Reviewerly/exHarmony huggingface dataset:
A multi-strategy particle swarm optimization framework based on deep reinforcement learning

The particle swarm optimization (PSO) algorithm is a well-known optimization algorithm that has shown good performance in solving engineering problems. However, the performance and convergence speed of the PSO algorithm is easily affected by the parameter settings. In this paper, we propose an adaptive parameter optimization framework (APOF) for the PSO algorithm by using the Deep Deterministic Policy Gradient (DDPG) of deep reinforcement learning. In order to achieve better optimization effect, the strategy group is extracted from the APOF, so that the APOF can be combined with more strategies to improve the searchability of the optimized algorithm. This paper also improves the PSO algorithm and proposes the hybrid cluster PSO algorithm (HCPSO) as the built-in algorithm of the APOF. In the experiment, twenty-one functions are selected to implemented, and the optimization effect of the APOF algorithm is tested. The experimental results show that the APOF has a good optimization effect and scalability, and the built-in HCPSO algorithm also achieves good performance.
Vyakranly : Hindi Grammar &amp; Spelling Errors Detection and Correction System

The growing demand for automation tools for Hindi over the past few years has led NLP experts to start working towards tasks that facilitate research and development for Hindi Language Processing. Researchers have been increasingly putting efforts into building models to perform essential NLP tasks like spell correction, grammar correction, summarizing, and so on. Compared to the plethora of tools and data available for English, Hindi is a relatively new area in which not much work has been done so far. Therefore, researchers need to build frameworks and technologies that support the Hindi language to perform such complex tasks effectively.However, with limited data and tools available for Hindi, performing Grammatical Error Correction (GEC) for Hindi may come across as a challenge. Therefore, we propose Vyakranly (व्याक्रणली), a Hindi Translation and Grammatical Error Detection Toolkit for the Indic language Hindi. Objectives of Vyakranly (व्याक्रणली) are Hindi Text Spelling Error Detection and correction, Hindi Sentence Grammar Error Detection and correction, English to Hindi and Hindi to English text translation. Highlights of our work are Hindi spelling detection as well as correction and grammar error detection. It is difficult to find such past work for the Hindi language due to their relative lack of digitized content and complex morphology, compared to English.
An Approach For Generating High Quality Structured Data

Structured data is modeled to represent data distributions and obtain realistic data. Data augmentation is of great significance to data privacy protection, machine learning (ML) applications, etc. However, modeling structured data distributions is challenging due to the presence of both numerical and categorical columns. Additionally, structured data often suffers from class imbalance issues. This paper presents a method for generating structured data using generative adversarial networks (GANs). Discrete variables are transformed into continuous variables using an embedding model, while the variational Bayesian Gaussian mixture model (VBGMM) is employed to model the distribution of numerical variables. To address the issue of class imbalance, a multi-category generator is designed. The proposed method is evaluated using various metrics and is compared with other data generation techniques and traditional oversampling methods. The results demonstrate the effectiveness of the proposed method for structured data generation.
A Novel Fragile Zero-watermarking Algorithm Based on Quaternion and LBP under the Background of Blockchain

After the emergence of blockchain technology, although the problem of copyright protection on the chain has been solved, the problem of cross-chain prosecution and confirmation protection has not been solved.Zero watermarking is a technique that achieves copyright protection without any modification to the carrier image. This paper proposes a fragile zero-watermarking algorithm based on quaternion and LBP.In this way, the joint blockchain and fragile zero-watermark technology can solve the problem of cross-chain prosecution and confirmation protection.
Research on high precision Chinese text sentiment Classification based on ALBERT Optimization

Text sentiment classification is an important research field in natural language processing and has great research value for social public opinion and business development. The existing emotion classification models have some problems, such as low classification accuracy, huge parameter amount, and difficult model training, which cannot provide high precision models for users with low computing power. ALBERT reduces the number of BERT parameters of the same magnitude by more than ten times, making it possible for the average user to run the model. In this paper, we dynamically fuse the middle layer of ALBERT model with two channels to obtain different granularity of text semantic information and improve the accuracy of the model. We added TextCNN to enhance the model's ability to capture local emotion words, added Attention mechanism to enrich the feature extraction process of the model, and constructed a new model ALBERT-TextCNN-Attention (PATA). We not only compensate for the slight accuracy loss of lightweight ALBERT model compared to BERT model, but also develop a new model with low parameter number and high accuracy for the study of text sentiment classification. The experimental results show that the parameter number of the proposed model is only 19.72% of the BERT model, and the accuracy of the proposed model is 90.63% on the public data set waimai-10k, which is better than other models based on ALBERT in recent studies.
iNAV-drlSLAM: An Improved Indoor Self-Driving Framework for Mobile Robots using Deep Reinforcement Learning integrated with SLAM

