Obstacle avoidance is a key technology of multiagents flocking control. However, most existing research studies assume that the agent can obtain global information about obstacles and have specific constraints on the shape and boundary of obstacles, which easily limit their practical applications. To relax these constraints, we assume that the agent can only perceive the position of obstacle boundary points within its sensing radius and propose an obstacle boundary point and an expected velocity-based flocking algorithm of multiagents with obstacle avoidance. In this algorithm, the attraction/repulsion potential is designed to avoid collisions between agents and between the agent and obstacle boundary points, the expected velocity is designed to tow the agent to move along the boundary of obstacles, and the virtual leader is designed to lead all agents to realize the group objective. Finally, the sufficient conditions that the agent does not collide are demonstrated, and the performance of the proposed algorithm is further verified through simulations.

Jianhui Wu

Yuanfa Ji

Xiyan Sun

Weibin Liang

Obstacle Boundary Point and Expected Velocity-Based Flocking of Multiagents with Obstacle Avoidance

Active learning (AL) is a well-known technique to optimize data usage in training, through the interactive selection of unlabeled observations, out of a large pool of unlabeled data, to be labeled by a supervisor. Its focus is to find the unlabeled observations that, once labeled, will maximize the informativeness of the training dataset, therefore reducing data-related costs. The literature describes several methods to improve the effectiveness of this process. Nonetheless, there is a paucity of research developed around the application of artificial data sources in AL, especially outside image classification or NLP. This paper proposes a new AL framework, which relies on the effective use of artificial data. It may be used with any classifier, generation mechanism, and data type and can be integrated with multiple other state-of-the-art AL contributions. This combination is expected to increase the ML classifier’s performance and reduce both the supervisor’s involvement and the amount of required labeled data at the expense of a marginal increase in computational time. The proposed method introduces a hyperparameter optimization component to improve the generation of artificial instances during the AL process as well as an uncertainty-based data generation mechanism. We compare the proposed method to the standard framework and an oversampling-based active learning method for more informed data generation in an AL context. The models’ performance was tested using four different classifiers, two AL-specific performance metrics, and three classification performance metrics over 15 different datasets. We demonstrated that the proposed framework, using data augmentation, significantly improved the performance of AL, both in terms of classification performance and data selection efficiency (all the codes and preprocessed data developed for this study are available at <a target="_blank" href="https://github.com/joaopfonseca/publications/">https://github.com/joaopfonseca/publications/</a>).

Joao Fonseca

Fernando Bacao

Improving Active Learning Performance through the Use of Data Augmentation

The portable executable header (PEH) information is commonly used as a feature for malware detection systems to train and validate machine learning (ML) or deep learning (DL) classifiers. We propose to extract the deep features from the PEH information through hidden layers of a feed-forward deep neural network (FFDNN). The extraction of deep features of hidden layers represents the dataset with a better generalization for malware detection. While feeding the deep feature of one hidden layer to the succeeding layer, the Gaussian error linear unit (GeLU) activation function is applied. The FFDNN is trained with the GeLU activation function using the deep features of individual layers as well as concatenated deep features of all hidden layers. Similarly, the ML classifiers are also trained and validated in with individual layer deep features and concatenated features. Three highly effective ML classifiers, random forest (RF), support vector machine (SVM), and k-nearest neighbour (k-NN) have been investigated. The performance of the proposed model is demonstrated using a statically significant large dataset. The obtained results are interesting and encouraging in terms of classification accuracy. The classification accuracy reaches 99.15% with the internal discriminative deep feature for the proposed FFDNN-ML classifier with the GeLU activation function.

