Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

AUTOMATION AND ROBOTICS (116 journals)                     

Showing 1 - 101 of 101 Journals sorted alphabetically
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 10)
ACM Transactions on Human-Robot Interaction     Open Access   (Followers: 4)
Advanced Robotics     Hybrid Journal   (Followers: 29)
Advances in Computed Tomography     Open Access   (Followers: 2)
Advances in Image and Video Processing     Open Access   (Followers: 27)
Advances in Robotics & Automation     Open Access   (Followers: 12)
Artificial Life and Robotics     Hybrid Journal   (Followers: 17)
Augmented Human Research     Hybrid Journal  
Automated Software Engineering     Hybrid Journal   (Followers: 9)
Automatic Control and Information Sciences     Open Access   (Followers: 4)
Automation and Remote Control     Hybrid Journal   (Followers: 6)
Autonomous Agents and Multi-Agent Systems     Hybrid Journal   (Followers: 9)
Autonomous Robots     Hybrid Journal   (Followers: 11)
Biocybernetics and Biological Engineering     Full-text available via subscription   (Followers: 4)
Biological Cybernetics     Hybrid Journal   (Followers: 10)
Biomimetic Intelligence and Robotics     Open Access  
Cognitive Robotics     Open Access   (Followers: 4)
Computational Intelligence and Neuroscience     Open Access   (Followers: 18)
Computer-Aided Design     Hybrid Journal   (Followers: 9)
Construction Robotics     Hybrid Journal   (Followers: 5)
Current Robotics Reports     Hybrid Journal   (Followers: 4)
Cybernetics & Human Knowing     Full-text available via subscription   (Followers: 3)
Cybernetics and Systems Analysis     Hybrid Journal  
Cybernetics and Systems: An International Journal     Hybrid Journal   (Followers: 1)
Design Automation for Embedded Systems     Hybrid Journal   (Followers: 4)
Digital Zone : Jurnal Teknologi Informasi Dan Komunikasi     Open Access  
Drone Systems and Applications     Open Access   (Followers: 1)
Electrical Engineering and Automation     Open Access   (Followers: 9)
Facta Universitatis, Series : Automatic Control and Robotics     Open Access   (Followers: 1)
Foundations and Trends® in Robotics     Full-text available via subscription   (Followers: 4)
GIScience & Remote Sensing     Open Access   (Followers: 58)
IAES International Journal of Robotics and Automation     Open Access   (Followers: 5)
IEEE Robotics & Automation Magazine     Full-text available via subscription   (Followers: 69)
IEEE Robotics and Automation Letters     Hybrid Journal   (Followers: 9)
IEEE Transactions on Affective Computing     Hybrid Journal   (Followers: 23)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 17)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 70)
IEEE Transactions on Cybernetics     Hybrid Journal   (Followers: 16)
IEEE Transactions on Intelligent Vehicles     Hybrid Journal   (Followers: 2)
IEEE Transactions on Medical Robotics and Bionics     Hybrid Journal   (Followers: 5)
IEEE Transactions on Neural Networks and Learning Systems     Hybrid Journal   (Followers: 56)
IEEE Transactions on Robotics     Hybrid Journal   (Followers: 71)
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews     Hybrid Journal   (Followers: 16)
IET Cyber-systems and Robotics     Open Access   (Followers: 2)
IET Systems Biology     Open Access   (Followers: 1)
Industrial Robot An International Journal     Hybrid Journal   (Followers: 2)
Intelligent Control and Automation     Open Access   (Followers: 6)
Intelligent Service Robotics     Hybrid Journal   (Followers: 2)
International Journal of Adaptive, Resilient and Autonomic Systems     Full-text available via subscription   (Followers: 3)
International Journal of Advanced Pervasive and Ubiquitous Computing     Full-text available via subscription   (Followers: 4)
International Journal of Advanced Robotic Systems     Full-text available via subscription   (Followers: 1)
International Journal of Agent Technologies and Systems     Full-text available via subscription   (Followers: 4)
International Journal of Ambient Computing and Intelligence     Full-text available via subscription   (Followers: 3)
International Journal of Applied Evolutionary Computation     Full-text available via subscription   (Followers: 3)
International Journal of Artificial Life Research     Full-text available via subscription  
International Journal of Automation and Control     Hybrid Journal   (Followers: 11)
International Journal of Automation and Control Engineering     Open Access   (Followers: 5)
International Journal of Automation and Logistics     Hybrid Journal   (Followers: 4)
International Journal of Automation and Smart Technology     Open Access   (Followers: 3)
International Journal of Bioinformatics Research and Applications     Hybrid Journal   (Followers: 14)
International Journal of Biomechatronics and Biomedical Robotics     Hybrid Journal   (Followers: 2)
International Journal of Humanoid Robotics     Hybrid Journal   (Followers: 6)
International Journal of Imaging & Robotics     Full-text available via subscription   (Followers: 3)
International Journal of Intelligent Information Technologies     Full-text available via subscription   (Followers: 1)
International Journal of Intelligent Machines and Robotics     Hybrid Journal   (Followers: 3)
International Journal of Intelligent Mechatronics and Robotics     Full-text available via subscription   (Followers: 5)
International Journal of Intelligent Robotics and Applications     Hybrid Journal  
International Journal of Intelligent Systems Design and Computing     Hybrid Journal   (Followers: 2)
International Journal of Intelligent Unmanned Systems     Hybrid Journal   (Followers: 3)
International Journal of Machine Consciousness     Hybrid Journal   (Followers: 7)
International Journal of Machine Learning and Cybernetics     Hybrid Journal   (Followers: 31)
International Journal of Mechanisms and Robotic Systems     Hybrid Journal   (Followers: 2)
International Journal of Mechatronics and Automation     Hybrid Journal   (Followers: 5)
International Journal of Robotics and Automation     Full-text available via subscription   (Followers: 8)
International Journal of Robotics and Control     Open Access   (Followers: 3)
International Journal of Robotics Applications and Technologies     Full-text available via subscription   (Followers: 1)
International Journal of Robotics Research     Hybrid Journal   (Followers: 15)
International Journal of Space-Based and Situated Computing     Hybrid Journal   (Followers: 2)
International Journal of Synthetic Emotions     Full-text available via subscription  
International Journal of Tomography & Simulation     Full-text available via subscription   (Followers: 1)
Journal of Automation and Control     Open Access   (Followers: 9)
Journal of Computer Assisted Tomography     Hybrid Journal   (Followers: 2)
Journal of Control & Instrumentation     Full-text available via subscription   (Followers: 19)
Journal of Control, Automation and Electrical Systems     Hybrid Journal   (Followers: 11)
Journal of Intelligent and Robotic Systems     Hybrid Journal   (Followers: 6)
Journal of Intelligent Learning Systems and Applications     Open Access   (Followers: 4)
Journal of Robotic Surgery     Hybrid Journal   (Followers: 3)
Jurnal Otomasi Kontrol dan Instrumentasi     Open Access  
Machine Translation     Hybrid Journal   (Followers: 12)
Proceedings of the ACM on Human-Computer Interaction     Hybrid Journal   (Followers: 2)
Results in Control and Optimization     Open Access   (Followers: 5)
Revista Iberoamericana de Automática e Informática Industrial RIAI     Open Access  
ROBOMECH Journal     Open Access   (Followers: 1)
Robotic Surgery : Research and Reviews     Open Access   (Followers: 1)
Robotica     Hybrid Journal   (Followers: 5)
Robotics and Autonomous Systems     Hybrid Journal   (Followers: 19)
Robotics and Biomimetics     Open Access   (Followers: 1)
Robotics and Computer-Integrated Manufacturing     Hybrid Journal   (Followers: 7)
Science Robotics     Full-text available via subscription   (Followers: 11)
Unmanned Systems     Hybrid Journal   (Followers: 4)
Wearable Technologies     Open Access   (Followers: 4)

