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Surrogate-assisted evolutionary algorithms (SAEAs) are popular for solving expensive optimization problems. However, most existing SAEAs are designed for solving single-objective or multiobjective optimization problems with two or three objectives. Few works had been reported to deal with expensive many-objective optimization problems with more than three objectives because of two difficulties. One is the curse of dimensionality caused by many-objective problems, and the other is the fewer computational resources available in a limited time for expensive optimization problems. Since an effective selection method can better solve the many-objective optimization problems, high-efficiency search and accurate model can save computational resources for expensive optimization problems, this paper proposes a diverse/converged individual competition algorithm, which owns a novel diverse/converged individual competition selection mechanism, a hybrid search mechanism, and a segmentation approach. The diverse/converged individual competition selection mechanism maintains a good balance between the convergence and diversity of the selected solutions for solving many-objective optimization problems. The hybrid search mechanism performs a memetic search and genetic search at different stages of the evolution process to further generate superior solutions. The segmentation approach uses two different populations with small numbers to build two surrogate models which will predict different areas, and it can improve the accuracy of the prediction. The proposed algorithm is compared with several state-of-art algorithms on widely used benchmark functions. The experimental results show that the proposed algorithm performs significantly better than the compared algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel Adaptive Parameter Strategy Differential Evolution (APSDE) algorithm is proposed to overcome the parameters dependence and avoid local optima. The Parameter Update Mechanism (PUM), which has three different strategies, is used to reduce the dependence on parameters of DE. The Adaptive Proportion Adjustment Mechanism (APAM) is used to balance the proportion of PUM strategies in different development terms of exploitation and exploration, and the Random Restart Mechanism (RRM) is used to improve population diversity when exploitation is in stagnation. The proposed algorithm is verified in the CEC2018 test functions and the results show that APSDE has good abilities of exploitation, exploration, convergence, and stability. Secondly, Midcourse Guidance Maneuver Decision-making (MGMD) in Beyond Visual Range (BVR) air combat is studied and transformed into a single objective variational optimization problem, a MGMD system based on APSDE is established. Finally, the simulation of MGMD is carried out. The APSDE ranks first in the typical MGMD scenario experiment. In the adaptive Midcourse guidance confrontation, the winning rate of APSDE is 54%, and the statistical results show that the APSDE has an excellent MGMD ability. [ABSTRACT FROM AUTHOR]

Abstractive summarization (AS) aims to generate more flexible and informative descriptions than extractive summarization. Nevertheless, it often distorts or fabricates facts in the original article. To address this problem, some existing approaches attempt to evaluate or verify factual consistency, or design models to reduce factual errors. However, most of the efforts either have limited effects or result in lower rouge scores while reducing factual errors. In other words, it is challenging to promote factual consistency while maintaining the informativeness of generated summaries. Inspired by the knowledge graph embedding technique, in this paper, we propose a novel cross-modal knowledge guided model (CKGM) for AS, which embeds a multimodal knowledge graph (MKG) combining image entity-relationship information and textual factual information (FI) into BERT to accomplish cross-modal information interaction and knowledge expansion. The pre-training method obtains rich contextual semantic information, while the knowledge graph supplements the textual information. In addition, an entity memory embedding algorithm is further proposed to improve information fusion efficiency and model training speed. We elaborately conducted ablation experiments and evaluated our model on the Visual Genome, FewRel, MSCOCO, and CNN/DailyMail datasets. Experimental results demonstrate that our model can significantly improve the FI consistency and informativeness of generated summaries. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most efficient evolution algorithms (ES) for dealing with nonlinear, complicated and difficult global optimization problems. The main contribution of this paper can be summarized in three directions: Firstly, a novel crossover rate (CR) generation scheme based on the zscore value of fitness, named fcr, is introduced. For a minimization problem, the proposed CR generation strategy always assigns a smaller CR value to individual with smaller fitness value. Therefore, the parameters of individuals with better fitness are inherited by their offspring with high probability. In the second direction, the control parameters are adjusted by unused bimodal settings in which each parameter setting is selected according to the evolution status of individual. The third direction of our work is introducing the L1 norm distance as the weights for updating the mean value of crossover rate and scale factor. Theoretically, compared with L2 norm, L1-norm is more efficient to suppress outliers in the difference vector. These modifications are first integrated with the mutation strategy of JADE, then a modified version, named JADEfcr, is proposed. In addition, to improve the optimization ability further, another variant LJADEfcr by using a linear population reduction mechanism is considered. So as to confirm and examine the performance of JADEfcr and LJADEfcr, numerical experiments are conducted on 29 optimization problems defined by CEC2017 benchmark. For JADEfcr, its experimental results are made a comparison with twelve state-of-the-art algorithms. The comparative study demonstrates that in terms of robustness, stability and solution quality, JADEfcr are better and highly competitive with these well-known algorithms. For LJADEfcr, its results are compared with JADEfcr and other nine powerful algorithms including four recent algorithms and five top algorithms on CEC2017 competition. Experimental results indicate that LJADEfcr is superior and statistically competitive with these excellent algorithms in terms of robustness, stability and the quality of the obtained solutions. [ABSTRACT FROM AUTHOR]

Based on the problem of insufficient accuracy of the original tiny YOLOv3 algorithm for object detection in a lawn environment, an Optimized tiny YOLOv3 algorithm with less computation and higher accuracy is proposed. Three reasons affect the accuracy of the original tiny YOLOv3 algorithm for detecting objects in a lawn environment. First, the backbone of the original algorithm is composed of a stack of a single convolutional layer and a max-pooling layer, which results in insufficient ability to extract feature information of objects. An enhancement module is proposed to enhance the feature extraction capability of the shallow layers of the network. Second, the information of the shallow convolutional layers of the backbone is not fully used, which results in insufficient detection capability for small objects. Third, the deep part of the backbone uses a convolutional layer with an excessive number of channels, which results in a large amount of computation. A multi-resolution fusion module is proposed to enhance the information interaction capability of the deep and shallow layers of the network, and reduce the computation. To verify the accuracy of this Optimized tiny YOLOv3 algorithm, the algorithm was tested on the dataset containing trunk, spherical tree and person, and compared with the current research. The results show that the algorithm proposed in this paper improves the detection accuracy while reducing the calculation. [ABSTRACT FROM AUTHOR]

