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• 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 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]

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]

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]

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]

• MMODE is developed as a new hybrid gene selection method for tumor identification. • MMODE combines multi-tasking and multi-objective frameworks. • MMODE uses a new elite strategy and a new guidance strategy. • MMODE selects a few genes and achieves high classification accuracy. A key preprocessing step in tumor recognition based on microarray expression profile data and machine learning is to identify tumor marker genes. Gene selection aims to select the most relevant gene subset from the original ultra-high dimensional microarray expression profile data to improve tumor identification performance. Inspired by evolutionary multitasking (EMT) and multi-objective optimization, this paper puts forward a novel multitasking multi-objective differential evolution gene selection algorithm (MMODE) which uses new elite and guidance strategies to select the best gene subsets. MMODE initializes two different populations according to different filtering criteria to increase the diversity of the search. These two populations guide their respective populations to search in the optimal direction through knowledge transfer in the evolutionary process. In addition, MMODE employs new elite and guidance strategies that enables individuals to narrow the search range and jump out of local optima. The proposed algorithm is validated on 13 publicly available microarray expression datasets in comparison with state-of-the-art gene selection algorithms. The experimental results show that MMODE can find smaller gene subsets and achieve higher classification accuracy compared with other algorithms. [ABSTRACT FROM AUTHOR]

Optimal path planning is a fundamental problem in artificial intelligence (AI) and has wide applications in areas such as robotics, transportation, and logistics. In this paper, we propose a novel approach for ordering algorithms in pathfinding problems using the concept of shell layers. Our approach aims to improve the computational efficiency and distance cost of ordering algorithms. To evaluate the effectiveness of our approach, we compare it with five state-of-the-art techniques commonly used in the field of ordering algorithms on eight different scenarios with varying configurations. Our results show that our proposed approach outperformed the state-of-the-art techniques in terms of time-computational complexity and distance cost in most of the scenarios, demonstrating its potential as a new state-of-the-art technique for ordering algorithms. Specifically, our approach outperformed the nearest neighbor, A*, branch and bound, Christofides, and genetics ordering algorithms. However, we also identified two specific situations where our approach was outperformed by two state-of-the-art algorithms due to the specific distribution of the goal nodes. These findings highlight the importance of evaluating and comparing ordering algorithms in various scenarios. Our approach has significant implications for AI research and development, as it has the potential to improve the performance of pathfinding algorithms in various applications. We also discuss the limitations of our approach and potential areas for further research, such as investigating the effectiveness of our approach in different types of graphs and exploring the potential use of machine learning techniques to optimize the shell layer construction. The code for this study is available at https://github.com/abdullah1aloush1/MuGONA. • Equally-spaced nodes formed over workspace with obstacles, optimizing search process. • Efficient neighboring graph built for equally-spaced nodes, reducing complexity. • Path optimization for navigation by ordering goal nodes, reducing time and distance. • Path smoothing for navigation via interpolation to yield a trajectory on path nodes. [ABSTRACT FROM AUTHOR]

Stochastic optimization (SO) algorithms based on the Powerball function, namely powered stochastic optimization (PoweredSO) algorithms, have been confirmed, effectively, and demonstrated great potential in the context of large-scale optimization and machine learning tasks. Nevertheless, the issue of how to determine the learning rate for PoweredSO is a challenge and still unsolved problem. In this paper, we propose a class of adaptive PoweredSO approaches that are efficient, scalable and robust. It takes advantage of the hypergradient descent (HD) technique to automatically acquire an online learning rate for PoweredSO-like methods. In the first part, we study the behavior of the canonical PoweredSO algorithm, the Powerball stochastic gradient descent (pbSGD) method, with HD. The existing PoweredSO algorithms also suffer from the high variance because they take the similar algorithmic framework to SO algorithms, arising from sampling tactics. Therefore, the second portion develops an adaptive powered variance-reduced optimization method via utilizing both variance-reduced technique and HD. Moreover, we present the convergence analysis of the proposed algorithms and explore their iteration complexity on non-convex cases. Numerical experiments are conducted on machine learning tasks, verifying the superior performance over modern SO algorithms. • We propose a type of adaptive powered stochastic optimization algorithms. • The theoretical analysis of the resulting algorithms is established. • Experiments on machine learning tasks verify the efficacy of the algorithms. [ABSTRACT FROM AUTHOR]