We introduce a new navigation framework for mobile robots that combines Simultaneous Localization and Mapping (SLAM) and deep reinforcement learning (DRL) techniques. While SLAM algorithms are effective at mapping and navigating through the environment, they struggle to avoid dynamic obstacles and can be computationally intensive. In contrast, DRL algorithms can manage dynamic situations but may result in sub-optimal performance when used alone. Our framework addresses this issue by using SLAM to generate a static path plan using the Ant Colony Optimization (ACO) algorithm. In dynamic environments, the robot follows the global path plan generated by SLAM and moves between way-points using a DRL-based local path planning algorithm. We evaluate the performance of three DRL-based navigation algorithms - Deep Q-Learning Network (DQN), Twin Delayed Deep Deterministic policy gradient (TD3), and Proximal Policy Optimization (PPO) - and compare them with existing navigation methods for mobile robots. Our approach overcomes the traditional reliance on static global and local maps, making it well-suited for dynamic environments. The proposed framework provides a promising alternative to current navigation methods for mobile robots, improving accuracy and efficiency while maintaining scalability and safety.
A General Paradigm of Knowledge-driven and Data-driven Fusion

Knowledge and data fusion is a vital research hotspot in current artificial intelligence. The fusion of data-driven and knowledge-driven would be able to organically combine implicit intuition of machines and common-sense judgment of people. Yet, fusion models have mainly been applied to specific problems. Because of the complex mechanisms underlying knowledge formation, the creation of a novel fusion model requires tedious and time-consuming experiments. Here, we approach the problem of general data-driven and knowledge-driven fusion paradigm. We first propose a general framework of the fusion mode. Next, we introduce and show the specific types of the three basic models under this paradigm. We hope that this paradigm will play a role in the development of machine learning.
DenseNeXt: An Efficient Backbone for Image Classification

Recently, deep convolution neural networks have been widely used in image classification tasks. In this work, to model the channel correlations in the dense convolutional connections explicitly, an architecture named as DenseNext which integrates modified PSA modules and DenseNet is proposed. By replacing the 3×3 convolution in the DenseNet block with a PSA module, the Dense PSA (D-PSA) block is obtained. To improve the computing efficiency, the modified Dense Asymmetric PSA (DA-PSA) block can be obtained by splitting the big convolution kernel in the D-PSA block into asymmetric convolutions. DenseNeXt can be effectively built by selectively stacking the two convolution modules. Without bells and whistles, the accuracy of DenseNeXt50 on Cifar-10 is 0.52% higher than DenseNet50 and converges faster. DenseNeXts obtain significant improvements and can easily be competent for other vision tasks by fine-tuning.
A Constrained Sampling Assisted Differential Evolution for Expensive Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have become a widely used method to solve computationally expensive optimization problems, in which the evaluation of objective has high time cost. However, in many problems, the evaluation of constraints also has high time cost. These problems are known as expensive constrained optimization problems (ECOPs). To solve ECOPs, this paper proposes a constrained sampling assisted differential evolution (CSADE) algorithm. CSADE consists a global search process and a local search process. Global search process aims to explore the whole search space. During this process, constraints are transformed as a penalty to fitness and a radial basis function (RBF) model is built for the penalty fitness. DE/rand/1 algorithm is adopted for candidate generation to improve the population diversity. Local search process aims to exploit the promising areas. In this process, the objective and each constraint are approximated by an RBF model respectively. DE/best/1 algorithm is utilized for population convergence in promising areas. Extensive experiments with several SAEAs for ECOPs on benchmark test suites demonstrate the competitive performance of CSADE. Besides, comparison experiments on the optimization of bulk carrier design show the promising application in engineering optimization of CSADE.
Effectiveness of Deep Image Embedding Clustering Methods on Tabular Data

Deep learning methods are commonly benchmarked on image data sets, which may not be suitable or effective baselines for non-image tabular data. In this paper, we take a data-centric view to perform one of the first studies on deep embedding clustering of tabular data. Eight clustering and state-of-the-art embedding clustering methods proposed for image data sets are tested on seven tabular data sets. Our results reveal that traditional clustering ranks second out of eight methods on tabular data and is superior to most deep embedding clustering baselines. Our observation aligns with the literature that traditional machine learning of tabular data is still more effective than deep learning. Therefore, state-of-the-art embedding clustering methods should consider data-centric custom learning architectures and algorithms for tabular data.
GA=PSO: Realizing Equivalent Particle Swarm Optimization by Genetic Algorithm

