achieve high prediction accuracy. Furthermore, we adopt a leave-one-feature-out approach to investigate the importance of these factors. The proposed neural network model achieves an MAE value of 7.352, which outperforms other machine learning models in the comparison. Thus, this study provides a useful guide for researchers to predict citation counts and can be easily extended to other research fields. • Extracting factors based on multi-layer academic networks. • Extracting research topics using keyword co-occurrence network. • Using the neural network model to predict the citation counts. • Indicating network-related factors possess strong predictive. performance. • Measuring factor importance using the leave-one-feature-out method. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are currently the major causes of death globally. Among the strategies to prevent cardiovascular issues, the automated classification of heart sound abnormalities is an efficient way to detect early signs of cardiac conditions leading to heart failure or other, even asymptomatic, complications, quite effective for timely interventions. Despite the significant improvements in this field, there are still limitations due to the lack of solutions, available data-sets and poor (mainly binary — normal vs abnormal) classification models and algorithms. This paper presents a Medical Cyber–Physical System (MCPS) for the automatic classification of heart valve diseases onsite, in a timely manner. The proposed MCPS, indeed, can be deployed into personal and mobile devices, addressing the limitations of existing solutions for patients, healthcare practitioners, and researchers, through an efficient and easy accessible tool. It combines different neural network models trained on a new Italian dataset of 132 adult patients covering 9 heart sound categories (1 normal and 8 abnormal), also validated against two main open-access (Physionet/CinC Challenge 2016 and Korean) datasets. The overall MCPS performance (time, processing and energy resource utilization) and the high accuracy of the models (up to 98%) demonstrated the feasibility of the proposed solution, even with few data. The dataset supporting the findings of this paper is available upon request to the authors. • DB of 132 heart sounds of Italian patients in 9 categories, available upon request. • MCPS for automated HVD classification via hierarchical ML model structures. • Compared six ML classifiers (RF, SVM, NB, LR, DNN, LSTM) for HVD and majority voting. • MCPS validated on two open access datasets (Physionet/CinC 2016 and Korean). • Time, processing and energy efficiency of MCPS validated, with usage guidelines. [ABSTRACT FROM AUTHOR]

Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage. • Comparison of Machine Learning and Cointegration in SHM. • Consideration of damages and false alarms. • Usage of a reduced scale structure to reproduce the behavior of a bridge column. • Cointegration is the fastest to detect damage. [ABSTRACT FROM AUTHOR]

Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The overall goal of the FTC is to accommodate defects in the system components while they are in use and maintain stability with little to no performance reduction. These systems are crucial for mission-critical and safety-related applications where the safety of people is at stake and service continuity is crucial. In this review paper, a systematic study has been done for the development of FTC with machine learning, deep learning, and transfer learning algorithms. The challenges and limitations faced with their possible solutions through machine learning theories for the IFTC model are lined up. This paper guides researchers on the different possible types of machine learning algorithms and their advanced forms like deep learning and transfer learning. The differences among these are highlighted by the challenges and limitations of each. The paper is significant such that most of the important literature references from the Scopus database particularly related to important electrical and mechanical industrial problems have been discussed to guide the researchers who want to apply IFTC for specific industrial problems, being the research gap. Finally, future research directions for the development of IFTC are highlighted. [ABSTRACT FROM AUTHOR]

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserve the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localisation, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring. • Marine environments and fish habitats are widely surveyed using underwater cameras. • Deep learning (DL) offers an effective solution for processing underwater videos. • We write a tutorial on the key concepts and steps of DL for underwater monitoring. • We provide a survey of key DL-based visual fish monitoring methods/datasets. • We shed light on challenges/opportunities of DL for underwater fish monitoring. [ABSTRACT FROM AUTHOR]