Priyanka Singh

Samir Kumar Borgohain

Achintya Kumar Sarkar

Jayendra Kumar

Lakhan Dev Sharma

Feed-Forward Deep Neural Network (FFDNN)-Based Deep Features for Static Malware Detection

In order to improve the comprehensive performance of energy dispatching between different sites, the optimization research of particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm is carried out. We proposed a new improved PSO-ACO algorithm based on the idea of hybrid algorithm to solve the problem of poor energy dispatching efficiency between sites. First, the multiobjective performance indicators were introduced to transform the sites’ energy dispatching problem into a multiobjective optimization problem. Second, the vitality factor was introduced into the PSO strategy to solve the local optimal problem, and in the PSO-ACO fusion strategy, the PSO routes were transformed into the ant colony enhancement pheromone to accelerate the accumulation speed of the ACO initial pheromone. Then, the angle guidance function was introduced into the state transition probability of the ACO strategy to improve the global search capability, and a high-quality pheromone update rule was proposed to improve the convergence speed of the algorithm. Finally, simulation experiments were carried out on the improved PSO-ACO algorithm, Min–Max Ant System (MMAS) algorithm, ACO algorithm, PSO algorithm, and PSO update algorithm in a variety of complex site scenarios. The simulation results show that the improved PSO-ACO algorithm can plan a site energy dispatching route with shorter route, less time-consuming, and higher security and realize the comprehensive and global optimization of energy dispatching.

Qisong Song

Liya Yu

Shaobo Li

Naohiko Hanajima

Xingxing Zhang

Ruiqiang Pu

Energy Dispatching Based on an Improved PSO-ACO Algorithm

The capabilities of deep models are constantly mined for extraction and representation of features among text classification tasks. However, these models are sensitive to changes in input data, resulting in poor robustness. Meanwhile, the model lacks information interaction and weak representation ability. In this work, for feature extraction, a joint model that consists of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism is proposed. This new model can improve versatility and fully discover category information in text. For feature representation, a projector under the supervised contrastive learning method is introduced. The method can improve the representation of an encoder and realize aggregation of the same category. Considering the robustness of the PCRA, the gradient penalty is added to a contrastive loss function. Experiments are performed on four datasets to assess the proposed model (PCRA and PCRA-GP) using an accuracy metric. The experimental results show that our model is suitable for variable-length and bilingual texts. Compared with the baseline model, it remains competitive, and it reaches SOTA on the 20 Newsgroups dataset. Moreover, the performance of the model is evaluated under different hyperparameters to clarify its working mechanism.

Depei Wang

Lianglun Cheng

Zhuowei Wang

Augmenting Feature Representation with Gradient Penalty for Robust Text Categorization

Image matching can be transformed into the problem of feature point detection and matching of images. The current neural network methods have a weak detection effect on feature points and cannot extract enough sparse and uniform feature points. In order to improve the detection and description ability of feature points, this paper proposes a self-supervised feature point detection and description network based on asymmetric convolution: ACPoint. Specifically, first, feature point pseudolabels are learned from an unlabeled dataset, and pseudolabels are used for supervised learning; then, the learned model is used to update pseudolabels. Through multiple iterations of model training and label updating, high-quality labels and high-accuracy models are obtained adaptively. The asymmetric convolution feature point (ACPoint) network adopts an asymmetric convolution module to simultaneously train three convolution branches to learn more feature information, which uses two one-dimensional convolutions to enhance the backbone of square convolution from both horizontal and vertical directions and improve the representation of local features during inference. Based on the ACPoint network, a cross-resolution image-matching method is proposed. Experiments show that our proposed network model has higher localization accuracy and homography estimation ability on the HPatches dataset.

Ruixing Zhang

Tao Yao

Lianshan Yan

Feature Point Detection and Description Networks Based on Asymmetric Convolution and the Cross-ResolutionImage-Matching Method

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International Journal of Intelligent Systems&nbsp;serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction.

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Chief Editor, Professor Jin Li, is based at&nbsp;Guangzhou University, China.&nbsp;His research interests include trust and dependable artificial intelligence, cloud computing, and blockchain.

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International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction.

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International Journal of Intelligent Systems

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International Journal of Intelligent Systems