           

Similar Journals
Journal Cover
International Journal of Machine Learning and Cybernetics
Journal Prestige (SJR): 0.7
Citation Impact (citeScore): 2
Number of Followers: 31  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1868-8071 - ISSN (Online) 1868-808X
Published by Springer-Verlag Homepage  [2468 journals]
  • A general framework for improving cuckoo search algorithms with resource
           allocation and re-initialization

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      Abstract: Abstract Cuckoo search (CS) has currently become one of the most favorable meta-heuristic algorithms (MHAs). In this article, a simple yet effective framework is proposed for CS algorithms to reinforce their performance, which contains two core mechanisms: computational resource allocation (CRA) and Gaussian sampling based re-initialization (GSR). The CRA is responsible for allocating more computational resources to promising individuals, thus promoting search efficiency and speeding up convergence, whilst the GSR is introduced to help the algorithm in maintaining population diversity. For testifying the effectiveness and generality of this framework (referred to as AR framework), it is embedded into nine well-established CS algorithms and extensive experiments are conducted on CEC 2013, CEC 2014, and CEC 2017 test suites. Experimental results indicate that the AR framework could bring a significant improvement on the performance of the classical CS as well as its variants, achieving an average efficient rate of 78.97%, 72.59%, and 86.21% on the three test suites, respectively. Besides, the comparisons between the classical CS, its AR framework version, and several other classical MHAs validate the effectiveness of the AR framework again. Additionally, the benefit of each mechanism (i.e., CRA and GSR) and their combination is also ascertained.
      PubDate: 2024-08-01
       
  • Tensor discriminant analysis on grassmann manifold with application to
           video based human action recognition

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      Abstract: Abstract Representing videos as linear subspaces on Grassmann manifolds has made great strides in action recognition problems. Recent studies have explored the convenience of discriminant analysis by making use of Grassmann kernels. However, traditional methods rely on the matrix representation of videos based on the temporal dimension and suffer from not considering the two spatial dimensions. To overcome this problem, we keep the natural form of videos by representing video inputs as multidimensional arrays known as tensors and propose a tensor discriminant analysis approach on Grassmannian manifolds. Because matrix algebra does not handle tensor data, we introduce a new Grassmann projection kernel based on the tensor-tensor decomposition and product. Experiments with human action databases show that the proposed method performs well compared with the state-of-the-art algorithms.
      PubDate: 2024-08-01
       