Max-cut is one of the most classic NP-hard combinatorial optimization problems. The symmetry nature of it leads to special difficulty in extracting meaningful configuration information for learning; none of the state-of-the-art algorithms has employed any learning operators. This paper proposes an original learning method for max-cut, namely post-flip edge-state learning (PF-ESL). Different from previous algorithms, PF-ESL regards edge-states (cut or not cut) rather than vertex-positions as the critical information of a configuration, and extracts their statistics over a population for learning. It is based on following observations. 1) Edges are the only factors considered by the objective function. 2) Edge-states keep invariant when rotating a local configuration to its symmetry position, but vertex-positions do not. These suggest that edge-states contain more meaningful information about a configuration than vertex-positions do. It is impossible to set the state of an edge without influencing some other edges' states due to their dependencies. Therefore, instead of setting edge-states directly, PF-ESL samples the flips on vertices. Flips on vertices are sampled according to their capacities in increasing the similarity on edge-states between the given solution and a population. PF-ESL is employed in an EDA (Estimation of Distribution Algorithm) perturbation operator and a path-relinking operator. Experimental results show that our algorithm is competitive, and show that edge-state learning is value-added for both the two operators. The main contributions of this paper are as follows. Firstly, previous state-of-the-art evolutionary algorithms for max-cut focus on vertex positions in their evolutionary operation, this paper proposes a new and more reasonable perspective suggesting that edge-states are the critical information of divided graphs rather than vertex positions, and introduces a novel method to measure and utilize their similarities based on it. Such a perspective is fundamental to learning based algorithms design for max-cut and other graph partitioning problems, and can shed lights on future researches. Furthermore, since max-cut is one of the most classic and fundamental NP hard problems, many real-world problems involve dividing graph data into different parts to optimize certain functions, this new perspective may inspire related or similar problems. Secondly, besides the original edge-states based perspective, and the post-flip edge-states learning (PFESL) operator based on it, our memetic algorithm also incorporates a novel evolutionary framework which alternates between EDA based Iterated Tabu search (ITS) and path relinking based genetic algorithm. Finally, the proposed algorithm provides competitive results on two mostly used benchmark sets and improves the best-known results of 6 most challenging instances. • An original edge-state perspective for design learning operators for max-cut. • A novel learning operator to learn edge states. • A new evolutionary framework for max-cut. • It provides competitive results on two mostly used benchmark sets. [ABSTRACT FROM AUTHOR]

The continuous malicious software (malware) attacks on smartphones pose a serious threat to the security of users, especially the dominant platform Android. Data-driven methods based on machine learning algorithms are a promising way to defend against that. In this paper, we explore the limitations of this kind of methods in improving the performance of malware detection, e.g., considering each feature in isolation and relying on balanced class data, and propose a multi-model ensemble framework MEFDroid by combining individual predictors, where hybrid deep learning based feature extraction methods are adopted to learn meaningful features from raw data. Besides, a novel fusion scheme is exploited to fuse multi-level representations and mine their correlations to maximize the utilization of original information. To evaluate the effectiveness of the MEFDroid framework, we conduct a step-by-step model justification experiment to investigate how the proposed algorithms (i.e., ESAES, EDAES and EDAFS) enhance the malware detection performance gradually. We also compare the proposed algorithms with classical machine learning methods, conventional sampling solutions for the imbalance problem. Besides, we investigate the reliability of our proposed methods using another public dataset. Our extensive experimental results demonstrate that the target model EDAFS in MEFDroid outperforms others in terms of most metrics, which means that it is an effective alternative solution to detect Android malware. [ABSTRACT FROM AUTHOR]

This research paper focuses on quaternion neural networks (QNNs) - a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. Previous studies have shown that QNNs outperform real-valued neural networks in basic tasks and have potential in high-dimensional problem spaces. However, research on QNNs has been fragmented, with contributions from different mathematical and engineering domains leading to unintentional overlap in QNN literature. This work aims to unify existing research by evaluating four distinct QNN backpropagation algorithms, including the novel GHR-calculus backpropagation algorithm, and providing concise, scalable implementations of each algorithm using a modern compiled programming language. Additionally, the authors apply a robust Design of Experiments (DoE) methodology to compare the accuracy and runtime of each algorithm. The experiments demonstrate that the Clifford Multilayer Perceptron (CMLP) learning algorithm results in statistically significant improvements in network test set accuracy while maintaining comparable runtime performance to the other three algorithms in four distinct regression tasks. By unifying existing research and comparing different QNN training algorithms, this work develops a state-of-the-art baseline and provides important insights into the potential of QNNs for solving high-dimensional problems. • Clifford Multilayer Perceptrons outperform other quaternion neural network methods. • Multilayer GHR Calc. networks approximate real nonlinear functions reasonably well. • Classic QNN backpropagation techniques are outdated and should be avoided. [ABSTRACT FROM AUTHOR]

