Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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1506 research outputs found
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Modeling and Verification of Chinese Wall Policy Based on Petri Nets with Data
Full text linkInformation security is an important issue in the design and development of information systems. As a well-known information security policy, Chinese Wall policy concerns the conflict of interest among sensitive information items. Since it is widely applied in many fields, it is important to explore the verification methods. Petri nets are a widely used formal method in the modeling and verification of information systems, and they are suitable for verifying Chinese Wall policy due to the capability of characterizing the concurrency. Particularly, some studies utilize colored Petri nets for modeling and verification of Chinese Wall policy. However, they do not characterize data operations including read, write and delete, which may affect the verification results. In this paper, we utilize Petri nets with data (PD-nets) to model and verify this policy. Specifically, we propose PD-nets for Chinese Wall policy to depict the control-flows, data-flows and data operations of information systems and introduce configurations and reachability graphs to describe the running states. We give theorems to prove the correctness of our method. Based on these theorems, we develop an algorithm to detect the violations of Chinese Wall policy. Furthermore, a case study is presented to show the effectiveness of our method, especially in modeling data operations and verifying their relevant CW policy
MINet: A Pedestrian Trajectory Forecasting Method with Multi-Information Feature Fusion
Full text linkPedestrian trajectory prediction plays an exceptionally vital role in autonomous driving, enabling advanced analysis and decision-making in certain scenarios to ensure driving safety. Predicting pedestrian trajectories is a highly complex task, encompassing static scenes, dynamic scenes, and subjective intent. To enhance the accuracy of pedestrian trajectory prediction, it is crucial to model these scenarios, extract relevant features, and fuse them effectively. However, existing methods only consider some of the scenarios mentioned above and extract static scene features through manual annotation of road key points, which fails to meet the demands of autonomous driving in complex traffic scenarios. To overcome these limitations, this paper introduces MINet -- a network that employs multi-information feature fusion. Unlike previous approaches, MINet adopts a more automated approach to extract static scenes, including sidewalks and lawns. Moreover, the network incorporates pedestrian destination modeling to improve prediction accuracy. Furthermore, to tackle the challenge of collision avoidance in crowded spaces, this paper incorporates the extraction of dynamic scene changes through relative velocity modeling of objects. The proposed network achieved an improvement of 47.7 % in the ADE metric and 62.6 % in the FDE metric on the ETH/UCY dataset. In the SDD dataset, there was an improvement of 18.4 % in the ADE metric and 35.2 % in the FDE metric
Collaborative Filtering Algorithm Based on Deep Denoising Auto-Encoder and Attention Mechanism
Full text linkThe burgeoning of e-commerce and online platforms has led to an explosion in data volume and diversity of user preferences, making effective recommendation systems crucial for personalizing user experiences. While collaborative filtering algorithms are traditionally favoured for their ability to leverage user-item interactions, they grapple with data sparsity and noise challenges. To tackle these challenges, Various approaches have emerged in recent years to tackle these challenges. Recent strides in deep learning, particularly autoencoders and neural networks, have shown promise in addressing these issues. However, limitations persist, such as suboptimal feature extraction and the underutilization of combined nonlinear and linear latent features in traditional autoencoders, as well as the overlooked impact of active users in recommendations. Addressing these research gaps, this study introduces a novel recommendation algorithm that synergizes a deep denoising autoencoder with an attention mechanism, aiming to refine recommendation performance by mitigating data sparsity and enhancing feature extraction. This fusion approach innovatively combines nonlinear and linear latent features and incorporates a neural attention mechanism, significantly improving the precision and personalization of recommendations. Ultimately, the proposed algorithm's effectiveness is assessed and benchmarked against state-of-the-art approaches, demonstrating its potential to revolutionize recommendation systems by offering more accurate and user-tailored suggestions
Machine Learning Approach for Ecological Public Transport Systems