Flight diversions due to adverse weather have a significant negative impact on airspace users, airports, and the environment. Previous work proposed a tree-based model that learned which flights are more likely to be diverted due to adverse weather conditions at the destination airport from historical traffic and weather data. This model was trained using confident learning to exclude from training unpredictable flight diversions caused by reasons other than weather, such as medical emergencies or technical problems. Aggregated performance metrics computed on the test set demonstrated high precision and moderate recall, and inspection of the Shapley values unravelled the most important features. Initial human observations made during the live trial, however, have raised a potential connection between the quality of predictions and the specific weather conditions that lead to flight diversions. This study aims to provide empirical evidence to support this hypothesis. To accomplish this, a data-driven methodology should be used to characterise the main conditions that cause flight diversions. Concurrently, the effectiveness of the model in each of these conditions should be evaluated. The dataset under consideration, however, presents a challenge because it contains a plethora of high-cardinality categorical features (e.g., destination airport and aircraft operator) as well as skewed numerical features with a wide range of scales (e.g., visibility and wind gust). This scenario significantly increases the difficulty of characterising the main conditions that cause flight diversions and conducting subsequent analyses even with established methods like factor analysis of mixed data. To address this challenge, this paper proposes to utilise supervised clustering, a method that combines state-of-the-art feature attribution, dimensionality reduction, and clustering algorithms to identify the most representative decisions of a model. Following that, the distinctive characteristics of the various decisions (e.g., extreme obscuration at the airport), each associated with a cluster, are determined by examining them. Finally, conventional classification and uncertainty metrics are computed for each cluster to assess the performance of the model. This paper shows that supervised clustering is effective in characterising flight diversions due to weather, and provides valuable insights into the performance of the model in different conditions, highlighting situations where predictions require careful consideration. • Flight diversions due to adverse weather conditions could be predicted with machine learning. • Supervised clustering effectively identifies the common weather conditions causing flight diversions. • Flight diversions in low visibility and windy conditions are highly predictable. [ABSTRACT FROM AUTHOR]

In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learning algorithms designed for modelling Survival Analysis data. The presented implementation uses a matrix-wise formulation, which allows to speed up the execution time. Additionally, SurvLIMEpy assists the user with visualisation tools to better understand the result of the algorithm. The package supports a wide variety of survival models, from the Cox Proportional Hazards Model to deep learning models such as DeepHit or DeepSurv. Two types of experiments are presented in this paper. First, by means of simulated data, we study the ability of the algorithm to capture the importance of the features. Second, we use three open source survival datasets together with a set of survival algorithms in order to demonstrate how SurvLIMEpy behaves when applied to different models. • Python package implementing SurvLIME algorithm for computing feature importance. • It supports a wide variety of survival models, including deep learning models. • Fast and efficient implementation due to matrix-wise optimisation problems. • An open-source implementation available on GitHub. • Stable release provided to PyPI. [ABSTRACT FROM AUTHOR]

• Pinch force strength is accurately predicted using sEMG. • Different pinch movements are accurately classified using sEMG. • Similarity of EMG features is evaluated. • An EMG feature-to-image method is proposed. The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning. The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R 2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers. The proposed methodology has the potential to realize simultaneous force and movement control of prosthetic hand. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed machine learning method to protect users' privacy and security. It currently faces the following two problems (McMahan et al., 2017): (1) The performance of the global model degrades when dealing with Non-Independent Identically Distributed (Non-IID) data. Other existing classical methods do not have a rigorous theory based on error processing ; (2) In the process of global model aggregation, the classical FL algorithm either directly averages local models or solely considers the proportion of local data to assign weights to the models, without accounting for the discrepancies between the local models. In this paper, a federated aggregation algorithm Federated Maximum Mean Discrepancy (FedMMD) is proposed to address the deviation between the local models and Non-IID. First of all, this paper utilizes the Dilated Convolution Meet Transformer (DCMT) model for local model feature extraction. This approach aims to capture more feature information and minimize the impact of Non-IID scenarios. Secondly, the learning stability of the local model data participating in the aggregation is compared with Maximum Mean Discrepancy (MMD). The weights of the local models involved in the aggregation are determined using the SKNQ (Student–Keuls–Newman-Q) method and the entropy weight method. The SKNQ method calculates and compares the MMD across multiple clients, while the entropy weight method assigns different weights to clients based on their deviations. On the standard dataset, the FedAvg algorithm (McMahan et al., 2017), the FedProx algorithm (Li et al., 2020) and the FedMMD algorithm are compared. The experimental results demonstrate that the FedMMD algorithm, used in training the global model, not only enhances the accuracy of learning IID and Non-IID data but also improves the generalization capability of the global model. • An DCMT model is proposed to alleviate the model fluctuations caused by NonIID data. • An MMD method is proposed to compared horizontally before the model is aggregated. • Entropy weight method and the SKNQ method are combined to allocate local models' weight when they are fused into global model. [ABSTRACT FROM AUTHOR]