Multi-agent reinforcement learning (MARL) has been successfully applied in many fields. In MARL, the policy evaluation problem is one of crucial problems. In order to solve this problem, distributed Temporal-Difference (TD) learning algorithm is one of the most popular methods in a cooperative manner. Despite its empirical success, however, the theory of the adaptive variant of distributed TD learning still remain limited. To fill this gap, we propose an adaptive distributed temporal-difference algorithm (referred to as MS - ADTD) under Markovian sampling over time-varying networks. Furthermore, we rigorously analyze the convergence of MS - ADTD , the theoretical results show that the local estimation can converge linearly to the optimal neighborhood. Meanwhile, the theoretical results are verified by simulation experiments. • This paper proposes a distributed adaptive TD learning algorithm under Markovian sampling. • It theoretically analyzes the non-asymptotic convergence performance of the proposed algorithm. • It verifies the performance of the proposed algorithm by experiments. [ABSTRACT FROM AUTHOR]

Large-scale multi-objective optimization problems (LSMOPs) bring significant challenges due to their large number of decision variables. Most of the existing algorithms fail to obtain high-quality solutions for the LSMOPs. To remedy this issue, an algorithm named dual-stage large-scale multi-objective evolutionary algorithm with dynamic learning strategy (DLMOEA-DLS) is proposed in this paper. In the DLMOEA-DLS, the entire evolution process mainly includes two stages, and each stage plays a different role in the searching process. In the first stage, the decision variables are clustering into two categories to be optimized independently for the convergence of the population. In the second stage, a dynamic learning strategy is designed to generate new offspring, in which each solution learns from a leader with better fitness and coupled control parameter for each solution is adaptively updated by learning from the historical behaviors of the solution. Moreover, an environmental selection operator is adopted to reserve promising solutions for the next iteration. To verify the performance of the DLMOEA-DLS, five state-of-the-art algorithms are used for comparison on 36 LSMOP benchmark instances, 48 LMF benchmark instances, and 6 real-world TREE benchmark instances. The experimental results demonstrate the superiority of the DLMOEA-DLS over the five state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• Time burst characteristic is studied by 3 aspects in fake review identification. • A suspicion degree method of fake reviews is proposed from (1) by LOF algorithm. • A fake review identification framework is given including the suspicion degree. • The case study shows that the proposed method outperforms the existed methods. With the rapid development of e-commerce, online reviews have played an increasingly important role in consumers' shopping intentions and behaviors. Therefore, how to effectively identify fake reviews has become one of the important issues that need to be resolved. Since the existing methods do not fully consider the time burst characteristics of reviews, this paper proposes a suspicion degree determining method based on the three-dimensional time series. Besides, combining the suspicion degree feature, review text features, and reviewer's behavior features together, this paper proposes a more comprehensive fake review identification framework. The yelp and amazon public data sets are carried out to verify the effectiveness of the proposed method, and the experimental results show that the proposed method outperforms the most advanced methods. [ABSTRACT FROM AUTHOR]

Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computational costs reasonable. The marine predators algorithm (MPA) is a modern optimization meta-heuristic, inspired by widespread Lévy and Brownian foraging strategies in ocean predators as well as optimal encounter rate strategies in biological interactions between predator and prey. However, MPA is not without its shortcomings. In this paper, a quasi-opposition based learning and Q-learning based marine predators algorithm (QQLMPA) is proposed. This offers multiple improvements over standard MPA. Primely, Q-learning allows MPA to fully use the information generated by previous iterations. And also, quasi-opposition based learning serves to increase population diversity, reducing the risk of convergence to inferior local optima. Numerical experiments demonstrate better performance by QQLMPA on 32 benchmark optimization functions and three engineering problems: designs of pressure vessel, hydro-static thrust bearing, and speed reducer. • Q-learning is introduced to MPA for adaptively choosing search mode. • Quasi-opposition learning is used to enhance the exploration of MPA. • The proposed optimizer can achieve great performance for continuous optimization. [ABSTRACT FROM AUTHOR]

In fingerprint-based positioning methods, the received signal strength (RSS) vectors from access points are measured at reference points and saved in a database. Then, this dataset is used for the training phase of a pattern recognition algorithm. Several noise types impact the signals in radio channels, and RSS values are corrupted correspondingly. These noises can be mitigated by averaging the RSS samples. In real-time applications, the users cannot wait to collect uncorrelated RSS samples to calculate their average in the online phase of the positioning process. In this paper, we propose a solution for this problem by leveraging the distribution of RSS samples in the offline phase and the preceding state of the user in the online phase. In the first step, we propose a fast and accurate positioning algorithm using a deep neural network (DNN) to learn the distribution of available RSS samples instead of averaging them at the offline phase. Then, the similarity of an online RSS sample to the RPs' fingerprints is obtained to estimate the user's location. Next, the proposed DNN model is combined with a novel state-based positioning method to more accurately estimate the user's location. Extensive experiments on both benchmark and our collected datasets in two different scenarios (single RSS sample and many RSS samples for each user in the online phase) verify the superiority of the proposed algorithm compared with traditional regression algorithms such as deep neural network regression, Gaussian process regression, random forest, and weighted KNN. • Real-time positioning by considering the limitations of smartphones. • Extracting the RSS distribution in the offline phase. • Leveraging previous states of users in positioning. • Showing the robustness by extensive experiments on benchmark dataset. [ABSTRACT FROM AUTHOR]