The collaboration among different organizations is considered one of the main benefits of moving applications and services to a cloud computing environment. Unfortunately, this collaboration raises many challenges such as the access of sensitive resources by unauthorized people. Usually, Role-Based Access-Control (RBAC) model is deployed in large organizations. This paper addresses the scalability problem of the online stored rules. This problem affects the performance of the access control system due to increasing number of shared resources and/or number of collaborating organizations in the same cloud environment. Therefore, this paper proposes replacing the cross-domain RBAC rules with Role-To-Role (RTR) mapping rules among all organizations. The RTR mapping rules are generated using a newly proposed Role-Mapping algorithm. A comparative study is performed to evaluate the proposed algorithm's performance with concerning the Rule-Store size and the authorization response time. According to the results, it is found that the proposed algorithm reduces the number of stored rules which minimizes the Rule-Store size and reduces the authorization response time. Additionally, this paper proposes applying a concurrent approach on the RTR mapping model using the proposed Role-Mapping algorithm to achieve more savings in the authorization response time. Therefore, it will be suitable in highly-collaborative cloud environments. [ABSTRACT FROM AUTHOR]

This paper presents a fault detection and faulty area identification algorithm in double-circuit transmission lines (DCTLs) compensated by the generalized unified power flow controller (GUPFC) for the first time. The topology of the GUPFC-compensated DCTL is threatened by three types of faults: single-circuit faults, inter-circuit faults, and grounded double-circuit cross-country faults. In the first step, the suggested scheme can determine the time of fault in this type of topology using a very fast method that is based on the analysis of a current index called the current jump index. In the second step, the fault type and area are identified with the help of two other indices, i.e., voltage difference index and current difference index. The test network presented in this paper, which includes a DCTL and a GUPFC, along with the proposed algorithm, is implemented in the MATLAB software package. The results of the simulation and sensitivity analysis of the algorithm confirm the correct performance of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a random noise suppression and super-resolution reconstruction algorithm for seismic profiles based on Generative Adversarial Networks, in anticipation of reducing the influence of random noise and low resolution on seismic profiles. Firstly, the algorithm used the residual learning strategy to construct a de-noising subnet to accurate separate the interference noise on the basis of protecting the effective signal. Furthermore, it iterated the back-projection unit to complete the reconstruction of the high-resolution seismic sections image, while responsed sampling error to enhance the super-resolution performance of the algorithm. For seismic data characteristics, designed the discriminator to be a fully convolutional neural network, used a larger convolution kernels to extract data features and continuously strengthened the supervision of the generator performance optimization during the training process. The results on the synthetic data and the actual data indicated that the algorithm could improve the quality of seismic cross-section, make ideal signal-to-noise ratio and further improve the resolution of the reconstructed cross-sectional image. Besides, the observations of geological structures such as fractures were also clearer. [ABSTRACT FROM AUTHOR]

The external and middle ear conditions are diagnosed using a digital otoscope. The clinical diagnosis of ear conditions is suffered from restricted accuracy due to the increased dependency on otolaryngologist expertise, patient complaint, blurring of the otoscopic images, and complexity of lesions definition. There is a high requirement for improved diagnosis algorithms based on otoscopic image processing. This paper presented an ear diagnosis approach based on a convolutional neural network (CNN) as feature extraction and long short-term memory (LSTM) as a classifier algorithm. However, the suggested LSTM model accuracy may be decreased by the omission of a hyperparameter tuning process. Therefore, Bayesian optimization is used for selecting the hyperparameters to improve the results of the LSTM network to obtain a good classification. This study is based on an ear imagery database that consists of four categories: normal, myringosclerosis, earwax plug, and chronic otitis media (COM). This study used 880 otoscopic images divided into 792 training images and 88 testing images to evaluate the approach performance. In this paper, the evaluation metrics of ear condition classification are based on a percentage of accuracy, sensitivity, specificity, and positive predictive value (PPV). The findings yielded a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a PPV of 100% for the testing database. Finally, the proposed approach shows how to find the best hyperparameters concerning the Bayesian optimization for reliable diagnosis of ear conditions under the consideration of LSTM architecture. This approach demonstrates that CNN-LSTM has higher performance and lower training time than CNN, which has not been used in previous studies for classifying ear diseases. Consequently, the usefulness and reliability of the proposed approach will create an automatic tool for improving the classification and prediction of various ear pathologies. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low data quality and high incentive costs caused by the low enthusiasm of participants in mobile crowd sensing, a new task recommendation framework is proposed in this paper. First, the participants' historical behaviors are analyzed, assuming that user behaviors can be quantified as the user's willingness to participate, and the cosine similarity theorem is used to calculate the similarity between participants, thereby constructing a user-hybrid model. Secondly, probabilistic matrix factorization is developed to predict the willingness of participants, and a ranking model is obtained through learn-to-rank algorithm. Finally, a task recommendation list is generated according to the ranking model, which serves as the target participant's preferred task list for sensing task recommendation. The experiment in this paper is carried out on the MATLAB platform based on two real check-in datasets, Gowalla and Brightkite. The results show that the average allocation precision rate can reach 96%, and the sensing user participation rate is about 97%. Meantime, the user's mobile cost is reduced, and the overall goal of maximizing accuracy and minimizing perceived cost is achieved. [ABSTRACT FROM AUTHOR]

A recognition model named the SVM-NP is proposed in this paper to address the multi-attribute overlap in radar working recognition. The model is based on the K-NN boundary preselection algorithm and SVM-BP algorithm. Traditional classifiers tend to neglect the overlap of samples' attributes in classification, which leads to the low accuracy of classifiers. The K-NN boundary preselection can quickly select boundary samples from the total ones and reduce the whole samples' attribute overlap. The SVM-BP algorithm is improved based on the SVM-RFE algorithm, and the boundary samples with high attribute overlap are divided into many planes for training and testing. Compared with traditional methods, the overlap of sample attributes can be reduced twice in this model. Theoretical analysis and experimental results verify that the model proposed in this paper displays better performance in classification when appropriate parameters are provided. [ABSTRACT FROM AUTHOR]

