Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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HGCAN: Heterogeneous Graph Completion Method Based on Attribute Neighborhood
Full text linkRecent research shows that the effect of heterogeneous graph embedding learning is vulnerable to non-attribute nodes. However, the existing methods mainly use first-order neighbor nodes to complete attributes, which cannot achieve a satisfactory completion effect on the heterogeneous graphs with random non-attribute nodes. Therefore, this paper put forward an attribute completion method of heterogeneous graphs based on attribute neighborhoods, which is called HGCAN. HGCAN employs two major stages of completion. Specifically, in the first stage, we use meta-paths to construct attribute neighborhoods of non-attribute nodes. The attribute neighborhoods aggregation can capture the semantic relations of attributed nodes to initially complete attributes. Then, the second stage uses structural information to obtain the distance relationships between nodes to further improve the preliminary completed attributes. Finally, HGCAN is combined with an existing heterogeneous graph embedding learning model to verify the validity of the completed attributes and make the system end-to-end. Extensive experiments carried out on the ACM dataset show the proposed mechanism's superior performance over state-of-the-art attribute-completion methods
Prediction of Complex Event Graphs with Neural Networks
Full text linkA key problem domain inside Robotic Process Automation is the automatic discovery of workflow process schemes. Considering current process mining technologies, graph-based approaches dominate the industry. On the other hand, the conventional methods suffer from low time efficiency and varying accuracy. Machine learning-based methods can provide better efficiency, but they have significant limitations considering schema flexibility. The paper presents a novel neural network-based schema induction model for the discovery of event patterns containing parallel and optional sequences of different actors. This model can process more complex event graphs and situations than the conventional methods. The performed analysis and test results show the unique power of this approach in process schema mining
Increasing Text Filtering Accuracy with Improved LSTM
Full text linkHow to eliminate useless information in the vast network information and retain effective information is a problem that needs to be continuously explored in the field of deep learning. This paper conducts text classification on the network evaluation frequently encountered in daily life, mainly to screen out the meaningless comments published by Internet users, to have access to more useful information. In this paper, a text filtering model was constructed based on word vector and Long Short-Term Memory (LSTM) and improved by adding Deep Averaging Networks (DAN) and convolutional neural network (CNN). The major improvement of the LSTM & DAN model was to retain the original word vector information and to improve the accuracy of the text classification model without increasing hyperparameter and model structure complexity. The LSTM & CNN model mainly combines the advantages of convolutional neural network in exploring the deep information of text, which was an improvement over the original LSTM. It was proved by experiments that this improvement is meaningful. Compared with the shallow neural network, the accuracy has been greatly improved
Visual Communication Design and Color Balance Algorithm for Multimedia Image Analysis
Full text linkAs culture continues to evolve, the field of visual communication design faces new challenges in the era of new media. To address these challenges, a paper proposed innovative ideas for the industry's growth and development. Specifically, the paper suggested that incorporating visual communication design and color balance into multimedia image analysis can enhance the visual impact of images. Results showed that images analyzed under this approach received a visual effect score 10 % higher than those analyzed without it, validating the effectiveness of this proposal. The visual effect score of Image 2 (the visual design proposed in this article) was 10 % higher than that of Image 1 (general visual design). Based on the comparison of the spatial comparison Transfer Function of Modulation Transfer Function, the increase is about 12.5 % under this method. Overall, this paper offered new perspectives on improving visual communication design for the era of new media
Intelligent Route Planning Method for UAV Based on Swarm Intelligence and Deep Learning Technology