• 3 layer neural networks outperform other ML algorithms in automated assessments. • In subjective assignments, deep neural networks can improve score prediction. • Word embeddings and linguistic feature results in more precise text mining models. • Extraction and selection of appropriate features can improve results. Machine learning (ML) has recently gained popularity in a variety of domains for automating tasks. This has also been used in the evaluation of student responses/ assignments. This paper presents a qualitatively enhanced methodology for automated score prediction of subjective assignments. To consider all the major aspects of a human grader, 21 linguistic features related to syntactical, grammatical, sentimental, and readability are analyzed from the assignments. This study makes use of the ASAP competition dataset from Kaggle. The evaluation metric used is Quadratic Weighted Kappa (QWK), which measures the agreement between the human graded score and the predicted score. The effect of appropriate feature selection has been observed using Mutual Information Regression. Four ML algorithms are investigated on identified linguistic features. 3 Layer Neural Network with feature selection performed well among all chosen ML algorithms with average QWK of 0.678. To include the benefits of deep learning, a new hybrid model (LF-BiLSTM-att-FS) is proposed that combines a higher level deep neural network (DNN) with the selected features. Pre-trained Glove embedding is used to include contextual information of the text. The proposed model results demonstrated an improvement in overall accuracy, with an average QWK value of 0.768. [ABSTRACT FROM AUTHOR]

Clustering used to discover hidden patterns in unlabeled data sets is an important task in data mining. Therefore, clustering validation applied to evaluating the clustering results has been recognized as one of the vital issues for clustering applications. The existing validation index is often used to evaluate the results rather than guide the clustering process dynamically. However, using the index can automatically adjust the algorithm's operation according to the actual data distribution, thereby improving the algorithm's adaptability. Simulating the division and aggregation process of cells in biology, in this paper, we propose a new two-phase (grouping and merging) cell group clustering algorithm by using a continuous validation and correction mechanism. In the grouping phase, a new clustering internal validation index called Split Index (S I) is utilized to evaluate the cohesion of a cell group continuously, and then a validation and correction mechanism is adopted to validate and split the cell groups so that the S I of each cell group can meet the split threshold ε , finally the cell nucleus of each cell group is determined by finding the sum of the minimum distances from the nucleus to other samples. In the merging phase, the cell group merge method is adopted to merge all the reachable cell groups in a density-reachable manner. Ultimately the clustering problem of arbitrarily distributed samples is completed. Experiments on the synthetic and the UCI Machine Learning Repository 1 1 http://archive.ics.uci.edu/ml/index.php. data set show that the validation index can effectively guide the clustering process, and the algorithm can deal with various data sets, including imbalanced data sets and spherical non-spherical clusters. [Display omitted] • We define a Split Index to evaluate the cohesion of a cell group. • A new method using clustering validation index is applied in clustering process. • Algorithm SICG is proposed based on validation & correction mechanism. • SICG with good result has little dependence on the parameter. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is recognized as one of the majority public and challenging problems in the Machine Learning domain. FS can be examined as an optimization problem that needs an effective optimizer to determine its optimal subset of more informative features. This paper proposes a wrapper FS method that combines chaotic maps (CMs) and binary Group Search Optimizer (GSO) called CGSO, which is used to solve the FS problem. In this method, five chaotic maps are incorporated with the GSO algorithm's main procedures, namely, Logistic, Piecewise, Singer, Sinusoidal, and Tent. The GSO algorithm is used as a search strategy, while k-NN is employed as an induction algorithm. The objective function is to integrate three main objectives: maximizing the classification accuracy value, minimizing the number of selected features, and minimizing the complexity of generated k-NN models. To evaluate the proposed methods' performance, twenty well-known UCI datasets are used and compared with other well-known published methods in the literature. The obtained results reveal the superiority of the proposed methods in outperforming other well-known methods, especially when using binary GSO with Tent CM. Finally, it is a beneficial method to be utilized in systems that require FS pre-processing. • A novel feature selection method is proposed for classification task. • Five Chaotic Group Search Optimizers are investigated. • Twenty UCI datasets were used in the experiments. • Our methods got a higher accuracy results using a fewer number of features. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel density-based radar scanning clustering algorithm. Its main objective is to quickly discover and accurately extract individual clusters by employing the radar scanning strategy. By using this algorithm, the number of clusters does not need to be specified beforehand. Two techniques are utilized in our proposed method. First, we use a fast mean-shift algorithm with adaptive radius and active subsets to effectively locate the centers, reducing the computational time significantly. Second, we employ the shape of the probability density function of the distribution of distances between a selected point and the other points in the data set. This is performed to determine the critical parameters of the radiuses of the fast mean-shift algorithm and radiuses of clusters. The new algorithm has four merits. It reduces the computational complexity, overcomes problems caused by high dimensionality, is capable of dealing with heterogeneous spherical data sets, and lastly, is robust to noise and outliers. After applying our proposed method to several kinds of synthetic and real-world data sets, the results indicate that the density-based radar scanning algorithm is efficient and accurate. • The proposed algorithm discovers and extracts clusters by a radar scanning strategy. • The number of clusters does not need to be given beforehand. • This algorithm reduces time complexity and solves high dimensionality. • It is capable of dealing with heterogeneous data. • It is robust and invulnerable to noise and outliers. [ABSTRACT FROM AUTHOR]