  • ConDA: state-based data augmentation for context-dependent text-to-SQL

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      Abstract: Abstract The context-dependent text-to-SQL task has profound real-world implications, as it facilitates users in extracting knowledge from vast databases, which allows users to acquire the information interactively for better accuracy. Unfortunately, current models struggle to address this task effectively due to the scarcity of data led by the high annotation overhead. The most straightforward method for addressing this problem is data augmentation, which aims at scaling up the parsing corpus. However, the naive methods suffer from the low diversity of the augmented data. To address this limitation, we propose the state-based CONtext-dependent text-to-SQL Data Augmentation (ConDA), which generate and filter augmented data based on the dialogue state, which has higher diversity. Experimental results show that ConDA yields performance improvement on all experimental datasets with an average boosting of \(1.6\%\) , proving the effectiveness of our method.
      PubDate: 2024-08-01
       
  • Fast Shrinking parents-children learning for Markov blanket-based feature
           selection

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      Abstract: Abstract High-dimensional data leads to degraded performance of machine learning algorithms and weak generalization of models, so feature selection is of great importance. In a Bayesian network (BN), the Markov blanket (MB) of a target node (T) is the best feature subset of that node. Therefore, this paper proposes Fast Shrinking parents-children learning for Markov blanket-based feature selection (FSMB), which first determines the parents-children of the target node, and then discovers spouses while checking candidate parents-children set. In spouse determination process, a secondary screening strategy is proposed to remove false-positive spouses effectively. In this process, once the spouses of T with respect to T’s child are determined, the parents-children set is immediately tested and the false-positive parents-children are removed in time, which not only can avoid the influence of false-positive parents-children on the subsequent spouse discovery, but also do not need to determine the spouses for false-positive parents-children. To verify the effectiveness of FSMB, experiments were performed on eight state-of-the-art MB algorithms on six standard networks and eight real datasets, the results show that FSMB outperforms other algorithms in terms of accuracy and efficiency.
      PubDate: 2024-08-01
       
  • Combining core points and cluster-level semantic similarity for
           self-supervised clustering

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      Abstract: Abstract Contrastive learning utilizes data augmentation to guide network training. This approach has attracted considerable attention for clustering, object detection, and image segmentation. However, previous studies have ignored the impact of false-negative pairs, resulting in the dissimilarity of the semantic representations of the same cluster. Some researchers have attempted to address this problem; however, only considering the image level has provided unsatisfactory results. To this end, we propose a novel feature extraction algorithm suitable for clustering, combining core points and semantic similarity at the cluster level to restructure positive and negative pairs. Specifically, the core points consisting of the n-nearest neighbors of the cluster center are considered the semantic sample relations of the cluster. This information is explored to reconstruct semantic positive and negative pairs to maximize intra-cluster similarity and inter-cluster variability. More accurate cluster centers offer a sub-optimal initialization for updating the feature model and clustering assignment, which is optimized by the expectation-maximization framework. Extensive experiments conducted on six benchmark datasets show promising clustering performances with relatively few training epochs. The proposed method outperforms the best baseline by 4 \(\%\) (1.5 \(\%\) ) on CIFAR-100 (CIFAR-10). The CPCS code is open-sourced at https://github.com/Cappuccino-Sugar/CPCS.
      PubDate: 2024-08-01
       
  • Drfnet: dual stream recurrent feature sharing network for video dehazing

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      Abstract: Abstract The primary effects of haze on captured images/frames are visibility degradation and color disturbance. Even though extensive research has been done on the tasks of video dehazing, they fail to perform better on varicolored hazy videos. The varicolored haze is still a challenging problem in video de-hazing. To tackle the problem of varicolored haze, the contextual information alone is not sufficient. In addition to adequate contextual information, color balancing is required to restore varicolored hazy images/videos. Therefore, this paper proposes a novel lightweight dual stream recurrent feature sharing network (with only 1.77 M parameters) for video de-hazing. The proposed framework involves: (1) A color balancing module to balance the color of input hazy frame in YCbCr space, (2) A multi-receptive multi-resolution module (MMM), which interlinks the RGB and YCbCr based features to learn global and rich contextual data, (3) Further, we have proposed a feature aggregation residual module (FARM) to strengthen the representative capability during reconstruction, (4) A channel attention module is proposed to resist redundant features by recalibrating weights of input features. Experimental results and ablation study show that the proposed model is superior to existing state-of-the-art approaches for video de-hazing.
      PubDate: 2024-08-01
       
  • Aspect category sentiment classification via document-level GAN and POS
           information

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      Abstract: Abstract The purpose of aspect-category sentiment classification (ACSC) is to determine the sentiment polarity of the predefined aspect category from the texts. Current methods for ACSC have two main limitations. Since the aspect categories are not presented in the given texts, the establishment of relation between the aspect-category and its sentiment opinion expression is challenging using the widely-applied aspect-term sentiment classification approaches. Besides, the aspect-category-related information on document level are ignored during processing. In this work, we focus on dealing with the part-of-speech information based on gated-activation functions. Furthermore, two graph attention networks (GANs) are employed to exploit the document-level sentiment of both the entity and the attribute (intra-entity sentiment tendency and intra-attribute sentiment tendency). The aspect-category detection (ACD) is taken as a auxiliary task to capture the relevant semantic information. Besides, contrastive learning is receiving an increasing amount of interest due to its success in self-supervised representation learning in the field of NLP. By performing contrastive learning, representations of positive examples are drawn closer while those of negative samples are distanced. Comparing with the baseline methods, experimental results reveal that our model achieves the state-of-the-art performance in ACSC tasks.
      PubDate: 2024-08-01
       