Full text linkDue to its potential applications in numerous industries, Unmanned Aerial Vehicles (UAVs) have gained considerable attention recently. UAV networks that are autonomous and decentralized have various practical uses, such as in disaster recovery, environmental monitoring, and security surveillance. Due to frequent route distractions and traffic congestion at high node speeds, the performance of routing systems in these networks drops considerably. UAVs with a mission to gather sensory data from various sources require meticulous route planning to decrease traffic congestion effectively. Due to flight time, range, and coverage area limitations, efficient route planning is crucial for maximizing the efficiency of UAV data collection. Optimal route planning and a delicate balancing act between these critical parameters are two of the biggest obstacles in sensory data gathering. This study presents a new method for dealing with these issues by developing an Intelligent Route Planning for Sensory Data Collection (IRP-SDC) system to optimize autonomous UAV route planning with congestion-aware modelling by considering time, distance, and area coverage limits. The IRP-SDC framework uses Multi-Objective Grey Wolf Optimization and Deep Q-Learning (MOGWO-DQL) for smart UAV route planning. The MOGWO algorithm, developed after observing the hunting techniques of grey wolves, can perform a worldwide search, which helps determine the most efficient paths to take after gathering information. DQL, on the other hand, has adaptive learning capabilities that can modify the UAV's flight path in response to alterations in its external environment. The suggested framework combines the two techniques to maximize the usefulness of UAVs in gathering sensory data. Extensive trials were carried out to prove the efficacy of the proposed technique. The IRP-SDC system beats previous approaches concerning time, distance, and area coverage by providing an ideal route for a UAV to acquire sensory data
Two-Stage Trading Mechanism in Enabling the Design and Optimization of Flexible Resources Interaction in Smart Grid
Full text linkCurrently, renewable energy sources (RES) have been widely deployed in the smart grid, especially in the active distribution network (ADN). However, the inherent uncertainty of the renewable energy output significantly impacts the economy of the ADN operation. It is suggested that the utilization of flexible resources (FR) can effectively even out the uncertainty of RES. Nevertheless, the non-marketization of FR may prevent the emerging park-level integrated energy systems (PIES), important entities in ADN, from sufficiently offering their potential flexibilities. Therefore, this paper presents a two-stage local flexibility trading mechanism for motivating multi-PIESs to provide their flexible resources (PFR) to improve the operational flexibility of ADN. The first stage determines the dispatching plan of ADN according to the day-ahead forecasted values of RES. The second stage enables the multi-PIESs to trade their PFR with ADN to adjust the day-ahead dispatching plan in real-time to correct the forecasted errors of RES. In terms of implementing the two stages, firstly, the capacity of PFR with involving the adjustable tie line power of PIES is quantified using an optimization-based assessment model. Secondly, based on the change of the operation cost before and after the sale of PFR, a pricing model of PFR is established. And then, to determine the trading amount of PFR, a real-time ADN economic dispatching model with the network constraints is further constructed, which aims at minimizing the comprehensive operation cost. Afterwards, a marginal-based method is employed to obtain the clearing price of PFR. Finally, to ensure the feasibility of the trading results and to provide the accurate dispatching strategies for the PFR trading in next time interval, a rolling dispatch for the multi-PIESs is carried out. Case studies demonstrate that the presented flexibility trading mechanism significantly reduces the power curtailment of ADN, the operation costs of ADN and the multi-PIESs
Leveraging Genetic Algorithms for Efficient Search-Based Higher Order Mutation Testing
Full text linkHigher order mutation testing is a type of white-box testing in which the source code is changed repeatedly using two or more mutation operators to generate mutated programs. The objective of this procedure is to improve the design and execution phases of testing by allowing testers to automatically evaluate their test cases. However, generating higher order mutants is challenging due to the large number of mutants needed and the complexity of the mutation search space. To address this challenge, the problem is modeled as a search problem. The purpose of this study is to propose a genetic algorithm-based search technique for mutation testing. The expected outcome is a reduction in the number of equivalent high order mutants produced, leading to a minimum number of mutant sets that produce an adequate mutation score. The experiments were carried out and the results were compared with a random search algorithm and four different versions of the proposed genetic algorithm which use different selection methods: roulette wheel, tournament, rank, and truncation selection. The results indicate that the number of equivalent mutants and the execution cost can be reduced using the proposed genetic algorithm with respect to the selection method
Meta-Learning for EEG Motor Imagery Classification