• Deep learning algorithm for identification of type and flight mode of detected drones. • Novel deep learning algorithm for detection of multiple drones in radio frequency domain. • Results of three indoor experiments concerning collecting and analyzing radio signals from drones. • Public available database with radio signals of drones from two different radio frequency bands. Unmanned aerial systems, especially drones have gone through remarkable improvement and expansion in recent years. Drones have been widely utilized in many applications and scenarios, due to their low price and ease of use. However, in some applications drones can pose a malicious threat. To diminish risks to public security and personal privacy, it is necessary to deploy an effective and affordable anti-drone system in sensitive areas to detect, localize, identify, and defend against intruding malicious drones. This research article presents a new publicly available radio frequency drone dataset and investigates detection and identification methodologies to detect single or multiple drones and identify a single detected drone's type. Moreover, special attention in this paper has been underlined to examine the possibility of using deep learning algorithms, particularly fully connected deep neural networks as an anti-drone solution within two different radio frequency bands. We proposed a supervised deep learning algorithm with fully-connected deep neural network models that use raw drone signals rather than features. Regarding the research results, the proposed algorithm shows a lot of potentials. The probability of detecting a single drone is 99.8%, and the probability of type identification is 96.1%. Moreover, the results of multiple drones detection demonstrate an average accuracy of 97.3%. There have not been such comprehensive publications, to this time, in the open literature that have presented and enlightened the problem of multiple drones detection in the radio frequency domain. [ABSTRACT FROM AUTHOR]

Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads' execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads' real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy. • The first reinforcement learning approach for effective resource utilization in SMT. • Continuous action space for fine-grained and accurate action space exploration. • Encouraging the convergence of the model via forming a lower bound of the policy. • A self-play mechanism compensates for the long time-scale variance of the data. [ABSTRACT FROM AUTHOR]

Clustering has been applied in machine learning, data mining and so on, and has received extensive attention. However, since some data has noise or outliers, these noise or outliers easily bring about the objective function with large errors. In this paper, a robust clustering model with ℓ 2 , 1 , ℓ 1 norm and Laplacian regularization (RCLR) is proposed, on which, sparse error matrix is introduced to express sparse noise, and ℓ 1 norm is introduced to alleviate the sparse noise. In addition, the ℓ 2 , 1 norm is also introduced to achieves space robust by virtue of its nice rotation invariance property. Therefore, our RCLR is insensitive to data noise and outliers. More importantly, the Laplacian regularization is introduced into the RCLR to improve the clustering accuracy. In order to solve the optimization objective of clustering problem, we propose an iterative updating algorithm, named alternating direction method of multipliers (ADMM), to update each optimization variable alternatively, and the convergence of the proposed algorithm is also proved in theory. Finally, experimental results on a total of eleven datasets of three types of datasets, elaborate the superiority of this method over six existing classical clustering methods. Three types of datasets include face images dataset, handwritten recognition dataset, and UCI dataset. In particular, our RCLR clustering approach has the best effect on face image dataset. • Propose a joint optimization framework for clustering. • Utilize ℓ 2 , 1 and ℓ 1 to alleviate the influence of outliers and noises. • Prove algorithm convergence from theoretical and practical aspects. • Experimentation on three different types of real datasets. [ABSTRACT FROM AUTHOR]

