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In the domain of outdoor construction within the power industry, working at significant heights is common, requiring stringent safety measures. Workers are mandated to wear hard hats and secure themselves with seat belts to prevent potential falls, ensuring their safety and reducing the risk of injuries. Detecting seat belt usage holds immense significance in safety inspections within the power industry. This study introduces detection method of the seat belt for workers at height based on UAV Image and YOLO Algorithm. The YOLOv5 approach involves integrating CSPNet into the Darknet53 backbone, incorporating the Focus layer into CSP-Darknet53, replacing the SPPF block in the SPP model, and implementing the CSPNet strategy in the PANet model. Experimental results demonstrate that the YOLOv5 algorithm achieves an elevated average accuracy of 99.2%, surpassing benchmarks set by FastRcnn, SSD, YOLOX-m, and YOLOv7. It also demonstrates superior adaptability in scenarios involving smaller objects, validated using a UAV-collected dataset of seat belt images. These findings confirm the algorithm's compliance with performance criteria for seat belt detection at power construction sites, making a significant contribution to enhancing safety measures within the power industry's construction practices.

Given the limited extent of study conducted on the application of ChatGPT in the realm of education, this domain still needs to be explored. Consequently, the primary objective of this study is to evaluate the impact of factors within the extended value-based adoption model (VAM) and to delineate the individual contributions of these factors toward shaping the attitudes of university students regarding the utilization of ChatGPT for instructional purposes. This investigation incorporates dimensions such as social influence, self-efficacy, and personal innovativeness to augment the VAM. This augmentation aims to identify components where a hybrid approach, integrating partial least squares (PLS), artificial neural networks (ANN), deep neural networks (DNN), and classification algorithms, is employed to accurately discern both linear and nonlinear correlations. The data for this study were obtained through an online survey administered to university students, and a purposive sample technique was employed to select 369 valid responses. Following the initial data preparation, the assessment process comprised three successive stages: PLS, ANN, DNN and classification algorithms analysis. Intention is influenced by attitude, which is predicted by perceived usefulness, perceived enjoyment, social influence, self-efficacy, and personal innovativeness. Moreover, personal innovativeness has the maximum contribution to attitude followed by self-efficacy, enjoyment, usefulness, social influence, technicality, and cost. These findings will support the creation and prioritization of student-centered educational services. Additionally, this study can contribute to creating an efficient learning management system to enhance students' academic performance and professional efficiency.

This research aims to reduce social media security risks and develop best practices to help governments address social media security risks more effectively. This research begins by reviewing the different discussions in the literature about social media security risks and mitigation techniques. Based on the extensive review, several key insights were identified and summarized to help organizations address social media security risks more effectively. Many national governments around the world do not have effective social media security policies and are unsure how to develop effective social media security strategies to mitigate social media security risks. This research provides guidance to national governments on mitigating potential social media security risks. This study incorporates ongoing debates in the literature and provides guidance on how to reduce social media security and technological risks. Practical insights are identified and summarized from the extensive literature. More discussions and studies are needed on strategies and practical insights to reduce social media risk for the Indonesian government.

As data privacy issues become more and more sensitive, increasing numbers of websites usually encrypt traffic when transmitting it. This method can largely protect privacy, but it also brings a huge challenge. Aiming at the problem that encrypted traffic classification makes it difficult to obtain a global optimal solution, this paper proposes an encrypted traffic identification model called the ET-BERT and 1D-CNN fusion network (BCFNet), based on multi-scale feature fusion. This method combines feature learning with classification tasks, unified into an end-to-end model. The local features of encrypted traffic extracted based on the improved Inception one-dimensional convolutional neural network structure are fused with the global features extracted by the ET-BERT model. The one-dimensional convolutional neural network is more suitable for the encrypted traffic of a one-dimensional sequence than the commonly used two-dimensional convolutional neural network. The proposed model can learn the nonlinear relationship between the input data and the expected label and obtain the global optimal solution with a greater probability. This paper verifies the ISCX VPN-nonVPN dataset and compares the results of the BCFNet model with the other five baseline models on accuracy, precision, recall, and F1 indicators. The experimental results demonstrate that the BCFNet model has a greater overall effect than the other five models. Its accuracy can reach 98.88%.

The relentless increase in data volume and complexity necessitates advancements in machine learning methodologies that are more adaptable. In response to this challenge, we present a novel architecture enabling dynamic classifier selection on FPGA platforms. This unique architecture combines hardware accelerators of three distinct classifiers—Support Vector Machines, K-Nearest Neighbors, and Deep Neural Networks—without requiring the combined area footprint of those implementations. It further introduces a hardware-based Accelerator Selector that dynamically selects the most fitting classifier for incoming data based on the K-Nearest Centroid approach. When tested on four different datasets, Our architecture demonstrated improved classification performance, with an accuracy enhancement of up to 8% compared to the software implementations. Besides this enhanced accuracy, it achieved a significant reduction in resource usage, with a decrease of up to 45% compared to a static implementation making it highly efficient in terms of resource utilization and energy consumption on FPGA platforms, paving the way for scalable ML applications. To the best of our knowledge, this work is the first to harness FPGA platforms for dynamic classifier selection.