The distributed alternating direction method of multipliers (ADMM) is an effective algorithm for solving large-scale optimization problems. However, its high communication cost limits its scalability. An asynchronous lazy ADMM algorithm based on hierarchical sparse allreduce communication mode (HSAC-ALADMM) is proposed to reduce the communication cost of the distributed ADMM: firstly, this paper proposes a lazily aggregate parameters strategy to filter the transmission parameters of the distributed ADMM, which reduces the payload of the node per iteration. Secondly, a hierarchical sparse allreduce communication mode is tailored for sparse data to aggregate the filtered transmission parameters effectively. Finally, a Calculator-Communicator-Manager framework is designed to implement the proposed algorithm, which combines the asynchronous communication protocol and the allreduce communication mode effectively. It separates the calculation and communication by multithreading, thus improving the efficiency of system calculation and communication. Experimental results for the L1-regularized logistic regression problem with public datasets show that the HSAC-ALADMM algorithm is faster than existing asynchronous ADMM algorithms. Compared with existing sparse allreduce algorithms, the hierarchical sparse allreduce algorithm proposed in this paper makes better use of the characteristics of sparse data to reduce system time in multi-core cluster. [ABSTRACT FROM AUTHOR]

Current approaches for the prospective regulatory assessment of chemicals do not account sufficiently for elevated mixture risks. The Mixture Assessment Factor (MAF, better labeled a Mixture Allocation Factor) has been suggested for mixtures of industrial chemicals in the EU's Chemicals Strategy for Sustainability, as a pragmatic tool to account for potential mixture risks already during the risk and safety assessment of individual chemicals. The MAF is to be applied in scenarios in which specific mixture risk assessments are not possible, due to a lack of data and/or the complexity of the relevant exposure scenarios. Several approaches and algorithms for calculating a MAF have been suggested in the literature. The MAFexact, which is a member of the larger MAFceiling class, is defined as the maximum fraction of the risk quotient of each chemical that is still acceptable to occur in a mixture, without the sum of risk quotients exceeding 1. This paper provides a comparative overview of the different MAF types discussed in the literature. It argues that the MAFexact is the most promising approach in the context of chemical registration and authorization under regulatory frameworks such as REACH because this approach ensures a protection level that is similar to the protection level used in the current safety assessment of individual chemicals under REACH. Other MAF approaches either disproportionally impact low-risk substances, without leading to any appreciable risk reduction, or hamper risk communication because they lead to fluctuating residual risks after the MAF application. The paper also presents a case study comparing the different MAF approaches and finally discusses the MAF concept in the wider context of chemical regulation. [ABSTRACT FROM AUTHOR]

In this research paper, performance of an efficient audio contents integrity verification algorithm using Discrete Cosine Transform (DCT) has been analyzed and studied in presence the different noise and attacks. This approach of audio contents verification is designed based on a self-mark idea, the embedded mark is generated from the same audio file after transforming the sensitive 2-D audio file into two equal size partitions. Integrity and confidentiality verification of classified recorded audio scenario also, has been tested and evaluated in this research paper using the encryption technique to encrypt the marked audio files. Different computer experiments simulation have been executed to evaluate and analysis the performance of the stand-alone self-mark algorithm and with the encryption stage merging. These experiments tested the performance of these two security approaches with presence different attacks. Reverse processes are executed to verify the contents integrity of the audio files. According to the quality of the extracted audio file from the verification process compared to the original audio file, this audio contents integrity algorithm is applicable and robust. Quality of the extracted audio file is evaluated using the different metrics tools. The different attacks are employed to measure the robustness and reliability of this algorithm. The statistical analysis of the results has been presented, it is proved to have high detection sensitivity to observe and discover any audio manipulations even they were inaudible. [ABSTRACT FROM AUTHOR]

With the purpose of providing higher data rate and ultra-reliable and low-latency communications for the users, employing the small cells in the upcoming Fifth-Generation-New Radio (5G-NR) cellular networks and beyond is leading to the serious challenges in mobility management due to dynamicity of the user equipments (UEs). Among different issues related to the mobility of the UEs, the handover management is one of the key procedures to let the UEs experience a high quality of service (QoS)/quality of experience (QoE). So far, many protocols and algorithms have been proposed to enhance the mobility management in 5G-NR from various aspects, but still a thorough survey has not been incorporated to summarize the recent advances and future directions of the mentioned issue. Thus, the handover management and the corresponding challenges stand in the center of concentrations in this paper, with a glance on 4G to the advancements of 5G-NR. The challenges include QoS/QoE, throughput, delay, traffic load, the attacks during authentication process and resource allocation. To overcome the mentioned challenges, the handover procedure is evaluated by some key performance indicators such as handover ratio, handover failure, radio link failure and such like which depend on the received signal quality. The lack of new advancements and novel aspects of handover plus the separation of security and handover issues in previous works are perceived as research gaps and motivations for the current paper. In this regard, this paper aims to specify and analyze the technical issues, to provide an overview on the proposed methods and recent advances and to bring the future directions into the limelight. We categorize the concurrent standards and methods about the handover, and then, we survey the proposed algorithms including theoretical, algorithm-based and pattern-capturing approaches following the authentication process along with the vulnerabilities and the algorithms to counteract the attacks during handover. Also, we study various issues corresponding to network performance during the handover procedure. Finally, we discuss the open problems and future research directions. [ABSTRACT FROM AUTHOR]

The physical cloud servers consists of n number of machines for serving the requirements posted via Virtual Machines by the users. The allocation of requested Virtual Machines to appropriate Physical Servers is one among the challenging task exists in Cloud Computing. During the deployment phase several objectives needs to be considered which includes the total power consumption, the resource wastage, network propagation delay and so on. Allocating VM's to PM's with such fashion will lead the service providers financially. In this paper a swarm-based algorithm is modified for solving VM placement in terms of multi-objective perspective. Minimizing total power consumption and minimizing the resource wastage are the objectives considered in this paper. The results show the significance of the proposed model in terms of objective space and with the performance indicators such as ONGV and Spacing ( S p ). [ABSTRACT FROM AUTHOR]