  • Data-driven quantification and intelligent decision-making in traditional
           Chinese medicine: a review

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      Abstract: Abstract Traditional Chinese medicine (TCM) originates from the practical experience of human beings’ constant struggle with nature. In five thousand years, TCM has gradually risen from empirical medicine to modern evidence-based medicine with complete scientific principles such as fundamental systematic theories, treatment principles and methods, classic prescriptions, famous medicines. The development of information science, data science, and computer technology has provided effective models, methods, and technologies for modern TCM’s quantitative and intelligent diagnosis and treatment decision-making. And it also has promoted the development of TCM from evidence-based medicine to intelligent TCM. Starting from the development of TCM, we introduce the rise and connotation of ancient, modern, and intelligent TCM. Moreover, we emphatically analyze the research status of quantification and intelligent decisions for the whole disease cycle, including data-driven modern TCM diagnosis, program optimization, and treatment program evaluation. In addition, we discuss the critical issues of data-driven TCM quantification and intelligent decision research and briefly elaborate on the new ideas of data-driven intelligent TCM research. In conclusion, compared with traditional research paradigms, the advantages of data-driven medical decision research paradigms are as follows: (1) From the perspective of decision-making subjects, the data-driven research paradigm describes the clinical decision-making mechanism in real scenarios with rigorous mathematical theories, which will break through the difference between the conclusions drawn by clinical design research methods and clinical practice. (2) By applying the results of basic theoretical research to clinical decision-making practice in real scenarios, the data-driven medical decision-making research paradigm will contribute to getting out of the dilemma that the conclusions drawn by traditional AI models are difficult to explain in clinical practical decision-making.
      PubDate: 2024-08-01
       
  • BPSO-SLM: a binary particle swarm optimization-based self-labeled method
           for semi-supervised classification

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      Abstract: Abstract The self-labeled methods have been favored by scholars in semi-supervised classification. Mislabeling is a great challenge for self-labeled methods and one of the reasons for mislabeling is that high-confidence unlabeled samples are found by mistake. While multiple variations of self-labeled methods have been developed, most existing strategies for finding high-confidence unlabeled samples heavily rely on specific assumptions. To solve the above issue, a binary particle swarm optimization-based self-labeled method (BPSO-SLM) is proposed and includes the following iterative self-labeled process: (a) A given classifier is trained on the set of labeled data; (b) The binary particle swarm optimization-based sample subspace optimization (BPSOSSO) is innovatively proposed to help BPSO-SLM find high-confidence unlabeled samples and low-confidence unlabeled samples from the set of unlabeled data; (c) The trained classifier is used to predict found high-confidence unlabeled samples; (d) High-confidence samples with pseudo labels are added to the set of labeled data, while low-confidence samples are returned to the set of unlabeled data and will be predicted again in the next iteration. The above process repeats until no high-confidence samples are found. After that, BPSO-SLM outputs the trained classifier during the iterative self-labeled process. The main characteristic of BPSO-SLM is that the strategy of finding high-confidence unlabeled samples makes little or no specific hypothesis about the geometric, distribution, and others of the selected high-confidence unlabeled samples. Intensive experiments on benchmark data prove that BPSO-SLM outperforms 8 state-of-the-art self-labeled methods in classification accuracy, Marco F-measure, and labeling error rate with various ratios of labeled data.
      PubDate: 2024-08-01
       
  • Dual flow fusion graph convolutional network for traffic flow prediction

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      Abstract: Abstract In recent decades, motor vehicle ownership has increased worldwide year by year, which causes that the accurate prediction of traffic flow on urban road networks becomes more important. However, the dual dependence on the micro layer and the macro layer creates a huge challenge for the prediction task. Previous models lack comprehensive analysis of the macro features at different time granularities. In this paper, we propose a novel Dual Flow Fusion Graph Convolutional Network (DFFGCN) to solve this problem. For capturing more macro features, we build the interactions between the micro layer and the macro layer at more time granularities. Then the spatial-temporal normalization model is introduced to separate the temporal and spatial influences. Therefore, the proposed DFFGCN has a better learning ability compared with other advanced models. Finally, we give experiments to show the effectiveness and superiority of our proposed model. Experimental results on three traffic datasets demonstrate that DFFGCN can achieve state-of-the-art performance consistently. And the ablation studies confirm the importance of each element of DFFGCN.
      PubDate: 2024-08-01
       
  • Survey and open problems in privacy-preserving knowledge graph: merging,
           query, representation, completion, and applications