• FCS is proposed as a complementary step for principal component analysis. • PCA-FCS identifies the most relevant components based on their level of contribution. • PCA-FCS simultaneously performs dimension reduction and component selection. • PCA-FCS has substantially improved the accuracy of the random forest algorithm. Principal component analysis (PCA) is one the most effective and widely used dimensionality-reduction techniques which aggregates the maximal variance in the first few components. In many applications, the first few components of PCA are used in place of the original variables in statistical and machine learning models; nevertheless, such use does not guarantee the selection of the most relevant components to the target variable. This paper presents an efficient approach in which all components, rather than simply the first few, are considered as input for the random forest (RF) model, and a feature selection algorithm is integrated with the RF to select the most relevant components, hence called fully component selection (FCS). The proposed method was evaluated on spectroscopic data comprising 70 soil samples with 2050 spectral bands to estimate soil organic carbon (SOC). The FCS approach was compared to the RF model once considering the original features, then again using only the first few components, and again using all components without feature selection. The results showed that RF-FCS substantially outperformed other approaches, such that the R2 was increased between 25.2% and 55.5%. Furthermore, the findings indicated that the most relevant principal components (PCs) to the target variable were not the first few, but PC6, PC7, PC8, PC15, and PC43. Using the FCS approach increased model performance substantively, and coupling it with machine learning and statistical models is strongly recommended for high dimensional data applications. [ABSTRACT FROM AUTHOR]

• We study fairness of AI algorithms in Privileged Group Selection Bias data settings. • A typical domain which often presents such selection bias is AI-based hiring. • We demonstrate that such selection bias can indeed lead to a high algorithmic bias. • We propose three in-process and pre-process fairness mechanisms. • Our methods improve fairness considerably with a minimal compromise in accuracy. An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. In this paper, we study the fairness of AI algorithms in data settings for which unprivileged groups are extremely under-represented compared to privileged groups. A typical domain which often presents such Privileged Group Selection Bias (PGSB) is AI-based hiring, which stems from an inherent lack of labeled information for rejected applicants. We first demonstrate that such a selection bias can lead to a high algorithmic bias, even if privileged and unprivileged groups are treated exactly the same. We then propose several methods to overcome this type of bias. In particular, we suggest three in-process and pre-process fairness mechanisms, combined with both supervised and semi-supervised learning algorithms. An extensive evaluation that was conducted using two real-world datasets, reveals that the proposed methods are able to improve fairness considerably, with only a minimal compromise in accuracy. This is despite the limited information available for unprivileged groups and the inherent trade-off between fairness and accuracy. [ABSTRACT FROM AUTHOR]

Feature selection in real-world data mining problems is essential to make the learning task efficient and more accurate. Identifying the best feature selection algorithm, among the many available, is a complex activity that still relies heavily on human experts or some random trial-and-error procedure. Thus, the automated machine learning community has taken some steps towards the automation of this process. In this paper, we address the metalearning challenge of recommending feature selection algorithms by proposing a novel meta-feature engineering model. Our model considers a broad collection of meta-features that enable the study of the relationship between the dataset properties and the feature selection algorithm performance in terms of several criteria. We arrange the input meta-features into eight categories: (i) simple, (ii) statistical, (iii) information-theoretical, (iv) complexity, (v) landmarking, (vi) based on symbolic models, (vii) based on images, and (viii) based on complex networks (graphs). The target meta-features emerge from a multi-criteria performance measure, based on five individual performance indexes, that assesses feature selection methods grounded in information, distance, dependence, consistency, and precision measures. We evaluate our proposal using a recently developed framework that extracts the input meta-features from 213 benchmark datasets, and ranks the assessed feature selection algorithms, to fill in the target meta-features in meta-bases. This evaluation uses five state-of-the-art classification methods to induce recommendation models from meta-bases: C4.5, Random Forest, XGBoost, ANN, and SVM. The results showed that it is possible to reach an average accuracy of up to 90% applying our meta-feature engineering model. This work is the first to use an extensive empirical evaluation to provide a careful discussion of the strengths and limitations of more than 160 meta-features. These meta-features, while designed to aid the task of feature selection algorithm recommendation, can readily be employed in other metalearning scenarios. Therefore, we believe our findings are a valuable contribution to the fields of automated machine learning and data mining, as well as to the feature extraction and pattern recognition communities. • A novel meta-feature engineering model recommends feature selection algorithms. • The proposal obtains promising results from 213 datasets with hit rates of up to 90%. • Some simple, landmarking, image, and graph-based input meta-features highlighted. • A multi-criteria performance measure rigorously assesses candidate algorithms. • Chains of binary or multiclass classifiers can efficiently rank candidate algorithms. [ABSTRACT FROM AUTHOR]

• Discretization is an important process in terms of both machine learning and data mining. • The effect of discretization on ensemble methods was analyzed. • Four Chi-square based discretization algorithms were used. • The Ensemble methods performed better on discrete data sets. Classification is one of the important tasks in data mining and machine learning. Classification performance depends on many factors as well as data characteristics. Some algorithms are known to work better with discrete data. In contrast, most real-world data contain continuous variables. For algorithms working with discrete data, these continuous variables must be converted to discrete ones. In this process called discretization, continuous variables are converted to their corresponding discrete variables. In this paper, four Chi-square based supervised discretization algorithms ChiMerge(ChiM), Chi2, Extended Chi2(ExtChi2) and Modified Chi2(ModChi2) were used. In the literature, the performance of these algorithms is often tested with decision trees and Naïve Bayes classifiers. In this study, differently, four sets of data discretized by these algorithms were classified with ensemble methods. Classification accuracies for these data sets were obtained through using a stratified 10-fold cross-validation method. The classification performance of the original and discrete data sets of the methods is presented comparatively. According to the results, the performance of the discrete data is more successful than the original data. [ABSTRACT FROM AUTHOR]