This paper considers the identification problem of the linear continuous time-delay systems. By using the multi-frequency responses, a stochastic gradient gradient-based iterative (SG-GI) algorithm is derived. The proposed algorithm can estimate the unknown parameters and the unknown time delays simultaneously. To improve the parameter estimation accuracy of the SG-GI algorithm, a multi-innovation stochastic gradient gradient-based iterative (MISG-GI) algorithm is derived by using the multi-innovation identification theory. In addition, a forgetting factor is introduced to increase the parameter estimation accuracy. The resulting algorithm is called the multi-innovation forgetting gradient gradient-based iterative (MIFG-GI) algorithm. The effectiveness of the proposed strategies is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) target localization by using hybrid time difference of arrival (TDOA) and angle of arrival (AOA) measurements from multiple sensors has been an active research area for several decades due to its extensive applications in various fields. For this nonlinear estimation problem, the pseudolinear system of equations constructed by using the measurements generally acts as the basis of numerous localization algorithms. In this paper, we aim to improve the performance of 3D TDOA–AOA localization by introducing the constrained total least squares (CTLS) framework wherein the inherent characteristics of the pseudolinear equations can be properly taken into consideration. On the basis of the total least squares model, the CTLS model for 3D TDOA–AOA localization is established by imposing the inherent characteristics of the pseudolinear equations as additional constraints. Then, the multi-constraint optimization problem in CTLS model is solved by using an iterative algorithm based on successive projections. Extensive numerical simulations are accomplished for evaluating the performance of the proposed CTLS algorithm. The results show that the proposed algorithm gives moderate accuracy enhancement with acceptable computational cost, and more importantly, it is more robust to large measurement noise than the compared algorithms. [ABSTRACT FROM AUTHOR]

Improving classification performance is an essential goal for various practical applications. Feature selection has become an important data preprocessing step in machine learning systems. However, many effective methods based on heuristic search strategies have the problem of high running costs. This paper proposes an efficient multiobjective feature selection method based on artificial immune algorithm optimization. It introduces a clone selection algorithm to explore the search space of optimal feature subsets. According to the target requirements of feature selection, combined with biological research results, this method introduces genome shuffling technology and a conditional lethal mutation mechanism to improve the search performance of the algorithm. Experimental comparisons are conducted on 21 benchmark datasets with 17 advanced feature selection methods in terms of classification accuracy, the number of feature subsets, and computational cost. The results show that the algorithm achieves the smallest number of selected features (only 3.26% compared to the lowest) and better average classification accuracy with a much lower average computational cost than others (only 3.62% compared to the lowest). [ABSTRACT FROM AUTHOR]

In the new era, the key measure to accelerate the construction of smart city, so as to promote the modernization of urban governance system and governance capacity, is to establish a good urban innovation ecosystem, and guide its continuous evolution to the direction of the highest efficiency and the best performance. Focusing on solving the practical problem of "how the urban innovation ecosystem evolves", this paper develops a NK algorithm using BP neural network and DEMATEL method. First, through literature research, constructing the urban innovation ecosystem including five subsystems of innovation talents, innovation subjects, innovation resources, innovation environment and innovation network. Then, taking Beijing as an example, the weights and the number of epistatic relationships of each subsystem in its innovation ecosystem are calculated by BP neural network and DEMATEL method, and the NK model is modified; on this basis, the fitness values corresponding to different states of the system are calculated using MATLAB software, and the optimal evolution path of Beijing innovation ecosystem is determined through the comparison of 100,000 simulation results. The results show that the optimal evolution path of Beijing's innovation ecosystem is to create a favorable environment and culture for innovation first; then increase the input of innovation resources; and then promote the development of innovation network assets; on this basis, cultivate, attract and retain innovative talents; and finally strengthen the construction of innovation subjects. [ABSTRACT FROM AUTHOR]

In this paper we complete the exploration of connected components of the space of BPS Wilson loops in three-dimensional N = 4 Chern-Simons-matter theory on S3. The algorithm is to start with a supersymmetric Wilson loop, choose a preserved supercharge, and look for BPS deformations built out of the matter fields in the proper representations. Using this, we discover many new moduli spaces of nonconformal BPS Wilson loops preserving a single or two supercharges, which are subsets of the symmetries of the 1/4 and 3/8 BPS operators. Along with the those previously found in [1-2] and [3], the total moduli spaces are closed under this formalism. [ABSTRACT FROM AUTHOR]

By the asymptotic oracle property, non-convex penalties represented by minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) have attracted much attentions in high-dimensional data analysis, and have been widely used in signal processing, image restoration, matrix estimation, etc. However, in view of their non-convex and non-smooth characteristics, they are computationally challenging. Almost all existing algorithms converge locally, and the proper selection of initial values is crucial. Therefore, in actual operation, they often combine a warm-starting technique to meet the rigid requirement that the initial value must be sufficiently close to the optimal solution of the corresponding problem. In this paper, based on the DC (difference of convex functions) property of MCP and SCAD penalties, we aim to design a global two-stage algorithm for the high-dimensional least squares linear regression problems. A key idea for making the proposed algorithm to be efficient is to use the primal dual active set with continuation (PDASC) method to solve the corresponding sub-problems. Theoretically, we not only prove the global convergence of the proposed algorithm, but also verify that the generated iterative sequence converges to a d-stationary point. In terms of computational performance, the abundant research of simulation and real data show that the algorithm in this paper is superior to the latest SSN method and the classic coordinate descent (CD) algorithm for solving non-convex penalized high-dimensional linear regression problems. [ABSTRACT FROM AUTHOR]