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      Abstract: Abstract Knowledge Graph (KG) has attracted more and more companies’ attention for its ability to connect different types of data in meaningful ways and support rich data services. However, due to privacy concerns, different companies cannot share their own KGs with each other. Such data isolation problem limits the performance of KG and prevents its further development. Therefore, how to let multiple parties conduct KG-related tasks collaboratively on the basis of privacy protection becomes an important research question to answer. In this paper, to fill this gap, we summarize the open problems for privacy-preserving KG in the data isolation setting and propose possible solutions for them. Specifically, we summarize the open problems in privacy-preserving KG from four aspects, i.e., merging, query, representation, and completion. We present these problems in detail and propose possible technical solutions for them, along with the datasets, evaluation methods, and future research directions. We also provide three privacy-preserving KG application scenarios.
      PubDate: 2024-08-01
       
  • Unsupervised domain adaptation via feature transfer learning based on
           elastic embedding

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      Abstract: Abstract Supervised classification algorithms usually require a large quantity of well-labeled samples for training to achieve satisfied performance. Nevertheless, it is prohibitively difficult to create such datasets with complete annotation. Unsupervised domain adaptation is able to deal with this problem via transferring knowledge from a relevant dataset with rich labels to an unlabeled target dataset. In this paper, a novel unsupervised domain adaptation method named feature transfer learning based on elastic embedding (EEFTL) is presented for image classification. Rather than make a rigid embedding that the binary label matrix is exactly equal to a linear function, EEFTL adopts an elastic embedding induced by the prediction label matrix. Specifically, EEFTL introduces a flexible regression residue term to model the mismatch between the embedded features of samples and the prediction labels. In addition, EEFTL integrates a label fitness term to effectively utilize the label information from the source samples, a distribution matching term to reduce the distances between domains in both the marginal and conditional distributions, and a manifold regularization term to preserve the sample-wise structure information under the elastic embedding. Extensive experiments are carried out on multiple benchmark datasets, and the results prove the effectiveness of the proposed method.
      PubDate: 2024-08-01
       
  • Dual stage black-box adversarial attack against vision transformer

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      Abstract: Abstract Relying on wide receptive fields, Vision Transformers (ViTs) are more robust than Convolutional Neural Networks (CNNs). Consequently, some transfer-based attack methods that perform well on CNNs perform poorly when attacking ViTs. To address the aforementioned issues, we propose dual-stage attack framework named DSA. More specifically, we introduce a dual spatial optimization strategy involving both decision space and feature space optimization to improve the transferability of adversarial examples across different ViTs. Adversarial perturbations are generated by our proposed semi self-integrated module in the first stage and optimized by the feature extractor in the second stage. During this process, our proposed integrated model makes full use of the discriminative information in the deep transformer blocks and achieves significant improvements in transferability. To further enhance the transferability, we design the random perturbation masking module to alleviate the over-fitting of adversarial examples to the surrogate model. We evaluate the transferability of attacks on state-of-the-art ViTs, CNNs, and robustly trained CNNs. Extensive experiments demonstrate that the proposed dual-stage attack can greatly boost transferability between ViTs and from ViTs to CNNs.
      PubDate: 2024-08-01
       
  • A hospitalization mechanism based immune plasma algorithm for path
           planning of unmanned aerial vehicles

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      Abstract: Abstract Unmanned aerial vehicles (UAVs) and their specialized variants known as unmanned combat aerial vehicles (UCAVs) have triggered a profound change in the well-known military concepts and researchers from different disciplines tried to solve challenging problems of the mentioned vehicles. Path planning is one of these challenging problems about the UAV or UCAV systems and should be solved carefully by considering some optimization requirements defined for the enemy threats, fuel or battery usage, kinematic limitations on the turning and climbing angles in order to further improving the task success and safety of autonomous flight. Immune plasma algorithm (IP algorithm or IPA) modeling the details of a medical method gained popularity with the COVID-19 pandemic has been introduced recently and showed promising performance on solving a set of engineering problems. However, IPA requires setting the control parameters appropriately for maintaining a balance between the exploration and exploitation characteristics and does not design the particular treatment and hospitalization procedures by taking into account the implementation simplicity. In this study, IP algorithm was supported with a newly designed and realistic hospitalization mechanism that manages when an infected population member enters and discharges from the hospital. Moreover, the existing treatment schema of the algorithm was changed completely for improving the efficiency of the plasma transfer operations and removing the necessity of IPA specific control parameters and then a novel path planner called hospital IPA (hospIPA) was presented. For investigating the performance of hospIPA on solving path planning problem, a set of detailed experiments was carried out over twenty test cases belonging to both two and three-dimensional battlefield environments. The paths calculated by hospIPA were also compared with the calculated paths of other fourteen meta-heuristic based path planners. Comparative studies proved that the hospitalization mechanism making an exact discrimination between the poor and qualified solutions and modified treatment schema collecting the plasma being transferred by guiding the best solution give a tremendous contribution and allow hospIPA to obtain more safe and robust paths than other meta-heuristics for almost all test cases.
      PubDate: 2024-08-01
       
  • Efficient evolutionary neural architecture search based on hybrid search
           space

