Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Multicriteria Decision Making Approach to Support Adoption of Connected and Autonomous Vehicles
Get PDFConnected and autonomous vehicles (CAV) have recently attracted policymakers, manufacturers, and customers' attention. Despite their numerous benefits, CAVs still have to overcome many challenges related to the implementation and market penetration. When not dominated by financial constraints, the CAV adoption heavily depends on how policymakers and the government address the other challenges, including public perception, rules, and regulations. This study aims at formulating recommendations to support decision-makers in choosing the most appropriate and sustainable strategy to implement CAV technology. To do so, key barriers were first identified based on the literature review and discussions with decision-makers. Moreover, long-term adoption of CAV technologies in alternative future scenarios is developed. Multicriteria decision-making analysis was conducted to weigh these barriers and rank different strategies of CAV implementation. The transportation system of the Sultanate of Oman was used as a study case. It was found that the lack of technical skills and policies/regulations are the main barriers to the adoption of CAV technologies. To overcome these barriers, suggested strategies include establishing low-cost and short-term solutions, providing training to transportation professionals, and investing in statewide radio communications/IoT for emergency responses
Canny Edge Detection Analysis Based on Parallel Algorithm, Constructed Complexity Scale and CUDA
Get PDFEdge detection is especially important for computer vision and generally for image processing and visual recognition. On the other hand, digital image processing is widely used in multiple science fields such as medicine, X-ray analysis, magnetic resonance tomography, computed tomography, and cosmology, i.e. information collection from satellites, its transferring, and analysis. Any step of image processing, from obtaining the image to its segmentation and object recognition is followed by image noise. The processing speed is vital in popular fields that demand image analysis in real time. In this work, we have proposed an approach of parallel computing of the Canny algorithm using CUDA technology, the complexity of object recognition is analyzed according to the type of the image noise and the level of its density. The sequenced implementation on GPU and the parallel implementation on GPU was considered. The results were analyzed for efficiency and reliability. Also, parallel acceleration is calculated according to the size of the incoming image. The manipulations with the image showed the growth of processing speed of 68 times, whereas the manipulations with the size of the kernel showed the growth of processing speed of 26 times. Another contribution of this work is the analysis of the complexity of object recognition depending on the type of image noise and the level of its density. Furthermore, the increase of Gaussian noise density linearly increases the complexity of object recognition
Guard-Function-Constraint-Based Refinement Method to Generate Dynamic Behaviors of Workflow Net with Table
Get PDFIn order to model complex workflow systems with databases, and detect their data-flow errors such as data inconsistency, we defined Workflow Net with Table model (WFT-net) in our previous work. We used a Petri net to describe control flows and data flows of a workflow system, and labeled some abstract table operation statements on transitions so as to simulate database operations. Meanwhile, we proposed a data refinement method to construct the state reachability graph of WFT-nets, and used it to verify some properties. However, this data refinement method has a defect, i.e., it does not consider the constraint relation between guard functions, and its state reachability graph possibly has some pseudo states. In order to overcome these problems, we propose a new data refinement method that considers some constraint relations, which can guarantee the correctness of our state reachability graph. What is more, we develop the related algorithms and tool. We also illustrate the usefulness and effectiveness of our method through some examples
Using Machine Learning for Intrusion Detection Systems
Get PDFGiven the importance of the computer systems in our daily life today, it is decisive to be able to protect the computer systems against attacks. Intrusion Detection Systems (IDSs) are the crucial component of modern cybersecurity systems. IDSs are built-in in the devices of the major providers such as Cisco and Juniper. Since the early days of the Internet up to now, the IDSs rely heavily on signature-based detection methods. However, in recent years, researchers utilize the power of machine learning techniques and achieve very good performance in classifying network attacks. In this paper, we analyze the machine learning techniques that have been proposed in recent years. We propose some new techniques to improve the performance of the existing methods. The experimental results using real-world datasets show that our suggestions can boost the predictive accuracy of the models
Brain Tumor Detection Using Selective Search and Pulse-Coupled Neural Network Feature Extraction
Get PDFThe identification of tumorous tissues in the brain based on Magnetic Resonance Images (MRI) analysis is a challenging and time consuming task that highly depends on radiologists expertise. As prompt diagnosis of tumors can often be inherent to the patient's survival, it is however crucial to decrease the amount of time spent on the manual analysis of MRI while increasing the accuracy of the detection process. To tackle these issues, many research works have already investigated efficient computer vision systems. They offer new opportunities to assist health care providers in the establishment of fast and more accurate tumor detection, classification and segmentation. However, often based on deep learning methods, the development and tuning of these solutions remains time and energy consuming while inducing a lack of explainability in the decision making system. In this study, we respond to these issues by solving a brain tumor detection task using the Selective Search (SS) algorithm coupled with a simplified Pulse-Coupled Neural Network (PCNN) for visual feature extraction and detection validation. The performed experiments showed promising results in terms of computational cost and detection accuracy. This leads to the development of a light-weight brain tumor detection system