Linear motion commands of multi-axis computer numerical control (CNC) machine tools need to be smoothed at the transition corners, because the velocity discontinuities at corners can result in fluctuations on machine tool motions and lead to poor surface quality. However, few studies have reported on the transition between non-line contour. The paper proposes a novel two-step local corner smoothing method for non-line contour transition, including path smoothing and feedrate scheduling. In path smoothing, a non-uniform rational B-spline (NURBS) curve is adopted to smooth the adjacent non-line contour of screw rotor section while constraining the cornering error with the set tolerance. The maximum deviation between the original trajectory and smoothed toolpath can be analytically calculated. In feedrate scheduling, a jerk-limited S-curve feedrate scheduling scheme is developed based on machine tool dynamics. Finally, in order to verify the correctness of the proposed method, a series of numerical simulations and actual machining and measurement experiments are conducted, and the simulations and experimental results have showed the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI, that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments. The ZoMBI algorithm demonstrates compute time speed-ups of 400× compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3× more highly optimized than those discovered by similar methods. [ABSTRACT FROM AUTHOR]

360-degree videos have become increasingly popular with the application of virtual reality (VR) technology. To encode such kind of videos with ultra-high resolution, an efficient and real-time video encoder becomes a key requirement. The Versatile Video Coding (VVC) standard has good coding performance. However, it has pretty high computational complexity which increasing the application cost of 360-degree videos. Among them, the decision of the quadtree with nested multi-type tree (QTMT) partitioning structure is one of the time-consuming procedures. In this paper, based on the characteristics of 360-degree video with Equirectangular projection (ERP) format, the empirical variogram combined with Mahalanobis distance is introduced to measure the difference between the horizontal and vertical directions of the CU, and a fast partition algorithm is proposed. The experimental results show that the algorithm saves 32.13% of the coding time with only an increase of 0.66% in BDBR. [ABSTRACT FROM AUTHOR]

In the network economy domain, urban competitiveness refers to the comparison between cities in terms of competition and development. It is the ability to gain competitive advantage under different factors. The evaluation of urban competitiveness will help cities to learn from each other, and provides reference for the government to enhance urban competitiveness. Unlike various studies in the literature exploiting only the non-linear characteristics of urban competitiveness, this paper selects BP (Back Propagation) network as the main framework for evaluation. A Genetic Algorithm BP (GABP) network based on genetic optimization is utilized. The weights are optimized besides the crossover mutation of GA algorithm. To compensate the slow prediction in the stand-alone mode, this work proposes a MapReduce (MP) based method; MR-GABP via cloud computing. The model ensures effective urban competitiveness evaluation with improved convergence speed and threshold generation speed. The systematic experiments conducted verify effectiveness of the method while the results obtained reveal that performance of the method is better than the other methods in terms of accuracy and recall yielded as 95.1% and 92.6% respectively. [ABSTRACT FROM AUTHOR]

We present a keyphrase extraction algorithm named TopicLPRank in this paper, which is an improved TopicRank algorithm. Different from the TopicRank which only uses the relative distance information of the text, we think that the length and absolute position of the text candidate keyphrases also have a certain influence on the results of the model for extraction keyphrases. Therefore, the proposed TopicLPRank incorporates these two factors on the basis of the TopicRank. The experimental results show that adding the location information and length information of candidate keyphrases can, respectively, increase the F-Score of the model by around 2.7 % points and 1.7 % points, which is equivalent to an increase of 19.6 and 12.3 % compared with the TopicRank. At the same time, the fusion of the length and location information of the candidate keyphrase can increase the F-Score by around 3.5 percentage points, which is equivalent to an increase of 25.21 % compared with the TopicRank in the dataset NUS. [ABSTRACT FROM AUTHOR]

Combined with one-time pad encryption scheme, quantum key distribution guarantees the unconditional security of communication in theory. However, error correction and privacy amplification in the post-processing phase of quantum key distribution result in high time delay, which limits the final secret key generation rate and the practicability of quantum key distribution systems. To alleviate this limitation, this paper proposes an efficient post-processing algorithm based on polar codes for quantum key distribution. In this algorithm, by analyzing the channel capacity of the main channel and the wiretap channel respectively under the Wyner's wiretap channel model, we design a codeword structure of polar codes, so that the error correction and privacy amplification could be completed synchronously in a single step. Through combining error correction and privacy amplification into one single step, this efficient post-processing algorithm reduces complexity of the system and lower the post-processing delay. Besides, the reliable and secure communicaiton conditions for this algorithm has been given in this paper. Simulation results show that this post-processing algorithm satisfies the reliable and secure communication conditions well. [ABSTRACT FROM AUTHOR]

The paper focuses on the correction of Illumina WGS sequencing reads. We provide an extensive evaluation of the existing correctors. To this end, we measure an impact of the correction on variant calling (VC) as well as de novo assembly. It shows, that in selected cases read correction improves the VC results quality. We also examine the algorithms behaviour in a processing of Illumina NovaSeq reads, with different reads quality characteristics than in older sequencers. We show that most of the algorithms are ready to cope with such reads. Finally, we introduce a new version of RECKONER, our read corrector, by optimizing it and equipping with a new correction strategy. Currently, RECKONER allows to correct high-coverage human reads in less than 2.5 h, is able to cope with two types of reads errors: indels and substitutions, and utilizes a new, based on a two lengths of oligomers, correction verification technique. [ABSTRACT FROM AUTHOR]

In this paper we present an open-source Time-of-Flight and radar dataset of a neonatal thorax simulator for the development of respiratory rate detection algorithms. As it is very difficult to gain recordings of (preterm) neonates and there is hardly any open-source data available, we built our own neonatal thorax simulator which simulates the movement of the thorax due to respiration. We recorded Time-of-Flight (ToF) and radar data at different respiratory rates in a range of 5 to 80 breaths per minute (BPM) and with varying upstroke heights. As gold standard a laser micrometer was used. The open-source data can be used to test new algorithms for non-contact respiratory rate detection. [ABSTRACT FROM AUTHOR]