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      Abstract: Abstract Manually designed convolutional neural networks have demonstrated excellent performance in various domains, but designing neural networks suitable for specific tasks poses significant challenges, and the emergence of Neural Structure Search (NAS) provides a new solution to this problem. However, existing algorithms either focus solely on network lightweight, resulting in subpar network performance, or excessively emphasize performance, leading to substantial network redundancy. With consideration for both network parameters and performance, this paper designs a hybrid search space based on residual modules and RepVGG modules using genetic algorithm, and stacks them together to form a more efficient network. To achieve this, we propose an efficient variable-length encoding strategy, utilizing units as the fundamental encoding space to encode variable-length individuals; we design evolutionary operations encompassing single-point crossover and three types of mutation operators to ensure population diversity; during training, a random forest-based performance predictor is employed to significantly shorten the network search time. To demonstrate the effectiveness of the proposed algorithm, we introduce the concept of transfer learning, which involves decoding the globally optimal solution, fine-tuning it, and then transferring it to three categories of real-world application datasets. Through comparisons with various algorithms, our approach consistently achieved leading performance.
      PubDate: 2024-08-01
       
  • Long-short interest network with graph-based method for sequential
           recommendation

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      Abstract: Abstract In recommender systems, sequence information is crucial. Sequence data contains user preferences and reflects the evolution of user interests over time. Therefore, how to utilize sequence information to capture dynamic user interests is a critical issue in sequential recommender systems (SRSs). Attention-based methods are commonly used in SRSs and achieve state-of-the-art results. However, attention mechanisms lack the ability to represent the temporal dimension and cannot use sequence order effectively. To this end, this paper proposes a novel model structure called Long-Short Interest Network (LSIN), which fuses Long Short Term Memory (LSTM) and Transformer encoder. We use two LSTM layers to capture the user’s long-term and short-term interests, respectively. Furthermore, adding the LSTM can help the self-attention mechanism better model the sequential relationship between items. In addition, most embedding models generate embedding vectors based on individual items without considering the connection between items and users. This will be an obstacle and bring difficulty for later models to capture the evolution of user interests. Therefore, we use a heterogeneous graph to model the interactions between users and items. And design a weight-based graph embedding for generating embedding vectors, which can encode higher-order structural information by propagating on the graph. Finally, we propose LSRec, a framework that unites the above two structures to achieve more accurate recommendations. The new model yielded significant benefits. The experiments on four benchmark data sets demonstrate the effectiveness of LSRec, which achieves almost 5 \(\%\) improvement in NDCG@10 compared with Self-Attention based Sequential Recommendation model (SASRec).
      PubDate: 2024-08-01
       
  • A fast DBSCAN algorithm using a bi-directional HNSW index structure for
           big data

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      Abstract: Abstract The Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is one of the most popular and effective density-based clustering algorithms at present. Although it can effectively identify clusters and noise points of arbitrary shapes, it is very difficult to efficiently address the tasks with large scale data. The time complexity of the DBSCAN is \(O(n^2)\) where its main computation time lies in \(\varepsilon\) -neighbor range query, which becomes the bottleneck of DBSCAN performance. To solve this problem, we propose a simple fast DBSCAN algorithm, called bh-DBSCAN, using a bi-directional HNSW index structure to improve the efficiency of DBSCAN by reducing redundant \(\varepsilon\) -neighbor range queries. Specifically, we first distinguish a point’s property (core point or border point). Next, we apply the filtNoise algorithm to filter the noise points that without core points in \(neighbor_x\) . Finally, we utilized the MergeCore algorithm to merge the cluster of border points in it’s core neighbor points. The experimental results show that our proposed algorithm could greatly improve the clustering efficiency without losing much accuracy based on the datasets tested.
      PubDate: 2024-08-01
       
  • Advancing ASD detection: novel approach integrating attention graph neural
           networks and crossover boosted meerkat optimization

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      Abstract: Abstract Autism spectrum disorder (ASD) is a neurodevelopmental condition that significantly impacts the lives of many children due to its hidden symptoms. Early detection of ASD is challenging because of its complex and heterogeneous nature. Magnetic resonance imaging (MRI) has emerged as a crucial tool for early detection, offering non-invasive imaging with detailed soft tissue information. However, existing approaches face limitations such as overfitting, underfitting, class imbalance, control, domain shift, and behavioral issues. To address these challenges, this paper proposes a novel ASD detection and classification model called the Autism Spectrum Disorder-based Attention Graph Neural Network and Crossover Boosted Meerkat Optimization (ASD-AttGCBMO) algorithm. The proposed method utilizes structural Magnetic Resonance Imaging (sMRI) data from the ABIDE 1 dataset. The data undergoes preprocessing to remove artifacts and noise, ensuring high image quality and consistency. Node feature extraction employs voxel-based morphometry (VBM) and surface-based analysis, which extract relevant features such as surface area, cortical thickness, shape descriptors, and brain volumes. The ASD-AttGCBMO model is trained using preprocessed sMRI images, employing the Adam and Stochastic Gradient Descent (SGD) optimizers to prevent overfitting, reduce classification loss, and improve convergence. The model is designed to enhance the learning process and capture complex patterns for accurate feature classification between ASD and control subjects. To optimize the hyperparameters in the attention-based neural network model, the CBMO algorithm is employed. Experimental validation is conducted using essential performance evaluation measures. The proposed method achieves impressive results, with accuracy, precision, recall, specificity, F1-score, Area under Receiver Operating curve (AUC/ROC), and computational time values of 98.8%, 99%, 98.5%, 98.6%, 98.2%, 0.989, and 3.05 s, respectively. Comparative analysis demonstrates that the proposed method outperforms other state-of-the-art methods.
      PubDate: 2024-08-01
       