Evolutionary computation algorithms (EC) are a kind of powerful stochastic optimization approaches inspired by biological phenomena, such as natural selection, genetic information transmission, and intelligent swarm behaviors. After years of development, EC has many branches. Among them, the most classical one is the genetic algorithm (GA), which is also the earliest EC method. Though a lot of novel EC methods have been developed in the literature, they actually are all on the foundation of GA. However, little attention is put on the research on the connection between GA and the other EC algorithms. To fill this gap, this paper takes an in-depth study to investigate the relation between GA and the particle swarm optimization algorithm (PSO). By analyzing the operators in GA and PSO, this paper designs a new GA by devising novel crossover and mutation operators to simulate the evolution of particles in PSO. In this way, this paper gives the first transformation of GA to PSO and demonstrates that PSO is actually a special version of GA. Experiments have conducted on the CEC2017 single-objective benchmark set. Experimental results show that the designed new GA achieves very similar performance with the classical PSO.
Frequent Trajectory Mining with Local Differential Privacy

With the rapid development of mobile Internet, trajectory data has become an indispensable data type in the era of big data due to its potential information. Since trajectory data of real number type cannot be directly applied to existing encoding mechanisms of local differential privacy and existing frequent item mining algorithms, we propose an approach called Private Frequent Trajectory Mining (PrivFTM). The implementation process of the algorithm is divided into three phases. Specifically, in the first phase, an adaptive spatial decomposition based on local differential privacy (PrivASD) is designed to discretize user trajectory data, while in the subsequent phase, the data collector completes the mining of candidate set and frequent items respectively according to the grouped user data. Extensive experiments performed on the two real-world datasets indicate that the performance of our PrivFTM is better than the existing algorithms.
Latent multi-view subspace clustering based on Laplacian regularized representation

We propose a latent multi-view subspace clustering model based on Laplacian regularized representation. To emphasize the information of the representation matrix at the local level and reflect the grouping effect of clustering, a Laplacian regularization is imposed on the representation matrix. Additionally, To boost the efficiency o f c lustering, we apply nonnegative constraints on the representation matrix. A unified framework is designed to solve the proposed model by using ALM and LADMAP methods. The proposed model has excellent performance, as demonstrated by a number of experimental findings.
A Robust Two-Part Modeling Strategy for Knowledge Graph Enhanced Recommender Systems

Knowledge graph (KG) enhanced recommender systems (RSs), i.e., KGRSs, have received extensive attention recently. However, the modeling strategy in existing KGRSs tends to excessively depend on the feature data of KG component or the historical data of RS component, which affects the performance of KGRSs in two aspects. First, as the KG data is usually incomplete and contains noise, excessive dependency on KG data limits the expressive ability and damages model robustness. Second, excessive dependency on the historical data in RS also limits the expressive ability because historical data is not enough to model users’ complex and changeable behavioral preferences. In order to solve these issues, this paper proposes a robust yet effective twopart modeling (TPM) strategy to reduce the strong dependency on KG feature data and RS historical data while make full use of multi-source data from both the KG and the RS components. That is, both the users and items in KGRSs are modeled by two parts information, i.e., a context part and an auxiliary part. The TPM is a general strategy that can be simply implemented into existing KGRSs to further improve models’ expressive ability and robustness. This paper applies the proposed TPM strategy to a typical KGRS named multitask feature learning for KG enhanced recommendation (MKR), to obtain TMKR. Experimental results show that TMKR can not only obtain a great prediction accuracy and recommendation stability, but also has a significant time cost reduction compared to MKR.
Chaotic Mapping Genetic Algorithm with Multiple Strategies

The genetic algorithm is a very successful metaheuristic algorithm. Its wide applications in recommendation algorithms, feature selection, and industrial design have proven its effectiveness. However, it still suffers from the issues of low performance and local optima. Therefore, we propose a method called Chaotic Mapping Genetic Algorithm with Multiple Strategies (CGA-M) inspired by the existence of chaos in nature’s evolution and shorten the evolutionary process by the linear decline method. We test its effectiveness on the classical test set from IEEE congress on evolutionary computation (IEEE CEC). The experimental results demonstrated its superiority.
An improved TLEA-CMA-ES for Solving Bilevel Optimization Problems