Recently, video transmission is going through many failures because of the limited size of the top-notch technique for storing large volume videos. Thus, video compression (VC) techniques are introduced, which try to eradicate various sorts of redundancies within or betwixt video sequences. However, the VC often falls short to maintain a good quality of compression if motion discontinuities are present in the video frames (VF). To trounce this challenge, this paper proposes an enhanced VC approach via run length-based ASCII Huffman (RLAH) encoding, Kernel-based deep Elman neural network (KDENN), together with modified Kalman filters (MKF) algorithms. Initially, the video is transmuted into frames, and the frame's color space model (CSM) is changed as of RGB to YCbCr. Next, the frames are bifurcated into [8 × 8] blocks, and the significant features are extracted as of every block. On account of these features, the KDENN identifies the motion of every block. Those blocks directly undergo a compression process in case of a single motion. Otherwise, MFK smoothens those blocks in order to eradicate the jitters and undesired movements, and then, it goes through compression. After that, RLAH encoding compresses the VF. Then, on the other side, the RLAH decoding algorithm decomposes the video. The results exhibit that the proposed work renders good quality videos with high PSNR value and also it trounces the prevailing compression techniques concerning compression ratio (CR). [ABSTRACT FROM AUTHOR]

In this paper, we study the generalized multiple-set split feasibility problem including the common fixed-point problem for a finite family of generalized demimetric mappings and the monotone inclusion problem in 2-uniformly convex and uniformly smooth Banach spaces. We propose an inertial Halpern-type iterative algorithm for obtaining a solution of the problem and derive a strong convergence theorem for the algorithm. Then, we apply our convergence results to the convex minimization problem, the variational inequality problem, the multiple-set split feasibility problem and the split common null-point problem in Banach spaces. [ABSTRACT FROM AUTHOR]

In the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection methods neglect the alignment between actual data sample differences and clustering results, while neural networks lack automatic parameter adjustment in response to changing target features. This paper presents the MBSSA-Bi-AESN model, a Bi-directional Adaptive Echo State Network that utilizes the modified salp swarm algorithm (MBSSA) and feature selection to address the limitations of manually set parameters. Initial feature subset selection involves assigning weights based on the consistency of clustering results with differences. Subsequently, the four critical parameters in the Bi-AESN model are optimized using MBSSA. The optimized Bi-AESN model and selected feature subset are then integrated for simultaneous model learning and optimal feature subset selection. Experimental analysis on eight datasets demonstrates the superior prediction accuracy of the MBSSA-Bi-AESN model compared to benchmark models, underscoring its feasibility, validity, and universality. [ABSTRACT FROM AUTHOR]

As an effective extension of rough set theory, the variable precision neighborhood rough set model has been applied to the attribute dependency-based improvement of decision tree algorithm of the solution concerning continuous data. However, the boundary region, as an effective description of the uncertainty of knowledge, has not been taken into account in the existing algorithms. In this paper, we define a novel decision rule based on boundary region and attribute dependency, and construct a decision tree algorithm via this decision rule. First, we define a measure called boundary coefficient based on the boundary region, which can be used for comparative quantitative analysis. Second, we define the boundary mixed attribute dependency by combining the boundary coefficient and the attribute dependency, which can consider both the boundary case of the target set and the attribute dependency. Finally, a novel decision tree algorithm is proposed by using the boundary mixed attribute dependency as the decision rule. The experimental results show that with a slight increase in leaf nodes, the total running time decreases and the maximum accuracy increases to 0.9518, which indicates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper provides convergence analysis of some variants of the hybrid extragradient algorithm (HEA) in Hilbert spaces. We employ the HEA to compute the common solution of the equilibrium problem and split fixed-point problem associated with the finite families of k -demicontractive mappings. We also incorporate appropriate numerical results concerning the viability of the proposed variants with respect to various real-world applications. [ABSTRACT FROM AUTHOR]

MCMC algorithm is widely used in parameters' estimation of GARCH-type models. However, the existing algorithms are either not easy to implement or not fast to run. In this paper, Hamiltonian Monte Carlo (HMC) algorithm, which is easy to perform and also efficient to draw samples from posterior distributions, is firstly proposed to estimate for the Gaussian mixed GARCH-type models. And then, based on the estimation of HMC algorithm, the forecasting of volatility prediction is investigated. Through the simulation experiments, the HMC algorithm is more efficient and flexible than the Griddy-Gibbs sampler, and the credibility interval of forecasting for volatility prediction is also more accurate. A real application is given to support the usefulness of the proposed HMC algorithm well. [ABSTRACT FROM AUTHOR]

In order to quickly find an appropriate composition of services that meet the individual user's requirements in the Internet big data, this paper proposes an improved particle swarm service composition method based on prior knowledge. This method firstly mines the service composition partial segments with certain frequencies of usage from a large number of historical service composition solutions, i.e. the service pattern. While receiving the user's service composition requirement, this method uses the service pattern matching algorithm proposed in this paper to match the corresponding service patterns as a partial solution of this composition requirement. Then the method proposes an improved particle swarm algorithm for the part that do not successfully match the corresponding service patterns. This improved particle swarm algorithm has a mechanism to escape from the local optima. Finally, the method integrates the partial solutions of the two aspects into a complete solution, i.e. a complete service composition solution. This paper compares the optimality, time complexity and convergence with other related service composition optimization algorithms through simulation experiments. According to the analysis of the experimental results, the method proposed in this paper shows good performance in three aspects: optimality, time complexity and convergence. [ABSTRACT FROM AUTHOR]

The multi-objective integer programming problem often occurs in multi-criteria decision-making situations, where the decision variables are integers. In the present paper, we have discussed an algorithm for finding all efficient solutions of a multi-objective integer quadratic programming problem. The proposed algorithm is based on the aspect that efficient solutions of a multi-objective integer quadratic programming problem can be obtained by enumerating ranked solutions of an integer quadratic programming problem. For determining ranked solutions of an integer quadratic programming problem, we have constructed a related integer linear programming problem and from ranked solutions of this integer linear programming problem, ranked solutions of the original integer quadratic programming problem are generated. Theoretically, we have shown that the developed method generates the set of all efficient solutions in a finite number of steps, and numerically we have elaborated the working of our algorithm and compared our results with existing algorithms. Further, we have analyzed that the developed method is efficient for solving a multi-objective integer quadratic programming problem with a large number of constraints, variables and objectives. [ABSTRACT FROM AUTHOR]