• The Rosenbrock method is applied to the Harris hawks optimization algorithm. • The proposed method is abbreviated as RLHHO. • A new random exploration technique is proposed and applied to the RLHHO. • RLHHO is tested over 53 test functions and 4 real-world problems. • RLHHO compares with multiple algorithms to verify its validity. Harris Hawks Optimization (HHO) algorithm is a newly proposed meta-heuristic optimization algorithm that simulates the hunting process of the Harris hawks. It has the characteristics of fewer adjustment parameters and a strong optimization effect, resulting in strong competitiveness in similar optimization algorithms. However, HHO is prone to premature convergence and low convergence accuracy when dealing with specific complex optimization problems. Therefore, our work integrates two novel strategies into the standard HHO to gain enhanced exploration and exploitation capabilities. Specifically, our work firstly proposed an exploration strategy based on logarithmic spiral and opposition-based learning to improve the exploration ability of HHO. Secondly, the local search technique for Rosenbrock Method (RM) is modified to dynamically fuse into the standard HHO to enhance the algorithm's local search capability and improve the convergence accuracy. The novel meta-heuristic algorithm proposed in this paper is called RLHHO. Finally, to validate the algorithm's effectiveness, the proposed RLHHO algorithm is fully performance tested with eight other traditional meta-heuristic optimization algorithms on 23 benchmark functions and 30 IEEE CEC2014 test functions. Besides, another six advanced meta-heuristics algorithms are also compared in the 30 CEC'2014 test functions. The experimental results show that RLHHO performs significantly better than HHO as well as other traditional and advanced meta-heuristic algorithms in most of the test functions. To test the scalability of RLHHO in complex real-world problems, it was used to optimize the solution of three constrained real-world engineering problems, and the experimental results show that RLHHO's powerful performance can be used as an effective tool for solving constrained engineering problems. Also, an effective hybrid model of kernel extreme learning machine is developed on the basis of RLHHO to cope with bankruptcy prediction problem. The experimental results show that this hybrid model is highly competitive with other mainstream classifiers regarding stability and prediction accuracy. The supplementary info and answers to possiblequeries will be publicly available at https://www.researchgate.net/profile/Chenyang_Li39/research. [ABSTRACT FROM AUTHOR]

This paper proposes AutoGCOP, a new general framework for automated design of local search algorithms. In a recently established General Combinatorial Optimisation Problem (GCOP) model, the problem of algorithm design itself is defined as a combinatorial optimisation problem. AutoGCOP defines a general framework to optimise the composition of elementary algorithmic components as decision variables in GCOP. By modelling various well-known local search meta-heuristics within a general framework, AutoGCOP supports automatic design of new novel algorithms which may be highly different from those manually designed in the literature. Within the consistent AutoGCOP framework, various elementary algorithmic components are analysed for solving the benchmark vehicle routing problem with time window constraints and different characteristics. Furthermore, two learning models based on reinforcement learning and Markov chain are investigated to learn and enhance the compositions of algorithmic components towards the automated design of search algorithms. The Markov chain model presents superior performance learning the compositions of algorithmic components during the search, demonstrating its effectiveness designing new algorithms automatically. • A framework for automated design of local search algorithms. • Various local search algorithms are modelled within the framework. • The vehicle routing problems are used as the domain problem. • Performance of elementary algorithmic components is analysed. • Two learning models based on reinforcement learning and Markov chain are compared. [ABSTRACT FROM AUTHOR]

• Improved oversampling algorithm (G-SOMO) to address the imbalance-learning problem. • It uses a Self-Organizing Map to identify optimal areas to generate artificial data. • The Geometric-SMOTE algorithm is used to generate the artificial instances. • G-SOMO is tested in 69 datasets and consistently outperforms the benchmarks. Traditional supervised machine learning classifiers are challenged to learn highly skewed data distributions as they are designed to expect classes to equally contribute to the minimization of the classifiers cost function. Moreover, the classifiers design expects equal misclassification costs, causing a bias for overrepresented classes. Different strategies have been proposed to correct this issue. The modification of the data set has become a common practice since the procedure is generalizable to all classifiers. Various algorithms to rebalance the data distribution through the creation of synthetic instances were proposed in the past. In this paper, we propose a new oversampling algorithm named G-SOMO. The algorithm identifies optimal areas to create artificial data instances in an informed manner and utilizes a geometric region during the data generation process to increase their variability. Our empirical results on 69 datasets, validated with different classifiers and metrics against a benchmark of commonly used oversampling methods show that G-SOMO consistently outperforms competing oversampling methods. Additionally, the statistical significance of our results is established. [ABSTRACT FROM AUTHOR]