Full text linkA brain-computer interface (BCI) based on motor imagery (MI) enables users to communicate with the computer directly using brain signals. However, due to the low signal-to-noise ratio and significant inter-subject variations, its long calibration time hinders the development of brain-computer interfaces. Meta-learning, as the current popular framework in few-shot learning, enables the model to quickly adapt to new few-shot tasks by learning a series of similar tasks. In this paper, we propose a novel algorithm that combines pre-training and meta-learning. It can reduce the number of training samples required for the target subject and meanwhile ensure stable and reliable BCI performance. This is achieved by first learning general feature representations from a large number of other subjects' data through pre-training, and then further optimizing the model by task scale using meta-learning based on the pre-training stage. Applications to a dataset confirm the effectiveness of the combination of pre-training and meta-learning. Results indicate that the proposed algorithm outperforms the considered comparable baseline algorithm, early meta-learning frameworks without pre-training, and fine-tuning in transfer learning. In addition, experiments show that the selection of source subjects can further improve the overall performance of the algorithm, and the pre-training stage is crucial for the model to achieve good performance. This study has significant instruction for the application of BCI in the field of medical rehabilitation. It can improve the classification performance of the disabled with poor motor imagination characteristics by using the data of healthy typical subjects and greatly reducing their training time
Lower Limb Motor Intention Detection Model Based on Feature Fusion and Reinforcement Learning Assisted Approach
Full text linkBrain-computer interface (BCI) technology holds immense promise in the rehabilitation of patients with movement disorders, leveraging the body's physiological mechanisms to enhance their quality of life by reshaping motor neural circuits through external devices. Nevertheless, current BCI applications for rehabilitation predominantly rely on a single physiological signal, often overlooking the synergistic impact of multiple signals. Simultaneously, while reinforcement learning shows significant potential for BCI applications, there exists a scarcity of studies exploring this intersection.This paper introduces a novel motor intention judgment model grounded in multimodal signal fusion and reinforcement feature selection. The model adeptly extracts comprehensive motor intention features by integrating pertinent information from both electroencephalogram (EEG) and electromyogram (EMG). Furthermore, reinforcement learning is employed for judicious feature selection, yielding promising outcomes in subsequent experiments. The study utilizes publicly available datasets to diagnose the motion intention of the subjects, complemented by ablation experiments to affirm the efficacy of the model components. In instances of feature-level fusion, the model demonstrated a noteworthy enhancement in the average five-classification accuracy, surpassing results obtained from isolated EEG and EMG experiments by 28.46 % and 12.68 %, respectively. The primary objective of this research is to furnish robust model support for motor rehabilitation training with exoskeletons, offering personalized solutions for the restoration of motor functions
Modeling and Analysis of Business Process Management Systems Using Timed Workflow Nets with Tables
Full text linkThe existing modeling methods for business process management systems (BPMS) focus on the logical and abstract data layers but often ignore operations related to the underlying database, thus failing to describe system behavior accurately. Workflow nets with tables (WFT-nets) compensate for the lack of description of database table operations in existing modeling methods. However, for timed business process management systems (TBPMS), their correct behavior depends on the logical correctness of the results obtained by the operational process and the time required for each activity to be correctly executed within a specified time. Since WFT-nets do not consider time properties, they are unable to describe time-related activities in TBPMS, potentially leading to incorrect results. This paper introduces timed workflow nets with tables (TWFT-nets), which add time elements to transitions to simulate time-constrained activities in the system. Additionally, we assign different labels to represent the execution strategies of activities under different time constraints. To analyze the soundness of TWFT-nets, we propose a timed database computation tree logic (TDCTL) model checking method and define soundness from three perspectives: logic control layer, data design requirements, and time constraints. We transform soundness into TDCTL formulas, provide a model checking algorithm, and develop a tool. Experiments show the effectiveness of our methods