The distributed architecture of cloud computing necessitates robust defense mechanisms to secure network-accessible resources against a diverse and dynamic threat landscape. A Network Intrusion Detection System (NIDS) is pivotal in this context, with its efficacy in cloud environments hinging on its adaptability to evolving threat vectors while mitigating false positives. In this paper, we present a novel NIDS algorithm, anchored in the Transformer model and finely tailored for cloud environments. Our algorithm melds the fundamental aspects of network intrusion detection with the sophisticated attention mechanism inherent to the Transformer model, facilitating a more insightful examination of the relationships between input features and diverse intrusion types, thereby bolstering detection accuracy. We provide a detailed design of our approach and have conducted a thorough comparative evaluation. Our experimental results demonstrate that the accuracy of our model is over 93%, which is comparable to that of the CNN-LSTM model, underscoring the effectiveness and viability of our Transformer-based intrusion detection algorithm in bolstering cloud security. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have been extensively utilized in feature selection tasks because they can obtain the global optimal solution. However, the meta-heuristic algorithm will take too much time in the face of a large number of samples. Although most of the studies compromise to approximate optimal solutions for avoiding time-consuming problems, a new problem with reduced classification performance, especially classification stability, is then generated. Aiming to above problems, this paper proposes a new feature selection framework. First, this framework exploits a voting ensemble strategy to improve classification stability by reducing the impact of misclassified labels on the overall classification results. Second, the framework uses a data perturbation strategy to enhance classification accuracy. In particular, the data perturbation strategy is able to generate more neighborhood relationships in the dataset, which could reveal the distribution of various features of the samples. A voting ensemble of different feature distributions is capable of extracting more information from the dataset, then the initially misclassified samples are more likely to be returned to the correct classification. Third, the framework takes a random sampling accelerator into account to solve the problem of excessive time consumption by reducing the size of the search sample space. Finally, for the sake of verifying the effectiveness of the proposed framework, four meta-heuristic feature selection methods based on a neighborhood rough set are compared on 20 datasets. The experimental results indicate that our framework could improve classification performance and accelerate feature selection, particularly in confronting large sample sizes. [ABSTRACT FROM AUTHOR]

The closed contiguous sequential pattern combines the advantages of closedness constraints and contiguity constraints and in recent years has been widely used in the fields of sequence classification, traffic trajectory visualization and football player trajectory analysis. Most of the previously developed closed contiguous sequential pattern mining algorithms pose some challenges. For instance, CCSpan, BP-CCSM, and LCCspm cannot mine the large-scale sequence database with reasonable time and memory usage, while C3Ro, which can mine patterns with multiple constraints, does not consider the specificity induced by the contiguity constraint of the pattern. To address these problems and improve the efficiency of mining closed contiguous sequential patterns, in this paper, we present an algorithm called CCSMP based on the pattern relation graph. Pattern relation graph is a novel data structure that has some key properties related to closed contiguous sequential pattern mining. In the experimental section, we not only conducted extensive experiments on real datasets to evaluate the performance and scalability of CCSMP but also analyzed the running time of each step of CCSMP to verify the effectiveness of the pattern relation graph. The experimental results show that CCSMP outperforms the existing state-of-the-art algorithm in most cases and that the use of the pattern relation graph can significantly reduce the time for closure checking. [ABSTRACT FROM AUTHOR]

This study investigates the joint replenishment and delivery problem taking trade credits the delay in payment permitted by suppliers and capacity-constrained into consideration (CJRD-TC). The CJRD-TC aims to minimize the total system costs by finding a reasonable replenishment and delivery schedule policy. In this paper, an opposite-based RUNge Kutta optimization algorithm (OBRUN) is proposed to compare with other intelligent algorithms, including the differential evolution (DE), RUNge Kutta optimization algorithm (RUN), sine cosine algorithm (SCA), and adaptive differential evolution with optimal external archive (JADE). The proposed algorithm is tested on 14 optimization functions. The values of average, standard deviation, and the best are the lowest for 12 functions, which demonstrates OBRUN is superior to other algorithms. We also provide graphs of search history, 2D views of functions, trajectory curve, average fitness history, and convergence curve of OBRUN, the balance between exploration and exploitation over the course of iterations can be observed. The convergence curves of five algorithms are also depicted, which show OBRUN has a fast convergence rate and accuracy than other algorithms. We conduct numerical experiments, parameter tuning, and sensitivity analysis for each of the small-scale, medium-scale, and large-scale number of items. The OBRUN can always find better solutions with the lowest mean values and the best-found total cost results than other algorithms. Indicators of the average optimality gap and the optimality gap dominate by the value of zero. These computational results have demonstrated OBRUN is a suitable and effective candidate for managers to solve the practical CJRD-TC problems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and innovative approach to simulating random variates from two distinct probability distributions, namely the neutrosophic uniform distribution and the neutrosophic Weibull distribution. The primary objective of this research is to present a cutting-edge methodology for generating random variates by leveraging the accept-reject simulation method, particularly in the context of managing and addressing uncertainty. In addition to introducing the simulation methodology, this work will also provide comprehensive algorithms tailored to these proposed methods. These algorithms are essential for implementing the simulation techniques and will be instrumental in their practical applications. Furthermore, this study aims to explore the relationship between the level of indeterminacy and the resulting random variates. By investigating how varying degrees of indeterminacy impact random variates, we gain valuable insights into the dynamics of these distributions under different uncertainty conditions. Preliminary results suggest that random variates exhibit a trend of decreasing as indeterminacy levels increase, shedding light on the intriguing interplay between indeterminacy and random variate generation. [ABSTRACT FROM AUTHOR]

In this paper, we prove strong convergence theorem of the general inertial Mann–Halpern algorithm for nonexpansive mappings in the setting of Hilbert spaces. We also prove weak convergence theorem of the general inertial Mann algorithm for k-strict pseudo-contractive mappings in the setting of Hilbert spaces. These convergence results extend and generalize some existing results in the literature. Finally, we provide examples to verify our main results. [ABSTRACT FROM AUTHOR]