• Additional reward parameter is added to the learning algorithm. • The replicator equation is extended from single-state to transition-state. • The algorithm and its dynamic are demonstrated on a 2 states battle of sexes game. Agent-based evolutionary game theory studies the dynamics of the autonomous agents. It is important for application that relies on the agents to perform the automated tasks. Since the agents make their own decision, therefore the stability of the interaction needs to be comprehended. The current state of the art in agent-based replicator dynamics are piecewise and state-coupled replicator dynamics which focus on joint-action single-state reward. This paper introduces additional reward parameter to the learning algorithm, extends the replicator dynamics to joint-action transition-state reward and shows that it can be changed to single-state reward and independent-action reward. The replicator equation is expressed based on the tree diagram approach and is verified with the numerical simulation in a two states battle of sexes coordination game for various types of rewards. The numerical results are consistent with the phase portraits generated by the replicator equation and are able to provide some general insights to the coordination game such as the number of convergence points, the rate of convergence and the effect of initial points on the convergence. [ABSTRACT FROM AUTHOR]

• A deep learning algorithm predicts the latency and availability of cloud services. • Experiments on 15 servers from 3 cloud service providers in 3 regions for 6 weeks. • The prediction is usually above 90% accurate as compared with the data obtained. Low latency and high availability in cloud services give users satisfactory response time and guarantee stability to request they make to services that are hosted in the cloud, thus increasing the usability and reliability of cloud services. On the other hand, high latencies and poor availability will cost businesses their customers due customers dissatisfaction, thus losing customers to competitors. This situation noticeable in e-commerce businesses where real-time response and decision making within seconds are critical for business service delivery. Therefore, latency and availability are important parameters in the Service Level Agreement for cloud users when choosing Cloud Services Providers (CSPs). But the challenge for businesses is that they do not have their in–house mechanism that can accurately predict the required latency and availability for their requirements. Companies only rely on the CSPs tools for estimating resource requirements, which is biased towards the CSPs business model. In this paper, we developed a deep learning algorithm that predicts the latency and availability of cloud services using real-time live data from three CSPs. We designed and implemented experiments on Amazon Web Services, Alibaba Cloud and Tencent Cloud in Beijing University of Posts and Telecommunications to run compute instances across the United States, Europe and Asia Pacific regions. In each cloud platform, five servers were used that resulted in 30,815,100 invocations of http and ping operations for 6 weeks. The algorithm used the data on hourly, daily and weekly basis as historical network data to predict latency and availability. We used MATLABs deep learning toolbox for the implementation of our algorithm and the results showed that the prediction is usually above 90% accurate as compared with the data obtained. The results also revealed that latency performance depends on the locations of users and the availability depends on number of availability zones used. [ABSTRACT FROM AUTHOR]

• Method extracts a vectorial embedding representation of image classification tasks. • Method evaluated in a metalearning case study with six CNN algorithms and 45 datasets. • Feature vectors of datasets with similar difficulty are close in the embedding space. • The results overcame the baseline methods in all assessed performance measures. • Meta-learning with Dataset2Vec reached high precision, overcoming image descriptors. Convolutional Neural Networks (CNNs) have become the main solution for image classification tasks in different applications. Although several CNN architectures are available, there is no best architecture regardless the problem at hand. The selection of the most suitable CNN architecture is usually performed by trial and error, which may take much time and computational resources. Meta-learning (MtL) is a framework developed in machine learning to perform algorithm selection based on the meta-features of each task being solved. Such meta-features are usually descriptive characteristics extracted from the training dataset available in the task at hand. Despite the increasing attention of MtL for algorithm selection, its success strongly depends on defining relevant meta-features to represent the classification tasks of interest. This paper proposes the ImageDataset2Vec method for extracting meta-features to describe image classification datasets. ImageDataset2Vec adopts a pre-trained deep neural network to extract features from images datasets, embedding them in a single feature vector. The derived meta-features are employed by MtL to select CNN architectures for image classification. The proposed approach was evaluated for selecting among six CNN algorithms in 45 two-classes image datasets. The results showed that MtL using ImageDataset2Vec overcame different baseline methods, selecting the best possible CNN algorithm in 84.45 % of the datasets. Furthermore, the proposal was statistically equivalent to the ground truth when the best CNN is recommended, i.e., when MtL does not select the best CNN, it selects a competitive algorithm. These results show that the proposal was able to extract representative features from image datasets. [ABSTRACT FROM AUTHOR]