Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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1506 research outputs found
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Formal Modelling of Program Dependence Net for Software Model Checking
Full text linkProgram dependence net (PDNet) is a kind of Petri nets which can represent concurrent systems and software to apply the automata-theoretic approach for software model checking on Linear Temporal Logic (LTL). This paper presents a formal modelling method to construct a PDNet which is consistent with the behavior of multi-threaded C programs (PThread programs) from a source code. For concurrent programs with a function call and POSIX threads, we propose the formal operational semantics by the labeled transition system (LTS). We formalize the statements by the basic PDNet structure based on LTS operational semantics. Then, we propose the formal modelling method to build a basic flow to simulate the execution of PThread programs. Finally, we give a case study to illustrate the formal modelling method for verifying PThread programs on LTL properties
Improved Swin Transformer-Based Model for Hot-Rolled Strip Defect Detecting
Full text linkHot-rolled steel strip plays an important role in the field of industrial manufacturing. In addition, defects on its surface affect the aesthetics of the subsequent products and their corrosion resistance, wear resistance, and fatigue strength. However, the existing methods are difficult to learn or capture discriminative feature representations, resulting in poor detection performance. Therefore, its surface defect detection faces two main challenges: one is the insufficient ability to extract local features, and the other is the limited ability to detect multi-scale targets. To address the above issues, we propose a Residual Deformable Convolution and Double LayerNorm Swin Transformer and Channel Expansion Feature Pyramid Networks (RTCN) multi-scale hot-rolled strip surface defect detection model, which adopts Double LayerNorm Swin Transformer (DLST) and as Residual Deformable Convolution Block (RDCB) its backbone network to increase the sensitivity of the model's detection of small and irregular defects. In addition, we adopt Channel Expansion Feature Pyramid Networks (CEFPN) to introduce more feature dimensions to better capture the structure and semantic image information. Ultimately, we assess the proposed model using the publicly available NEU-DET dataset. Our comprehensive testing shows that the model developed in this paper beats the most advanced approach by 1.1 % to 7.2 % in mAP
Multi-Label Bird Species Classification Using Sequential Aggregation Strategy from Audio Recordings
Full text linkBirds are excellent bioindicators, playing a vital role in maintaining the delicate balance of ecosystems. Identifying species from bird vocalization is arduous but has high research gain. The paper focuses on the detection of multiple bird vocalizations from recordings. The proposed work uses a deep convolutional neural network (DCNN) and a recurrent neural network (RNN) architecture to learn the bird's vocalization from mel-spectrogram and mel-frequency cepstral coefficient (MFCC), respectively. We adopted a sequential aggregation strategy to make a decision on an audio file. We normalized the aggregated sigmoid probabilities and considered the nodes with the highest scores to be the target species. We evaluated the proposed methods on the Xeno-canto bird sound database, which comprises ten species. We compared the performance of our approach to that of transfer learning and Vanilla-DNN methods. Notably, the proposed DCNN and VGG-16 models achieved average F1 metrics of 0.75 and 0.65, respectively, outperforming the acoustic cue-based Vanilla-DNN approach
Enhanced Deep Learning-Based Model for Sentiment Analysis to Identify Sarcasm Appeared in the News
Full text linkIn the field of natural language processing (NLP), detecting emotions or sentiments can be a challenging task, and sometimes emotions can be more complex than just positive or negative. However, detecting sarcasm in textual data adds another layer of complexity. Despite this, identifying the underlying sarcasm in the text has become a recent area of interest among NLP researchers. Headlines in newspapers often use sarcasm to engage readers, but readers may have difficulty recognizing it, leading to a misinterpretation of the news and spreading misinformation. As a result, there is an urgent need for technology that can automatically identify sarcasm with high accuracy. Recent studies in this domain have revealed a need for a robust and efficient model. Deep learning approaches have proven to be effective in sarcasm detection. In this work, we propose a novel two-stage model that uses a word-embedding technique to select relevant features followed by an advanced deep-learning architecture to classify sarcasm in news headlines. Our proposed method demonstrates promising results in identifying sarcasm in text with an accuracy rate of approximately 97 %. We have fine-tuned the hyper-parameters to increase the precision level, which enhances the efficacy of our model. Our work provides a significant contribution to the field of NLP by presenting a reliable and effective model for sarcasm detection. The comparison of our model with recent advancements indicates that our approach outperforms them. By using our model, readers can avoid misinterpretations and the spreading of misinformation. Therefore, our work can have a positive impact on society, and we believe that it can inspire future research in the field of sarcasm detection
Intelligent Fusion Recommendation Algorithm for Social Network Based on Fuzzy Perception
Full text linkIn order to improve the effect of intelligent fusion recommendation under the background of social network, this paper combines the fuzzy perception algorithm to research the intelligent fusion recommendation algorithm of social network. Moreover, this paper proposes a task offloading scheme that relies on V2V communication to utilize idle computing resources in a "resource pool". In addition, this paper formulates the computational task execution time as a min-max problem to reduce the storage overhead to optimize the total task execution time. Numerical results show that the proposed scheme greatly reduces the task execution time. The introduced particle swarm optimization algorithm also proves the convergence speed and accuracy of the optimization problem. The research verifies that the intelligent fusion recommendation algorithm for social network based on fuzzy perception has good social network data fusion effect and can effectively improve the effect of intelligent fusion recommendation
Edge Computing Application of Expressway Intelligent Transportation System Based on IoT Technology