With the wide application of the shield tunneling method in tunnel engineering, the untimely and incorrect attitude control of shield systems has become an essential factor affecting the quality of shield tunneling. The use of multi-agent deep reinforcement learning (MADRL) algorithm to learn correct and timely shield attitude correction policies is beneficial for handling shield attitude control tasks which are complex and separable. However, the sensing data of the shield systems are complicated and widely distributed, which affects the learning efficiency of the agents. To resolve this problem, this paper proposes a MADRL framework based on state classification and assignment (SCA) for adaptive shield attitude control. SCA-MADRL uses a clustering algorithm to classify states and uses a planning model to make the agent's learning space a consistent state space so that each agent can learn efficiently in its own learning space. The state classification algorithm in the framework can improve the learning efficiency of the agent, and the proposed assignment algorithm can further enhance the learning effect when reusing the agent. This paper verifies the necessity and effectiveness of using task decomposition to implement MADRL for equipment control and provides a practical framework for automated and intelligent shield systems. [ABSTRACT FROM AUTHOR]

Federated learning (FL) has emerged as a promising privacy-preserving solution, which facilitates collaborative learning. However, FL is also vulnerable to poisoning attacks, as it has no control over the participant's behavior. Machine learning (ML) models are heavily trained for low generalization errors. Generative models learn the patterns in the input data to discover out-of-distribution samples, which can be used to poison the model, thereby degrading its performance. This paper proposes a novel approach to generate poisoned (adversarial) samples using hyperdimensional computing (HDC), projecting an input sample to a large HD space and perturbing it in the vicinity of the target class HDC model. This perturbation preserves the semantics of the original samples and adds hidden backdoor/noise into it. It generates a large set of adversarial samples equal to the HD space. It is observed that a trained ML model successfully misclassifies 60%–70% of the generated samples. The adversary uses these samples to frame data poisoning attacks, called hyperdimensional data poisoning attacks (HDPA). HDPA increases the attack impact by 5–10 × than existing poisoning attacks against the byzantine-robust defenses. Furthermore, we propose a hyperdimension-based confidence metric (HDBC) to check the model's conformance, which requires neither access to an ML model nor any additional calibration. • We frame a data poisoning attack in FL, called HDPA. • HDPA achieves 5–10× higher attack impact than existing attacks. • This paper presents an adversarial sample generation approach using the HDC model. • It does not even require the black-box access of the trained ML model. • A hyperdimension-based confidence metric is proposed to evaluate a model confidence. [ABSTRACT FROM AUTHOR]

Android has been a significant target of malware applications due to the exponential growth of mobile devices. This may result in severe threats to Android users such as financial loss, information leakage etc. The security of smartphones has encouraged the researchers in applying machine learning techniques to enhance the Android malware detection rate as the traditional approaches are not effective in distinguishing unknown malware. In recent years, many malicious apps have been detected on Android devices using machine learning based approaches. The problem with these detection systems is that they give a high false positive and false negative rate. Therefore, it is imperative to develop methods for better identifying and classifying malware. The main contribution of this paper is to improve the classification accuracy by fusing the base classifiers. This paper proposes a fusion approach named as MalDetect that enables an effective combination of machine learning techniques to improve the identification rate of malware. The proposed approach is based on the base classifiers and a set of ranking algorithms defined on their predictive error rate. The purpose of this method is to improve the detection rate of Android malware. Two datasets i.e. Drebin and AndroMD are used to illustrate the effectiveness of the proposed approach. Experimental results show that the proposed approach performs better than the individual base classifiers and traditional ensemble learning techniques for identifying Android malware. Further, the proposed approach is compared with the renowned Stacking ensemble technique that uses a meta -classifier. From the experimental outcomes, it is found that the weighted F-measure obtained from the Stacking for Drebin and AndroMD datasets is 0.9811 and 0.9775 respectively whereas the weighted F-measure acquired from the proposed approach is 0.9857 and 0.9790 respectively. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the world's population. Seizure detection and classification are difficult tasks and are ongoing challenges in biomedical signal processing to enhance medical diagnosis. This paper presents and highlights the unique frequency and amplitude information found within multiple seizure types, including their morphologies, to aid the development of future seizure classification algorithms. Whilst many published works in the literature have reported on seizure detection using electroencephalogram (EEG), there has yet to be an exhaustive review detailing multi-seizure type classification using EEG. Therefore, this paper also includes a detailed review of multi-seizure type classification performance based on the Temple University Hospital Seizure Corpus (TUSZ) dataset for focal and generalised classification, and multi-seizure type classification. Deep learning techniques have a higher overall average performance for focal and generalised classification compared to machine learning techniques, whereas hybrid deep learning approaches have the highest overall average performance for multi-seizure type classification. Finally, this paper also highlights the limitations of the TUSZ dataset and suggests some future work, including the curation of a standardised training and testing dataset from the TUSZ that would allow a proper comparison of classification methods and spur advancement in the field. [ABSTRACT FROM AUTHOR]