New Strategy Based on RBF Network to Develop a Collaborative Filtering Recommender System
Get PDFCollaborative filtering is a popular recommendation algorithm. It predicts user's interests according to the ratings or behaviour of other users in the system. However, the collaborative filtering recommender system suffers from several major limitations including scalability, sparsity, and cold start. In this paper, a collaborative filtering recommendation approach using radial basis function (RBF) network and power method is proposed. The proposed system has offline and online phases. In the offline phase, the sparse user-item rating matrix is completed by using RBF network based on Cover's theorem on the separability of patterns. RBF network learning is done by unsupervised kernel-based fuzzy c-means clustering algorithm for selecting RBF centers, and supervised gradient descend method for selecting RBF weights. In the offline phase, we predict non-rated items of a user. Then the full rating matrix is used to rank all the users. The ranking is done by solving an eigenvalue problem. This paper overcomes the scalability problem by clustering the users, the sparsity problem by completing the sparse rating matrix, and the new user cold start problem by recommending the top rated items of the high-ranked user. The results of the experiments, on the benchmark data sets, show that the proposed system produces high quality recommendation, in terms of accuracy and quality
PVRAR: Point-View Relation Neural Network Embedded with Both Attention Mechanism and Radon Transform for 3D Shape Recognition
Get PDFOwing to the favorable performance of deep neural networks for 3D shape recognition, an increasing number of researchers are interested in designing novel 3D shape descriptors. However, the relationship between multiple views and point clouds needs to be further elucidated. We propose a multimodal method that combines the features of point clouds and multiple views, i.e., point-view relation neural network embedded with both attention mechanism and Radon transform, to obtain better descriptors. First, a two-dimensional linear Radon transform is performed to investigate linear and color features in multiple views, and the features are used as the input of our network to enable significant distinctions between different views. Moreover, a convolutional block attention module is adopted to enhance the features of point clouds and hence improve the expression ability of feature descriptors. The effectiveness of the proposed method is verified using ModelNet40 and ModelNet10 datasets. Experimental results show that our method can effectively improve the capability of feature extraction and expression as well as achieve state-of-the-art performance on two well-known 3D datasets
Risk Assessment Method of Cloud Environment
Get PDFCloud technology usage in nowadays companies constantly grows every year. Moreover, the COVID-19 situation caused even a higher acceleration of cloud adoption. A higher portion of deployed cloud services, however, means also a higher number of exploitable attack vectors. For that reason, risk assessment of the cloud environment plays a significant role for the companies. The target of this paper is to present a risk assessment method specialized in the cloud environment that supports companies with the identification and assessments of the cloud risks. The method itself is based on ISO/IEC 27005 standard and addresses a list of predefined cloud risks. Besides, the paper also presents the risk score calculation definition. The risk assessment method is then applied to an accounting company in a form of a case study. As a result, 24 risks are identified and assessed within the case study where each risk included also exemplary countermeasures. Further, this paper includes a description of the selected cloud risks
Seabed Sediment Classification for Sonar Images Based on Deep Learning
Get PDFAlong with the development of sonar technology, the detection accuracy and stability of sonar have been improved. A large amount of seabed sediment information can be obtained through sonar detection. However, this information is often accompanied by noise interference, resulting in poor quality of the generated images. Moreover, sonar images are different from conventional images. There are single-channel images. The model needs to classify the images according to the texture features in the images. Coupled with the scarcity of sonar data, this makes it difficult to accurately classify the seabed sediment. According to the characteristics of sonar images, we propose the ShuffleNet-DSE which is a classification model based on deep learning. The ShuffleNet-DSE network is based on ShuffleNet-V2, while ensuring the lightweight of the network, it incorporates feature dense connection and Squeeze-and-Excitation (SE) structure channel self-attention. And combined with the sonar image's characteristics, the partial activation function of the model is changed to the Swish. The experimental results show that compared with the traditional machine learning classification method, ShuffleNet-DSE has greatly improved the classification accuracy and the computational cost. Compared with excellent neural network models such as AlexNet, MobileNet-V3, GoogLeNet and ResNet, it is more suitable for sonar image processing
Synthesis of Liveness-Enforcing Petri Net Supervisors Based on a Think-Globally-Act-Locally Approach and a Structurally Minimal Method for Flexible Manufacturing Systems
Get PDFThis paper proposes a deadlock prevention policy for flexible manufacturing systems (FMSs) based on a think-globally-act-locally approach and a structurally minimal method. First, by using the think-globally-act-locally approach, a global idle place is temporarily added to a Petri net model with deadlocks. Then, at each iteration, an integer linear programming problem is formulated to design a minimal number of maximally permissive control places. Therefore, a supervisor with a low structural complexity is obtained since the number of control places is greatly compressed. Finally, by adding the designed supervisor, the resulting net model is optimally or near-optimally controlled. Three examples from the literature are used to illustrate the proposed method