Full text linkWith the comprehensive arrival of the fifth generation mobile communication technology (5G) era, the Internet of Things (IoT) has developed rapidly and is widely used in various industries. As one of the modern transportation signs, highways play an irreplaceable role in social and economic development. The research on Intelligent Traffic Systems (ITS) on highways has always been a hot topic. With the continuous improvement of the highway network and the increasing mileage, the efficiency of the existing highway ITS in processing information and solving problems is relatively low. Introducing new technologies to achieve breakthroughs has become a top priority. Edge computing is widely used in the Internet of Things technology because of its low latency and high response speed. Based on the characteristics of edge computing technology, this paper conducted in-depth research on expressway ITS, and analyzed the specific functions of intelligent system through IoT technology and edge computing technology. Through analysis and experiment, it is concluded that in the experimental highway area, the ITS using edge computing technology has slightly improved compared with the traditional ITS in all aspects. The response speed of the monitoring system has increased by 4.9 %, the congestion rate has decreased by 11.27 %, the congestion duration has decreased by 37.3 %, and the accident rate has decreased by 7.63 %. The edge computing application of expressway ITS based on IoT technology has improved the safety and comfort of residents' travel. It not only improves the intelligence level of intelligent transportation systems, but also enhances the safety and comfort of residents' travel, meeting the travel needs of modern people
Clustering Mining Algorithm of Internet of Things Database Based on Python Language
Full text linkIn order to solve the problems of reading delay in data mining of the Internet of Things database, a clustering mining algorithm of the Internet of Things database based on Python language is proposed. We designed an improved crawler algorithm based on the open-source structure of scratch through Python language, judge the similarity of recruitment data topics in the Internet of Things database through Bayesian classifier, and crawl the recruitment data in the Internet of Things database: the number of keywords in the text space, the degree of keyword extraction, and the number of keyword data in the text space. The time series model is used to eliminate the delay of text features. On this basis, the semi-supervised learning and semi-cluster analysis method is used to construct the corresponding classifier, complete the adaptive classification process of the text data stream and realize the clustering mining of the Internet of Things database based on Python language. The experimental results show that this method has a low reading delay, and can mine the attention, number of posts and click time frequency of the Internet of Things database from which the recruitment data are obtained
Multilevel Ensemble Model for Load Prediction on Hosts in Fog Computing Environment
Full text linkWith the growing demand for various IoT applications, fog nodes frequently become overloaded. Fog computing requires effective load balancing to maximize resource utilization. It is essential to determine the load on host to obtain workload consolidation. Various random parameters, including CPU utilization, the number of CPU cores, RAM, memory allocated, memory available, disk I/O, and network I/O are employed to better comprehend host workloads. In the proposed work, the host's load status is detected using an ensemble approach into three categories: under-loaded, balanced and overloaded. Further, the proposed work considers three case studies and varying numbers of virtual machines (VMs) are executed with various parameter combinations. In each case study, a different number of VMs are executed in parallel on two different platforms. In the proposed study, we predicted the load on multiple hosts by employing a variety of advanced machine-learning models. To construct an ensemble model, we selected models with higher accuracy based on retrieved performance evaluation criteria. The ensemble method is applied to deal with the worst-case scenario of the model prediction. For a selected number of case studies, the Random Forest model, Ada Boost Classifier, Gradient Boost and Decision Tree models perform better than other models. These state-of-the-art predictive models are outperformed by our proposed ensemble model and achieves an improved accuracy of nearly 82 % by correctly classifying hosts as overloaded, underloaded and balanced
Interpretable Risk Assessment Methods for Medical Image Processing via Dynamic Dilated Convolution and a Knowledge Base on Location Relations
Full text linkExisting approaches to image risk assessment start with the uncertainty of the model, yet ignore the uncertainty that exists in the data itself. In addition, the decisions made by the models still lack interpretability, even with the ability to assess the credibility of the decisions. This paper proposes a risk assessment model that unites a model, a sample and an external knowledge base, which includes: 1. The uncertainty of the data is constructed by masking the different decision-related parts of the image data with a random mask of probabilities. 2. A dynamically distributed dilated convolution method based on random directional field perturbations is proposed to construct the uncertainty of the model. The method evaluates the impact of different components on the decisions within the local region by locally perturbing the attention region of the dilated convolution. 3. A triadic external knowledge base with relative interpretability is presented to reason and validate the model's decisions. The experiments are implemented on the dataset of CT images of the stomach, which shows that our proposed method outperforms current state-of-the-art methods
BTAN: Lightweight Super-Resolution Network with Target Transform and Attention
Full text linkIn the realm of single-image super-resolution (SISR), generating high-resolution (HR) images from a low-resolution (LR) input remains a challenging task. While deep neural networks have shown promising results, they often require significant computational resources. To address this issue, we introduce a lightweight convolutional neural network, named BTAN, that leverages the connection between LR and HR images to enhance performance without increasing the number of parameters. Our approach includes a target transform module that adjusts output features to match the target distribution and improve reconstruction quality, as well as a spatial and channel-wise attention module that modulates feature maps based on visual attention at multiple layers. We demonstrate the effectiveness of our approach on four benchmark datasets, showcasing superior accuracy, efficiency, and visual quality when compared to state-of-the-art methods.