In the 5G smart grid, battery liquid leakage negatively affects the safe storage and transportation of electricity. Image edge detection help locate the liquid leakage phenomenon. This paper proposes a digitalization-based algorithm that converts the image to the codeword and judges the edge by the code weight. Its main steps are image sampling, quantization, coding and judging. Compared with traditional Sobel and Canny operators, the algorithm dramatically enhances detection ability with machine learning. Moreover, this paper applies the algorithm to detect the leakage of infrared battery images in the 5G smart grid; The results show that it can accurately extract the liquid leakage location, improving fault diagnosis and early risk warning. [ABSTRACT FROM AUTHOR]

Every day, humans make hundreds of decisions, most of which are based on information we gather from our surroundings. The majority of this perception when it comes to driving is visual. Autonomous vehicles that are capable of recognizing items around them and making quick decisions in response to these impulses are self-driving cars. Self-driving cars are the main concern and an emerging subject for every automobile giant in the world. Death due to human failure in a road accident increases with every passing month and with technology assisting humans in every possible field, it is essential to prioritize road safety with it. In a thorough investigation of traffic safety, it was discovered that human error was the sole cause of 57% of accidents and a contributory factor in more than 90% of them. In comparison, only 2.4% of crashes were the result of a mechanical issue, and 4.7% were the result of environmental variables alone. Autonomous cars not only help reduce human errors while driving but also help in reducing driver's fatigue to a greater extent. This study examines the work of a number of scholars in order to provide a quick summary of how computer vision is being used in autonomous cars today. Previous research has shown the use of deep learning algorithms with the help of LiDAR sensors but they come with the problem of being relied on costly hardware. This paper focuses on the vertical and lateral movement of the car while using a more budget friendly yet effective option using just simple computer vision algorithms like Color space transformation, Canny edge detection and Hough line transformation to make the car detect lane lines and steering the car so that it remains bound to them. Simple computer vision requires less operational hardware which indicates that using cost effective boards like Raspberry Pi and Nvidia Jetson Nano can yield much more powerful results. The paper also highlights the reconstruction of a ready-made remote-controlled car that helps understanding autonomous cars even better. [ABSTRACT FROM AUTHOR]

Financial time series prediction has many applications in economics, but producing profitable strategies certainly has a special place among them, a daunting challenge. Statistical and machine learning techniques are intensively researched in the search for a holy grail of stock markets forecasting. However, it is not clear to prospecting researchers how good those popular models are regarding useful predictions on a real scenario. This paper contributes to that discussion, providing decisive evidences contrary to the use of basic out-of-the-box models, specifically Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF) and Naive-Bayes (NB). Results consider optimistic and unreal variables often found in literature, as well as a more close-to-real simulation of the models usage. Specifically, current day closing prices direction forecasting results are contrasted with those on next day forecasts. As expected, when forecasting the current day, accuracy is almost perfect. However, when used to forecast next day closing direction, with a strict data separation policy and without direction or snooping bias, ANN, SVM, RF and NB produce results essentially equal to random guessing. The main achieved result is the demonstration of how a machine learning approach would fare in a support decision system for forecasting short-term future market direction, regardless of the level of market development, considering more than 100 securities in a 10 years period. Consequences for algorithmic trading relate to discouraging usage of the considered models as implemented here. On a more abstract sense, this paper presents more evidence to the Efficient Market Hypothesis (EMH). [ABSTRACT FROM AUTHOR]

Automatic fault diagnosis in many systems is performed via the analysis of vibration data in the time or frequency domain. In the literature, many approaches for extracting featured from signals for fault diagnosis have been proposed. In this paper, we apply a variety of transform functions including Fourier, Wavelet, etc. to extract features from the vibration data, which includes the statistical features and some features automatically extracted via Convolutional Neural Networks (CNNs). For each of these feature extraction approaches, a learning algorithm is trained to diagnose the faults and their results are aggregated in an ensemble machine learning algorithm. The weights of the base learner algorithms are optimized via an evolutionary algorithm to achieve the best-weighted voting scheme. The architecture of CNNs has a significant effect on the performance of the algorithm, thus, in this paper, an evolutionary algorithm is proposed to find the best architecture for CNNs in fault diagnosis. The CNNs are trained via gradient descent algorithms which suffer from getting stuck in local optima. To manage this, we propose an evolutionary algorithm that benefits from the speed of gradient descent and the global search of evolutionary algorithms. The proposed algorithm is tested on a number of benchmark problems and the experimental results are presented. • An ensemble algorithm is proposed for fault diagnosis. • Different feature extractors are exploited. • An evolutionary training algorithm is proposed. [ABSTRACT FROM AUTHOR]