Text-based CAPTCHA is widely used as an online security guard, requiring a user to input letters for classifying human and automated software (known as a bot). However, they are still a problem for usability and robustness. This study investigated the effect of letter spacing, disturbing line orientation and disturbing line color on user test and robustness of text-based CAPTCHA. The 240 CAPTCHAS were tested using Thai undergraduate students. The results show that there were no significant differences in user tests for the three factors. For robustness, disturbing line orientation had no significant difference. However, overlapping letter CAPTCHA was the most significantly robust. CAPTCHA with a disturbing line using the same color as the background was more significantly robust than that using the same color as the foreground. Moreover, if no-spacing letter is used, the effect of disturbing line color is statistically significant in robustness while the effect of that became insignificant when a spacing between letter and overlapping letter are used. We recommend that CAPTCHA with no spacing letter and combined with disturbing line using the same color as the background is suitable for users and its robustness. This can be concluded that letter segmenting technique is not too hard for users (passed 88 %) while it is not too easy for bot attacks (passed 39 %). In terms of security, more studies can still be carried on the CAPTCHA to enabled more robustness against new crime technologies. In terms of usability, on other age groups could be consider.

Knowledge Graphs are a tool to structure (entity, relation, entity) triples. One possible way to construct these knowledge graphs is by extracting triples from unstructured text. The aim when doing this is to maximise the number of useful triples while minimising the triples containing no or useless information. Most previous work in this field uses supervised learning techniques that can be expensive both computationally and in that they require labelled data. While the existing unsupervised methods often produce an excessive amount of triples with low value, base themselves on empirical rules when extracting triples or struggle with the order of the entities relative to the relation. To address these issues this paper suggests a new model: Unsupervised Dependency parsing Aided Semantic Triple Extraction (UDASTE) that leverages sentence structure and allows defining restrictive triple relation types to generate high-quality triples while removing the need for mapping extracted triples to relation schemas. This is done by leveraging pre-trained language models. UDASTE is compared with two baseline models on three datasets. UDASTE outperforms the baselines on all three datasets. Its limitations and possible further work are discussed in addition to the implementation of the model in a computational intelligence context.

In emergency call centers, operators are required to analyze and prioritize emergency situations prior to any intervention. This allows the team to deploy resources efficiently if needed, and thereby provide the optimal assistance to the victims. The automation of such an analysis remains challenging, given the unpredictable nature of the calls. Therefore, in this study, we describe our attempt in improving an emergency calls processing system’s accuracy in the classification of an emergency’s severity, based on transcriptions of the caller’s speech. Specifically, we first extend the baseline classifier to include additional feature extractors of different modalities of data. These features include detected emotions, time-based features, and the victim’s personal information. Second, we experiment with a multi-task learning approach, in which we attempt to detect the nature of the emergency on the one hand, and improve the severity classification score on the other hand. Additional improvements include the use of a larger dataset and an explainability study of the classifier’s decision-making process. Our best model was able to predict 833 emergency calls’ severity with a 71.27% accuracy, a 5.33% improvement over the baseline model. Moreover, we extended our tool with additional modules that can prove to be useful when handling emergency calls.

Information extraction (IE) aims to discover and extract valuable information from unstructured text. This problem can be decomposed into two subtasks: named entity recognition (NER) and relation extraction (RE). Although the IE problem has been studied for years, most work efforts focused on jointly modeling these two subtasks, either by casting them into a structured prediction framework or by performing multitask learning through shared representations. However, since the contextual representations of entity and relation models inherently capture different feature information, sharing a single encoder to capture the information required by both subtasks in the same space would harm the accuracy of the model. Recent research (Zhong and Chen, 2020) has also proved that using two separate encoders for NER and RE tasks respectively through pipeline method are effective, with the model surpassing all previous joint models in accuracy. Thus, in this paper, we design An Pipeline method Information Extraction module called APIE, APIE combines the advantages of both pipeline methods and joint methods, demonstrating higher accuracy and powerful reasoning abilities. Specifically, we design a multi-level feature NER model based on attention mechanism and a document-level RE model based on local context pooling. To demonstrate the effectiveness of our proposed approach, we conducted tests on multiple datasets. Extensive experimental results have shown that our proposed model outperforms state-of-the-art methods and improves both accuracy and reasoning abilities.

In recent years, the field of emerging computer vision systems has witnessed significant advancements in automated disease diagnosis through the utilization of vision-oriented technology. This article proposes an optimal approach for detecting the presence of ailments in Rohu fish. The aims of our research is to identify the most significant features based on Analysis of Variance (ANOVA) feature selection and evaluate the best performance among all features for Rohu fish disease recognition. At the outset, diverse techniques for image preprocessing were employed on the acquired images. The region affected by the disease was partitioned through utilization of the K-means clustering algorithm. Subsequently, 10 distinct statistical and Gray-Level Co-occurrence Matrix (GLCM) features were extracted after the image segmentation. The ANOVA feature selection technique was employed to prioritize the most significant features N (where 5 $\le$ N $\le$ 10) from the pool of 10 categories. The Synthetic Minority Oversampling Technique, often known as SMOTE, was applied to solve class imbalance problem. After conducting training and testing on nine different machine learning (ML) classifiers, an evaluation was performed to estimate the performance of each classifier using eight various performance metrics. Additionally, a receiver operating characteristic (ROC) curve was generated. The classifier that utilized the Enable Hist Gradient Boosting algorithm and selected the top 9 features demonstrated superior performance compared to the other eight models, achieving the highest accuracy rate of 88.81%. In conclusion, we have demonstrated that the feature selection process reduces the computational cost.