No full textUsing convolutional neural networks and genetic programming, this study presents a new composite technique for modeling bicycle traffic in the town of Novo mesto, Slovenia. Every town needs public passenger transportation because the current transportation system has well-known issues like congestion, environmental effect, a lack of parking spaces, increased safety hazards, and excessive energy consumption. Urban transport is crucial for the functionality of any city. High-quality and usable urban transport not only affects the functionality of the city as an economic and social center, but it also reduces the number of passenger cars on the streets. The Novo mesto region, which has a population of around 30 000 people, is a major industrial center that is strongly reliant on metropolitan transportation. Unfortunately, the urban traffic of Novo mesto still has a relatively weak influence on the transport connectivity of the wider area. The study's goal is to examine and simulate bicycle rentals. For 35 weeks, convolutional neural networks and genetic programming were utilized to anticipate bicycle traffic. Three types of models were applied to study the impact of weather conditions on bicycle traffic: linear regression, genetic programming, and feed-forward neural networks. The proposed approach will be useful for cities with similar needs around the world
YOLO-DTO: Automotive Door Panel Fastener Detection Algorithm Based on Deep Learning
Full text linkThe common detection of fasteners of automobile door panels is based on the method of template matching, which has the problems of low detection accuracy and poor real-time performance under the influence of different lighting and different placement positions. To improve the detection speed and accuracy of fasteners in complex scenes, a small object detection algorithm, YOLO-DTO (Detect Tiny Object), was proposed based on the YOLOv8 algorithm. Firstly, considering that the algorithm uses strided convolution to compress the input image prematurely, resulting in the loss of fine-grained information in the early stage of the image, which makes it difficult to recover the complete detail information in the subsequent feature fusion process, this paper modifies the convolution module in the early stage of the algorithm and introduces the SPD (SPace-to-Depth) module to reconstruct the early stage of the original algorithm. Secondly, a selective attention module is embedded in the Neck output position of the algorithm to enhance the algorithm's ability to pay attention to the context information of fasteners. Finally, to optimize the regression efficiency of the bounding box, the MPDIoU loss function replaced the CIoU loss function. Experimental results show that the average detection accuracy of the YOLO-DTO algorithm is 98.8 %, which is 9.1 % and 1.7 % higher than that of the template matching method and YOLOv8 algorithm, respectively, which meets the detection standards of factory production lines and has the practical value
Advancing Early Diagnosis: Investigating Breast Cancer Cell Segmentation with Deep Learning and Transfer Learning Approaches
Full text linkBreast cancer, a critical global health concern, necessitates accurate and timely diagnosis. This research introduces a novel methodology that harnesses modern technologies, including deep learning and transfer learning, to enhance breast cancer cell segmentation. The study commences with meticulous dataset selection and preprocessing, followed by image segmentation using advanced techniques to differentiate between benign and malignant cells effectively. Two significant algorithms, Convolutional Neural Networks (CNN) and AlexNet, are employed, achieving remarkable classification accuracy of 94.5 % and 92.3 %, respectively. These models exhibit robust performance in identifying intricate patterns and features in breast cancer cell images, enabling precise diagnoses. Moreover, this study evaluates the models' performance on unseen data, affirming their sustained efficacy in clinical settings. The CNN model excels in accurately classifying and segmenting breast cancer cells, while AlexNet demonstrates transfer learning capabilities, which is particularly advantageous in scenarios with limited data availability. The findings underscore the potential of deep learning and transfer learning techniques in augmenting breast cancer diagnostics, paving the way for more accurate and effective cancer treatments
Knowledge Graph Representation Learning by Text Encoding and Graph Structure
No full textKnowledge graph representation learning aims to embed entities and relationships into low-dimensional space through knowledge graph embedding methods. Because knowledge graphs are incomplete, it is often necessary to complete the knowledge graph through representation learning methods. With the development of pre-trained language models, more and more research applies them to the field of knowledge graph representation learning, using the powerful semantic representation capabilities of pre-trained language models to improve the performance of knowledge graph embedding. Most of the existing methods make use of the semantic information of the triple text but do not fully consider the structural information of the triple and the graph structure information of the knowledge graph. The