In this paper we investigate the role of self-learning agents in multi-agent models of financial markets. We develop an agent-based simulation model of a financial market and, in addition to the agents with fixed strategies used in previous research, we introduce an agent with a self-learning strategy. To model the behavior of such an agent, we use deep reinforcement learning algorithms, namely deep deterministic policy gradient (DDPG). Next, we conduct a comparative analysis of the results of the constructed model with outcomes of previously proposed models, as well as with the characteristics of real market. To conduct comparative analysis, we use stylized facts of asset returns that allow us to evaluate and compare the characteristics of the markets. Our results show that a model with a self-learning agent gives a better approximation of the real market than a model with classic agents. In particular, unlike the model with classical agents, the model with a self-learning agent turns out to be not so heavy-tailed. Thus, we demonstrate that for a complete understanding of market processes simulation models should take into account self-learning agents that have a significant presence at modern financial markets. Finally, we present the python package 1 1 https://github.com/Kiri28/Agent-Based%5fMarket%5fsimulator. , which was developed by us as part of the research implementation. This package allows to simulate the financial market, as well as create your own agents and evaluate their impact on the market. • The paper explores the role of self-learning agents in modern financial market models. • We use stylized facts based analysis to compare simulated markets with each other. • Using a comparative analysis, we show that our model better describes real markets. • The paper provides our Python package for simulating financial markets. [ABSTRACT FROM AUTHOR]

Applying machine learning (ML) methods to multi-factor authentication is becoming increasingly popular. However, there is no comprehensive methodology to evaluate biometric systems based on machine learning in the literature. This paper proposes a general methodology for evaluation the ML-based systems for impostor recognition/detection using biometric traits. This includes creation of learning and testing sets with appropriate size balance (proportion) between these sets, selecting the number of instances coming from different users, evaluation of the influence of the impostors number on their detection rate, and the impact of the number of records representing user's behavior. In addition, we propose how the real data (possibly affected by account takeover attempts) could be used to extend the enrollment data to support the impostor detection. The proposed approach was used for a systematic comparison of an extensive set of ML and statistical methods. For some of them, the false acceptance rate (FAR) close to zero and false rejection rate (FRR) smaller than 0.05 in a supervised experiment were accomplished, proving the merit of certain ML-based approaches. Moreover, using the method proposed in the paper, a classifier trained on experimental data achieved FAR below 0.05 on the real-world data collected at an actual financial web page. • General methodology for evaluating ML impostor recognition using biometric traits. • Evaluation of the influence of the impostors number on their detection rate. • Evaluation of the impact of the number of records representing user's behavior. • The proposed approach tested on ML methods reaching FAR and FRR smaller than 0.05. • Test-bed trained classifier achieved FAR < 0.05 on data from financial service. [ABSTRACT FROM AUTHOR]

Fake news refuting is an essential part of online social networks (OSNs) management. Identifying the potential voluntary fake news refuters who can spontaneously repost the fake news refutation messages on the OSNs, and intentionally exposing such messages to them can contribute a lot to fake news regulation. Therefore, to fully utilize available user features and enhance fake news refuter identification, the paper proposes a novel paradigm for refuter classification problem, which leverages the pre-trained techniques and mixture of expert system to unify the structured and unstructured data. Specifically, the unstructured text records are first understood by the pre-trained ERNIE2 model, and then feed into Chi-Merge grounded Convolutional Neural Networks, which are expert classifiers in our framework, along with the structured profile data for further transformation. Besides, features extracted from text data are used for determining weights for multiple expert classifiers. Experiments on near 60 k real-world Weibo user data prove that our framework outperforms the large-scale pre-trained deep learning methods and traditional machine learning classification models. This paper provides a novel perspective of fake news refutation from the refuter's side and sheds light on artificial intelligence-driven OSNs management. [ABSTRACT FROM AUTHOR]