In recent years, bilevel optimization has aroused great interest among researchers. However, due to the lower-level optimization problems’ own high level of complexity, the classical optimization methods of gradient-based algorithms can not solve the bilevel optimization effectively. In another way, despite the fact that the evolutionary algorithm (EA) is a reliable and efficient way to solve bilevel optimization problems. Unfortunately, due to the nested structure, It is necessary to optimize many lower-level optimization processes in parallel, which incurs a high computational cost, while bilevel optimization problems can naturally be employed multi-task learning or transfer learning to accelerate algorithm convergence. However, the positive transfer can accelerate algorithm convergence, while the negative transfer can cause the algorithm’s performance to deteriorate. Hence, we propose two strategies to enhance the positive transfer, including introducing an indicator used to select the most similar tasks to execute knowledge transfer and an adaptive tuning method for adjusting the probability of cross-task knowledge transfer to determine when to transfer. Aiming to confirm the efficacy of our suggested strategy, this paper combines the proposed strategies with a transfer learning-based framework to form a new algorithm called ITLEA-CMA-ES. Compared with four well-established algorithms on two sets of extensively used benchmark problems, The effectiveness and reliability of ITLEA-CMA-ES have been confirmed b y t he experimental results.
Unsupervised Multi-Hashing for Image Retrieval in Non-stationary Environments

Hashing methods help retrieve swiftly in large-scale dataset, which is important for real-world image retrieval. New data is produced continually in the real world which may cause concept drift and inaccurate retrieval results. To address this issue, hashing methods in non-stationary environments are proposed. However, most hashing methods in non-stationary data environments are supervised. In practice, it is hard to get exact labels of data especially in non-stationary data environments. Therefore, we propose the unsupervised multi-hashing (UMH) method for unsupervised image retrieval in non-stationary environments. Thus, in the UMH, a set of hash functions is trained and added to the kept list of hash functions sets when a new data chunk occurs. Then, multiple sets of hash functions are kept with different weights to guarantee that similarity information in old and new data are both adapted. Experiments on two real-world image datasets show that the UMH yields better retrieval performance in non-stationary environments than other comparative methods.
Improvement Whale Optimization Algorithm Based on Mixed Strategy

As the original Whale Optimization Algorithm (WOA) has the drawback of falling into local extremes, it also fails to meet the expectations in terms of convergence effect. An improved whale optimization algorithm (HWOA) based on a hybrid strategy was proposed. First, the proposed algorithm is initialized based on the Zaslavskii chaotic map to obtain a population with better ergodicity; second, The elite search base strategy is used to improve the global optimization ability of the algorithm and increase the probability of jumping out of the local extreme value; Finally, by introducing an adaptive variable speed strategy, the search ability and development capabilities of the whale optimization algorithm are effectively coordinated while retaining the advantages of the algorithm. The improved whale optimization algorithm is tested against other algorithms on 10 benchmark functions. The final results demonstrate the effectiveness of the HWOA algorithm improvement strategy and outperforms other improvement algorithms in terms of effectiveness.
Triangular Regularized Diffusion Process for Visual Retrieval

As using pairwise distance to solve object retrieval problems is inefficient, many algorithms have been developed as a post-processing step to optimize the original retrieval result. Recent studies have demonstrated that regularized diffusion process can improve retrieval performance by solving an optimization problem of a general regularization framework. Typically, the general regularization framework consists of a smoothness constraint and a fitting constraint. In this paper, we propose a novel form of smoothness constraint, which reveals a triangular intrinsic manifold structure. With high-order information brought into the optimization process and an efficient way to solve the optimization problem, the retrieval result can be effectively improved by our proposed algorithm.
Design of electrical certificate examination system based on virtual simulation technology

The traditional maintenance electrician examination and certification usually includes four items, which are theoretical project examination, troubleshooting project examination, welding plate project examination and PLC project examination. The PLC assessment projects are often limited by the site and equipment, which leads to the problems of heavy workload and low efficiency in the assessment process of this project. Therefore, in order to improve this situation and improve the efficiency of assessment, this paper takes PLC project assessment as an example, and puts forward a virtual simulation technology based assessment system design scheme. The program only needs PLC related software to complete the project assessment content (including PLC program design, simulation debugging, operation monitoring, etc.). It gets rid of the limitation of site and equipment and improves the assessment efficiency, which is a beneficial attempt to reform the traditional assessment method.
Code Clone Detection Using Boosting Algorithms

To increase programming productivity, developers often copy and paste the source code with or without changing it. However, they may also introduce significant downsides in the long run, including complicating the software and raising maintenance costs. The activity of duplicating the code is known as code cloning. They are classified into four types - Type-1, Type-2, Type-3, and Type-4. In this paper, the author presents a machine-learning approach for detecting code clones of all kinds except for Type-2. Abstract Syntax Trees are used to extract features from the methods. A distance combination approach combines two feature vectors of a pair of methods and their class labels. Once the dataset is finalised, a machine- learning approach is utilised to classify the clone type. Moreover, boosting classifiers like XGBoost, CatBoost, LightGBM, Gradient Boosting and AdaBoost are evaluated for the highest classification accuracy. From the results obtained, LightGBM outperformed all the other classifiers with the highest F1 score of 0.81. This study would motivate future researchers to focus on identifying the Type-2 clones and extracting novel features in determining the clone types.