triple structure reflects the semantic information and relationship pattern of the triple, and the graph structure reflects the surrounding entity's semantic features. To address the above issues, this paper proposes a knowledge graph representation learning method named PREGSE, which is based on pre-trained language models and integrates graph structure information. Firstly, pre-trained language models are employed to encode triplets through text encoding, obtaining vectors for the triplets. Secondly, a graph attention network is utilized to learn various local graph structure information. Lastly, a multi-task learning strategy is applied to simultaneously learn triplet structure information and semantic information. We trained our model on the FB15k-237 and WN18RR datasets, and the results show that on the FB15k-237 dataset, our model improved the MRR metric by 27 % and the Hits@10 metric by 8 % compared to the StAR model. The experiments show that our model can further improve the performance of knowledge graph representation learning
Evaluating Combined Influence of Weighted Analysis Class Diagram Metrics on Early Software Size Estimation
Full text linkAnalysis class diagram (ACD) metrics like number of classes, number of methods, and number of attributes can be used for early software size estimation by project managers during initial project planning. However, not all of these ACD metrics have the same influence on software size. This study aims to empirically determine the relative influence of these ACD metrics on software size using historical data from academia and industry. Using the objective class points (OCP) metric as a base, two new metrics -- enhanced OCP (EOCP) and weighted EOCP (WEOCP) -- are proposed. Separate linear regression-based early software size estimation models are also constructed and validated using the original OCP metric and its newly proposed variants. A comparison of these models reveals that models based on our freshly proposed metrics perform better in terms of early size estimation accuracy
Lightweight Dual-Stream Human Behavior Inference Network Based on Multi-Layer Perceptron
Full text linkThe recognition of human behaviors in videos is a critical domain within human activity analysis. However, the current architectures and mechanisms of human behavior recognition methods dominated by CNN, GCNs, and LSTM are unduly complex resulting in high computational complexity of the models. Furthermore, these methods often exhibit poor robustness when it comes to recognizing behaviors across different environmental conditions and video angles. To address these challenges, this paper introduces a lightweight human skeleton interaction behavior inference network based on a multi-layer perceptron. This network leverages human skeleton information and utilizes minimal prior knowledge to infer limb behavior encoding. To reduce computational complexity, videos are divided into smaller segments, serving as the minimum computation units. This approach integrates three essential types of information: independent global information about individual postures, local interaction information regarding different limb parts, and temporal distance information. These three types of information are coupled through LSTM, incorporating temporal changes into network for recognition and classification. In comparison to previous similar methods, our proposed method is more lightweight, exhibits stronger robustness against interference and enables behavior recognition across different environments and perspectives
Task Offloading Decision and Resource Allocation Strategy Based on Improved DDPG in Mobile Edge Computing
Full text linkIn mobile edge computing (MEC), the mobile device can offload tasks to the server at the edge of the mobile network for execution, thereby reducing the delay of task execution and the energy consumption of the mobile device. However, the limited resources of the edge server prevent the mobile device from offloading all tasks to the edge servers. To solve the problems, this paper constructs a multi-users and single edge server model for mobile edge computing. In order to minimize the weighted total cost combined energy consumption of mobile devices and task execution delay under the constraints of computing resources and storage resource of the edge server, we propose a task offloading decision and resource allocation algorithm based on improved deep deterministic policy gradient (DDPG) -- PERDDPG. In our algorithm, a special reward function is designed to get the reward value for correlating negatively with the total cost. We can obtain the lowest total cost when the algorithm reaches the maximum reward value. Furthermore, we apply prioritized experience replay (PER) to improve DDPG. So, the PERDDPG has a more dynamic MEC scenario for making offloading decisions and computing resource allocation. Simulation results show that the proposed algorithm can get a better convergence speed and improve the cumulative reward compared to the existing algorithms, effectively reducing the weighted total cost of mobile devices and improving the success rate of task execution