• Machine learning approaches to resolve identity issues. • Implementing TensorFlow to investigate policing dataset. • Predictive models using ML techniques to identify fraudulent or genuine IDs. This paper focuses on applications of various machine learning techniques on an anonymized policing dataset used in EU SPIRIT Horizon 2020 project to identify fraudulent identities and help Law Enforcement Agencies (LEAs) in their investigation in finding potential criminals and identity resolution. Lack of qualitative data and appropriate methodology to carry out research on criminal fraudulent identities is a common reason for fewer research in this area. Additionally, it is a very sensitive data to work with and minor inaccuracy in prediction of result causes massive impact in the society as genuine people could be questioned whereas criminals could be sent free. Both of these issues are addressed in this paper by application of 39 million records from policing dataset and working towards higher accuracy while building the model. Various machine learning approaches are applied to train the dataset to make predictions and the research focus on being able to predict the 5 suspected fraudulent identities out of 39 million records in the policing dataset. One of the applied machine learning techniques include TensorFlow along with Keras model which has seldomly been applied by researchers in detection of criminal data. To compare the results and test accuracy of TensorFlow model, other machine learning techniques such as Support Vector Machine, Naïve Bayes and K-nearest Neighbours are also applied to have a comparative study on the obtained outcomes from each model. The goal of this research is to find fraudulent IDs amongst all the anonymized IDs in the criminal dataset using TensorFlow and three other machine learning models and select the most optimal model out of them. Since the model is comparing two names so string-matching techniques such as Levenshtein edit distance, Hamming Distance, Jaro-Winkler and Soundex were applied to select an effective approach first before building the model and analysing the results. TensorFlow model demonstrated highest accuracy with relatively least execution time and the only model to successfully predict all the 5 suspects from the policing dataset. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and non-motor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment. • Deep learning and wearable technology are used in diagnosis and monitoring. • Growing interest in the use of deep learning and wearables in Parkinson's disease. • The adoption of deep learning in Parkinson's disease is currently under development. • Technical and methodological challenges difficult the technology adoption. [ABSTRACT FROM AUTHOR]

Ignoring the temporal relationship of position points in trajectory data or the change in the number of position points in dummy trajectory generation can result in inadequate data availability. To address the data availability problem of publishing user trajectory data while ensuring privacy protection, this paper proposes an improved privacy protection method combining a Bidirectional Gated Recurrent Unit and differential privacy (BiGRU-DP). First, Laplace noise is added to the time attribute of the trajectory data. Secondly, BiGRU is used to predict the processing of trajectory data, considering the bi-directional data of the predicted point. Then, the k -means clustering method is used to generalize and optimize the trajectory data. Finally, the trajectory data is published after adding noise. The experimental simulation results show that BiGRU-DP enhances the availability of published data and has certain efficiency advantages over traditional trajectory data publishing methods while ensuring privacy protection. • Propose a trajectory data temporal attribute noise addition strategy. • Design a trajectory prediction algorithm to yield a more accurate trajectory. • Design a k -means trajectory timestamp position generalization processing algorithm. • Propose a trajectory exception-handling mechanism and post-processing mechanism. [ABSTRACT FROM AUTHOR]

Computational ecoacoustics has seen significant growth in recent decades, facilitated by the reduced costs of digital sound recording devices and data storage. This progress has enabled the continuous monitoring of vocal fauna through Passive Acoustic Monitoring (PAM), a technique used to record and analyse environmental sounds to study animal behaviours and their habitats. While the collection of ecoacoustic data has become more accessible, the effective analysis of this information to understand animal behaviours and monitor populations remains a major challenge. This survey paper presents the state-of-the-art ecoacoustics data analysis approaches, with a focus on their applicability to large-scale PAM. We emphasise the importance of large-scale PAM, as it enables extensive geographical coverage and continuous monitoring, crucial for comprehensive biodiversity assessment and understanding ecological dynamics over wide areas and diverse habitats. This large-scale approach is particularly vital in the face of rapid environmental changes, as it provides crucial insights into the effects of these changes on a broad array of species and ecosystems. As such, we outline the most challenging large-scale ecoacoustics data analysis tasks, including pre-processing, visualisation, data labelling, detection, and classification. Each is evaluated according to its strengths, weaknesses and overall suitability to large-scale PAM, and recommendations are made for future research directions. • Passive Acoustic Monitoring (PAM) enables continuous species monitoring. • We evaluate ecoacoustics datasets for applicability in large-scale PAM. • We compare key ecoacoustics data analysis frameworks/methods. • We discuss the open challenges/opportunities of large-scale PAM data analysis. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) is defined as the structure-borne propagating transient elastic stress waves produced due to the rapid release of energy because of internal structure changes or asperities contact under loading. AE sensor is one of the promising sensing technologies for machinery and structures health monitoring. A start-to-end comprehensive condition monitoring solution utilizes the potential of four technologies, i.e., platform, data, analytics, and operations. This study reviews these four technologies developed based on AE sensors for rotating machinery element's fault diagnosis and prognosis. Operating parameters influence the AE signal; hence, the influence of these parameters on AE characteristics is also discussed. Based on this review, challenges and future directions while using the AE sensor are detailed. No dedicated paper that summarises all these technologies in the context of AE sensors is available. Hence, using AE signals, analysis and measurements are mostly done by researchers based on their personal experiences. This article attempts to summarise the standard practices used by various researchers for AE data measurement and analysis for rotating machinery condition monitoring. [ABSTRACT FROM AUTHOR]