  • DBHC: Discrete Bayesian HMM Clustering

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      Abstract: Abstract Sequence data mining has become an increasingly popular research topic as the availability of data has grown rapidly over the past decades. Sequence clustering is a type of method within this field that is in high demand in the industry, but the sequence clustering problem is non-trivial and, as opposed to static cluster analysis, interpreting clusters of sequences is often difficult. Using Hidden Markov Models (HMMs), we propose the Discrete Bayesian HMM Clustering (DBHC) algorithm, an approach to clustering discrete sequences by extending a proven method for continuous sequences. The proposed algorithm is completely self-contained as it incorporates both the search for the number of clusters and the search for the number of hidden states in each cluster model in the parameter inference. We provide a working example and a simulation study to explain and showcase the capabilities of the DBHC algorithm. A case study illustrates how the hidden states in a mixture of HMMs can aid the interpretation task of a sequence cluster analysis. We conclude that the algorithm works well as it provides well-interpretable clusters for the considered application.
      PubDate: 2024-08-01
       
  • An evolutionary feature selection method based on probability-based
           initialized particle swarm optimization

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      Abstract: Abstract Feature selection is a common data preprocessing technique that aims to construct better models by selecting the most predictive features. Existing particle swarm optimization-based feature selection algorithms encounter two challenges when dealing with high-dimensional problems: easy to fall into local optimum and high computational cost. Therefore, this paper proposes an evolutionary dual-task feature selection method based on probability-based initialization particle swarm optimization (PPSO-EDT), which aims to find optimal solutions by transferring knowledge between two related tasks. Firstly, a probability-based initialization strategy is designed to accelerate population convergence by fully utilizing the correlation between labels and features. Secondly, a task generation strategy based on feature correlation was designed, which constructs the main task and auxiliary task by selecting feature subsets with highly correlated values and feature subsets without redundancy, respectively. Finally, an multi-task transfer mechanism is used to transfer knowledge and find optimal solutions. The results on 12 high-dimensional datasets indicate that the proposed method achieves high classification performance with a small feature subset in a relatively short amount of time.
      PubDate: 2024-08-01
       
 
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  Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

AUTOMATION AND ROBOTICS (116 journals)                     