The elliptic curve discrete logarithm problem (ECDLP) is a popular choice for cryptosystems due to its high level of security. However, with the advent of the extended Shor's algorithm, there is concern that ECDLP may soon be vulnerable. While the algorithm does offer hope in solving ECDLP, it is still uncertain whether it can pose a real threat in practice. From the perspective of the quantum circuits of the algorithm, this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor's algorithm. We give precise quantum circuits for extended Shor's algorithm to calculate discrete logarithms on elliptic curves over prime fields, including modular subtraction, three different modular multiplication, and modular inverse. Additionally, we incorporate and improve upon windowed arithmetic in the circuits to reduce the CNOT-counts. Whereas previous studies mostly focused on minimizing the number of qubits or the depth of the circuit, we focus on minimizing the number of CNOT gates in the circuit, which greatly affects the running time of the algorithm on an ion trap quantum computer. Specifically, we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts, along with improved constructions for modular inverse, point addition, and windowed arithmetic. Next, we precisely estimate that, to execute the extended Shor's algorithm with the improved circuits to factor an n-bit integer, the CNOT-count required is 1237 n 3 / log n + 2 n 2 + n . Finally, we analyze the running time and feasibility of the extended Shor's algorithm on an ion trap quantum computer. [ABSTRACT FROM AUTHOR]

The problem that I present in this paper concerns the issue of ethical evaluation of algorithms, especially those used in social media and which create profiles of users of these media and new technologies that have recently emerged and are intended to change the functioning of technologies used in data management. Systems such as Overton, SambaNova or Snorkel were created to help engineers create data management models, but they are based on different assumptions than the previous approach in machine learning and deep learning. There is a need to analyze both deep learning algorithms and new technologies in database management in terms of their actions towards a person who leaves their digital footprints, on which these technologies work. Then, the possibilities of applying the existing deep learning technology and new Big Data systems in the economy will be shown. The opportunities offered by the systems mentioned above seem to be promising for many companies and—if implemented on a larger scale—they will affect the functioning of the free market. [ABSTRACT FROM AUTHOR]

Zero frequency is a fundamental problem in information retrieval using language models and smoothing is applied to deal with this problem. The cluster-based smoothing method is found to be effective for information retrieval using language models. Since the effectiveness of cluster-based smoothing depends on clustering quality, there is scope for improvement by enhancing the clustering algorithm. In this paper, we present a study on how to improve cluster-based smoothing using a histogram-based incremental clustering algorithm and word embeddings. To our knowledge, this is the first study on the cluster-based smoothing method which is integrated with a language model for developing an effective IR system for the Bengali language which is one of the most spoken Indian languages. The proposed method has been tested on two benchmark Bengali IR datasets. The experimental results show that our proposed model for Bengali document retrieval is effective and it outperforms several baseline IR models. [ABSTRACT FROM AUTHOR]

Solar power is an excellent alternative to existing power sources; standalone PV systems demonstrate its importance. PV panels are the energy source for connected loads, with storage systems or batteries necessary due to solar insolation's intermittency. The present investigation uses a novel high-efficiency DC-DC converter to perform the maximum power point tracking (MPPT). This converter enables the connection of solar panels in series or parallel because it can step up or step down the PV voltage according to the DC link voltage. A bidirectional DC-DC converter is also used at the load side to maintain DC link voltage and charge/discharge the batteries. The second part of the paper discusses a modified Perturb and Observe (P &O) MPPT algorithm, which is vital in tapping the maximum power from PV panels. A fast solar MPPT is desired to track the operating point, which can be served by adding an acceleration factor to the existing P &O (hill climbing) solar MPPT algorithm. With the inclusion of the proposed converter and modification in P &O algorithm, the obtained system's efficiency is approximately 96% and tracking time reduced from 7 sec to 3.7 sec. The detailed analysis of component efficiency provides valuable insights into the performance of the system. A comprehensive simulation and hardware results obtained for various irradiation (Ropp, sine, step, ramp, less, no, etc.), temperature, loads, and acceleration factors are presented. [ABSTRACT FROM AUTHOR]

The deterministic directional overcurrent relay (DOCR) coordination approaches find limitation in providing fast and reliable protection to today's distribution networks due to growing integration of distributed generations (DGs). In this paper, an adaptive DOCR protection coordination scheme is proposed using an efficient optimization algorithm called the dragonfly algorithm (DA). The main inspiration of the proposed DA optimization technique originates from static and dynamic swarming behaviours of dragonflies. In this approach, the dominant changes in the network topologies are identified by monitoring the status of the circuit breakers connected at the terminals of the DGs and other main power components. When any dominant changes in the network topologies or operating modes are identified, the fault current for that particular condition is calculated and the new optimal DOCRs settings for the prevailing condition are obtained from the substation central computer in online mode. The comparative results with the existing approaches justify the superiority of the proposed scheme in achieving the minimum overall relay operating time and maintaining the coordination between the primary and backup relay pairs. In the proposed adaptive protection scheme, the average percentage reduction in the overall operating times of DOCRs in the 6-bus and the IEEE 14-bus test systems with different levels DG penetration is found to be 49.7% and 15%, respectively, compared to the existing approaches. [ABSTRACT FROM AUTHOR]

The deployment of long-term evolution systems in an unauthorized spectrum provided a credible solution to manage accelerating network data traffic. Meanwhile, the distribution of spectrum on unlicensed bands creates severe interaction issues. Therefore, this paper proposed a novel listen-and-talk-based MAC (LAT-MAC) protocol to enhance secondary users' (SUs') access performance by implementing a full-duplex mechanism. The application of full-duplex methods efficiently neglects collision and improves spectrum utilization of unlicensed bands. For cognitive radio networks, utilizing these benefits of a full duplex, the proposed LAT-MAC protocol simultaneously executes transmission processes and spectrum sensing. The cognitive network users sense spectrum utilizing energy detectors. For enhanced energy detection, the type 2 fuzzy cooperative spectrum sensing (T2FCSS) technique is implemented. Moreover, to attain enhanced spectrum sensing performances with the negligence of power-throughput tradeoff and self-interference, the switching algorithm-based cognitive radio network (SA-CRN) is employed. The performances of the proposed protocol are analyzed using different metrics, and evaluation results illustrate that it achieves enhanced performance compared with existing techniques. [ABSTRACT FROM AUTHOR]