Predictive analytics aims to build machine learning models to predict behavior patterns and use predictions to guide decision-making. Predictive analytics is human involved, thus the machine learning model is preferred to be interpretable. In literature, Generalized Additive Model (GAM) is a standard for interpretability. However, due to the one-to-many and many-to-one phenomena which appear commonly in real-world scenarios, existing GAMs have limitations to serve predictive analytics in terms of both accuracy and training efficiency. In this paper, we propose FXAM (Fast and eXplainable Additive Model), a unified and fast interpretable model for predictive analytics. FXAM extends GAM's modeling capability with a unified additive model for numerical, categorical, and temporal features. FXAM conducts a novel training procedure called Three-Stage Iteration (TSI). TSI corresponds to learning over numerical, categorical, and temporal features respectively. Each stage learns a local optimum by fixing the parameters of other stages. We design joint learning over categorical features and partial learning over temporal features to achieve high accuracy and training efficiency. We prove that TSI is guaranteed to converge to the global optimum. We further propose a set of optimization techniques to speed up FXAM's training algorithm to meet the needs of interactive analysis. Thorough evaluations conducted on diverse data sets verify that FXAM significantly outperforms existing GAMs in terms of training speed, and modeling categorical and temporal features. In terms of interpretability, we compare FXAM with the typical post-hoc approach XGBoost+SHAP on two real-world scenarios, which shows the superiority of FXAM's inherent interpretability for predictive analytics. • A unified and fast interpretable model (FXAM) for predictive analytics is proposed. • FXAM extends modeling capability and efficiency of Generalized Additive Model. • FXAM conducts a novel training procedure called Three-Stage Iteration. • Joint learning and partial learning help achieve high accuracy and efficiency. • FXAM performs much better on synthetic data sets and 13 real datasets. [ABSTRACT FROM AUTHOR]

Although the accurate potential for growth prediction is very important for Government grants and contributions programs to better support Small and Medium-sized Enterprises (SMEs), it is a challenging task due to the data heterogeneity (both structured data and free text data bilingual in English and French), the class imbalance issue, and the difficulties in efficient feature learning. To address these challenges, this paper presents a novel BERT-TCN model for portfolio predictions in government funding programs, with the following key contributions. First, we describe the application of a novel architecture to a prediction task involving sequential, structured, partially quantitative input data and free text input data. Specifically, our novel model predicts the growth of firms receiving government funding for innovation. Our model also deals with class imbalance in the data and the difficulties in efficient feature learning. Our model integrates a Transformer model, i.e., BERT, for text modeling with a Temporal Convolutional Network (TCN) for sequential prediction. Second, we also developed various performance evaluation criteria in Section 4.3, allowing comprehensive assessments of the proposed approach from both the machine learning perspective and funding program-specific perspective. Third, the importance of features (both text and numerical features) is quantified and evaluated, allowing insights into how different features contribute to the prediction and explainability of the proposed model. The proposed approach is trained and tested on a large dataset from a rich database, demonstrating that the proposed approach can greatly help individual human experts improve their results. • Proposing a novel BERT-TCN model for text modeling and temporal prediction. • Designing various evaluation criteria for comprehensive assessments of the model. • Exploring explainability of the model by ablation studies of various input features. [ABSTRACT FROM AUTHOR]

When engaging in activities such as using video display terminals, driving, or sports, effective fatigue monitoring is crucial. Nevertheless, obtaining biological information through contact devices may interfere with normal activities. Deterministic expressions in mainstream machine learning have limitations in reflecting the continuous, dynamic, and fuzzy process of fatigue status. Additionally, fatigue is a cumulative process where the previous state impacts the assessment of the current one. To address these needs and challenges, this paper proposes a novel model based on facial videos called the temporal adaptive fuzzy neural network (TAFNN) for fatigue assessment. TAFNN utilizes an adaptive fuzzy neural network as its foundation and employs causal and dilated convolutions for the time information processing method to achieve time series extraction and fatigue assessment. It leverages facial physiological and motion features to minimize interference during assessment. Furthermore, TAFNN introduces a new calculation method for rule antecedents to enhance stability. Experimental results demonstrate that TAFNN effectively captures the cumulation and fuzziness of state changes, outperforming other widely adopted methods in both assessment ability and runtime performance. The improved rule antecedent calculation method successfully mitigates the issue of multiple memberships rapidly approaching zero after combination. Through 1000 repeated experiments, the enhanced method reduces TAFNN's instability by 81.58%. • A novel temporal adaptive fuzzy neural network (TAFNN) for assessment is introduced. • TAFNN can simultaneously express fatigue state changes' cumulation and fuzziness. • The improved rule antecedent calculation method can enhance the stability of TAFNN. • TAFNN has advantages in both fatigue assessment capability and runtime performance. [ABSTRACT FROM AUTHOR]