Showing 1 - 101 of 101 Journals sorted alphabetically
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 10)
ACM Transactions on Human-Robot Interaction     Open Access   (Followers: 4)
Advanced Robotics     Hybrid Journal   (Followers: 29)
Advances in Computed Tomography     Open Access   (Followers: 2)
Advances in Image and Video Processing     Open Access   (Followers: 27)
Advances in Robotics & Automation     Open Access   (Followers: 12)
Artificial Life and Robotics     Hybrid Journal   (Followers: 17)
Augmented Human Research     Hybrid Journal  
Automated Software Engineering     Hybrid Journal   (Followers: 9)
Automatic Control and Information Sciences     Open Access   (Followers: 4)
Automation and Remote Control     Hybrid Journal   (Followers: 6)
Autonomous Agents and Multi-Agent Systems     Hybrid Journal   (Followers: 9)
Autonomous Robots     Hybrid Journal   (Followers: 11)
Biocybernetics and Biological Engineering     Full-text available via subscription   (Followers: 4)
Biological Cybernetics     Hybrid Journal   (Followers: 10)
Biomimetic Intelligence and Robotics     Open Access  
Cognitive Robotics     Open Access   (Followers: 4)
Computational Intelligence and Neuroscience     Open Access   (Followers: 18)
Computer-Aided Design     Hybrid Journal   (Followers: 9)
Construction Robotics     Hybrid Journal   (Followers: 5)
Current Robotics Reports     Hybrid Journal   (Followers: 4)
Cybernetics & Human Knowing     Full-text available via subscription   (Followers: 3)
Cybernetics and Systems Analysis     Hybrid Journal  
Cybernetics and Systems: An International Journal     Hybrid Journal   (Followers: 1)
Design Automation for Embedded Systems     Hybrid Journal   (Followers: 4)
Digital Zone : Jurnal Teknologi Informasi Dan Komunikasi     Open Access  
Drone Systems and Applications     Open Access   (Followers: 1)
Electrical Engineering and Automation     Open Access   (Followers: 9)
Facta Universitatis, Series : Automatic Control and Robotics     Open Access   (Followers: 1)
Foundations and Trends® in Robotics     Full-text available via subscription   (Followers: 4)
GIScience & Remote Sensing     Open Access   (Followers: 58)
IAES International Journal of Robotics and Automation     Open Access   (Followers: 5)
IEEE Robotics & Automation Magazine     Full-text available via subscription   (Followers: 69)
IEEE Robotics and Automation Letters     Hybrid Journal   (Followers: 9)
IEEE Transactions on Affective Computing     Hybrid Journal   (Followers: 23)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 17)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 70)
IEEE Transactions on Cybernetics     Hybrid Journal   (Followers: 16)
IEEE Transactions on Intelligent Vehicles     Hybrid Journal   (Followers: 2)
IEEE Transactions on Medical Robotics and Bionics     Hybrid Journal   (Followers: 5)
IEEE Transactions on Neural Networks and Learning Systems     Hybrid Journal   (Followers: 56)
IEEE Transactions on Robotics     Hybrid Journal   (Followers: 71)
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews     Hybrid Journal   (Followers: 16)
IET Cyber-systems and Robotics     Open Access   (Followers: 2)
IET Systems Biology     Open Access   (Followers: 1)
Industrial Robot An International Journal     Hybrid Journal   (Followers: 2)
Intelligent Control and Automation     Open Access   (Followers: 6)
Intelligent Service Robotics     Hybrid Journal   (Followers: 2)
International Journal of Adaptive, Resilient and Autonomic Systems     Full-text available via subscription   (Followers: 3)
International Journal of Advanced Pervasive and Ubiquitous Computing     Full-text available via subscription   (Followers: 4)
International Journal of Advanced Robotic Systems     Full-text available via subscription   (Followers: 1)
International Journal of Agent Technologies and Systems     Full-text available via subscription   (Followers: 4)
International Journal of Ambient Computing and Intelligence     Full-text available via subscription   (Followers: 3)
International Journal of Applied Evolutionary Computation     Full-text available via subscription   (Followers: 3)
International Journal of Artificial Life Research     Full-text available via subscription  
International Journal of Automation and Control     Hybrid Journal   (Followers: 11)
International Journal of Automation and Control Engineering     Open Access   (Followers: 5)
International Journal of Automation and Logistics     Hybrid Journal   (Followers: 4)
International Journal of Automation and Smart Technology     Open Access   (Followers: 3)
International Journal of Bioinformatics Research and Applications     Hybrid Journal   (Followers: 14)
International Journal of Biomechatronics and Biomedical Robotics     Hybrid Journal   (Followers: 2)
International Journal of Humanoid Robotics     Hybrid Journal   (Followers: 6)
International Journal of Imaging & Robotics     Full-text available via subscription   (Followers: 3)
International Journal of Intelligent Information Technologies     Full-text available via subscription   (Followers: 1)
International Journal of Intelligent Machines and Robotics     Hybrid Journal   (Followers: 3)
International Journal of Intelligent Mechatronics and Robotics     Full-text available via subscription   (Followers: 5)
International Journal of Intelligent Robotics and Applications     Hybrid Journal  
International Journal of Intelligent Systems Design and Computing     Hybrid Journal   (Followers: 2)
International Journal of Intelligent Unmanned Systems     Hybrid Journal   (Followers: 3)
International Journal of Machine Consciousness     Hybrid Journal   (Followers: 7)
International Journal of Machine Learning and Cybernetics     Hybrid Journal   (Followers: 31)
International Journal of Mechanisms and Robotic Systems     Hybrid Journal   (Followers: 2)
International Journal of Mechatronics and Automation     Hybrid Journal   (Followers: 5)
International Journal of Robotics and Automation     Full-text available via subscription   (Followers: 8)
International Journal of Robotics and Control     Open Access   (Followers: 3)
International Journal of Robotics Applications and Technologies     Full-text available via subscription   (Followers: 1)
International Journal of Robotics Research     Hybrid Journal   (Followers: 15)
International Journal of Space-Based and Situated Computing     Hybrid Journal   (Followers: 2)
International Journal of Synthetic Emotions     Full-text available via subscription  
International Journal of Tomography & Simulation     Full-text available via subscription   (Followers: 1)
Journal of Automation and Control     Open Access   (Followers: 9)
Journal of Computer Assisted Tomography     Hybrid Journal   (Followers: 2)
Journal of Control & Instrumentation     Full-text available via subscription   (Followers: 19)
Journal of Control, Automation and Electrical Systems     Hybrid Journal   (Followers: 11)
Journal of Intelligent and Robotic Systems     Hybrid Journal   (Followers: 6)
Journal of Intelligent Learning Systems and Applications     Open Access   (Followers: 4)
Journal of Robotic Surgery     Hybrid Journal   (Followers: 3)
Jurnal Otomasi Kontrol dan Instrumentasi     Open Access  
Machine Translation     Hybrid Journal   (Followers: 12)
Proceedings of the ACM on Human-Computer Interaction     Hybrid Journal   (Followers: 2)
Results in Control and Optimization     Open Access   (Followers: 5)
Revista Iberoamericana de Automática e Informática Industrial RIAI     Open Access  
ROBOMECH Journal     Open Access   (Followers: 1)
Robotic Surgery : Research and Reviews     Open Access   (Followers: 1)
Robotica     Hybrid Journal   (Followers: 5)
Robotics and Autonomous Systems     Hybrid Journal   (Followers: 19)
Robotics and Biomimetics     Open Access   (Followers: 1)
Robotics and Computer-Integrated Manufacturing     Hybrid Journal   (Followers: 7)
Science Robotics     Full-text available via subscription   (Followers: 11)
Unmanned Systems     Hybrid Journal   (Followers: 4)
Wearable Technologies     Open Access   (Followers: 4)

           

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JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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