The advancement of machine learning and symbolic approaches have underscored their strengths and weaknesses in Natural Language Processing (NLP). While machine learning approaches are powerful in identifying patterns in data, they often fall short in learning commonsense and the factual knowledge required for the NLP tasks. Meanwhile, the symbolic methods excel in representing knowledge-rich data. However, they struggle to adapt dynamic data and generalize the knowledge. Bridging these two paradigms through hybrid approaches enables the alleviation of weaknesses in both while preserving their strengths. Recent studies extol the virtues of this union, showcasing promising results in a wide range of NLP tasks. In this paper, we present an overview of hybrid approaches used for NLP. Specifically, we delve into the state-of-the-art hybrid approaches used for a broad spectrum of NLP tasks requiring natural language understanding, generation, and reasoning. Furthermore, we discuss the existing resources available for hybrid approaches for NLP along with the challenges and future directions, offering a roadmap for future research avenues. • Review of hybridization of machine learning and symbolic methods for NLP. • Taxonomy of NLP tasks with substantial use of hybrid approaches. • Overview of state-of-the-art hybrid approaches for NLU, NLG, and NLR tasks. • Open challenges and future directions of Hybrid NLP. [ABSTRACT FROM AUTHOR]

A new model of utility analysis and performance prediction in crowdfunding is proposed in this paper with considering backer's behavior-related decision. To establish a connection between the backer's behavior-related decision and crowdfunding performance, the proposed model includes a modeling analysis and an empirical analysis. The modeling analysis focuses on analyzing backer's backing decision with establishing the utility-driven model, while the empirical analysis verifies the association between the utility-related features and crowdfunding performance, as well as the prediction improvement of the proposed model. Specifically, prospect theory (PT) is applied first in the modeling analysis part to analyze backer's prospect utilities, and then evidential theory (ET) is applied to aggregate the multi-source dynamic feature's utility. To validate the proposed model, and based on the results of modeling analysis, we conduct the significant test with regression model and the prediction test with several machine-learning prediction model to verify whether the proposed utility features can improve the prediction results in terms of crowdfunding performance. The empirical results show that the correlation between behavior-related factors and crowdfunding performance is significant. And the area under the curve (AUC) index of the proposed model is, on average, 7.39% higher than the baseline model on several machine-learning prediction models. In addition to providing new insights for predicting crowdfunding results, this research provides a powerful tool to assist crowdfunding platform and fundraisers in operational management with analyzing backers' backing behavior. • Modeling backers' behavior-related decisions in crowdfunding. • Empirical results validate the effect of behavior-related features on performance. • On average, the proposed model improves prediction accuracy by 7.39%. [ABSTRACT FROM AUTHOR]

A missing data problem is inevitable when collecting time series datasets from marine sensors. Due to this, sensor data is not reliable enough to assist decision-making. To impute missing values, a number of methods are available. Choosing the best imputation method, however, is not a trivial task, as it usually involves domain expertise and trial-and-error iterations. Additionally, if imputations are done carelessly, they produce a high error, resulting in incorrect assumptions by stakeholders. In this paper, a meta-learning approach is presented that can be used to extract characteristics of the underlying data, and based on that, a less error-prone imputation method is recommended. Ten commercial ocean-going vessel datasets are used to evaluate our proposed method. A total of 29,527 data samples were generated, comprising 22 inputs and 1 target. The proposed method achieves a weighted F1-Score of 87.5% when utilizing stratified 10-fold cross-validation. Our approach can improve the average imputation score up to 86%, with the worst-case improvement being 5%. This demonstrates that our proposed approach is efficient and effective in recommending the best imputation methods. • The characteristics of the dataset influence the recommendation of imputation methods. • Meta-learning can model the relationship between dataset characteristics and the performance of imputation methods. • Interpolation and MissForest are the most effective imputation techniques. [ABSTRACT FROM AUTHOR]