In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience.
Due to the destruction caused by wildfires underscores the critical need for proactive wildfire management strategies. Particularly, early detection of wildfires can mitigate their impact on the environment and economic loss and save human lives. This research study builds upon our prior work on refining and improving identifying early-stage wildfires. Previous research focused on using a deep-learning model for detecting early wildfires. This study aims to classify the visibility levels of forest sensory images to improve the deep learning model. Due to the constraints of existing public wildfire detection datasets, we have created a specialized forest sensory images dataset tailored specifically for detecting wildfires in their initial stages. We have used an Open-source computer vision library (OpenCV) for image processing integrated with TensorFlow and Keras to create a hybrid deep learning model. The results showed a significant improvement in the accuracy from 84% to 87% in detecting early spots of fire in images captured by surveillance cameras or/satellites. The Convolutional Neural Network model has been evaluated by crucial classification metrics, including accuracy, precision, recall, and f1 values. The study aids in progressing the efficacy of early wildfire detection, furnishing valuable perspectives for urban centers and nations contending with the dangers presented by these catastrophic natural events.
In recent years, there has been concern in Japan that traditional culture cannot be passed on due to a lack of successors, resulting in decline. The tea ceremony, one of Japan's traditional cultures, has seen a decline in the number of participants year by year, and its continuation is at risk. We believe that instructors are indispensable in order to stop the decline in the tea ceremony population. By clarifying the characteristics of tea ceremony movements, we aim to construct a system that can replace instructors, and to help preserve and pass on the tea ceremony. Therefore, in this study, we quantitatively analyze the characteristics of the way the host's body moves during the tea ceremony based on the changes of velocity. Among the operations performed before making tea, the “Preparation,” “Handling the fukusa,” “Re-handling the fukusa,” and “Passing the chasen” are analyzed. The joints of the human body are determined from the 3D coordinates obtained during the operations using Kinect. The velocities of the obtained joint points are calculated, and the analysis is performed focusing on the velocity change. The results of the movement analysis revealed movements specific to experts.
In this paper, taking into account the limitation of not being able to touch or shine a laser on a part of the paintings, including the three-dimensional objects on display, the SfM method was used to acquire three-dimensional shapes from the images, texture as color information and diffuse reflection characteristics were added to the acquired three-dimensional data, and shading was also added in real time to create CG data. Using this CG data and High-Resolution images, museum staff were asked to evaluate the sensory reality of the CG images in comparison with the actual artworks.
At the National Institute for Fusion Science, high-temperature plasma is generated in the Large Helical Device (LHD). Numerous videos capturing plasma discharges during nuclear fusion experiments have been recorded. Analyzing these videos may unveil new discoveries and ensure the safe operation of experiments. Thus, estimating the future states of plasma light emission is essential for optimizing operational parameters in experiments. In current methods for plasma video prediction, a sequence of multiple frames is typically used as input to analyze visual movement trends and predict future plasma discharge conditions or generate future frames in plasma videos. Our study focuses on a more challenging task of video prediction using a single frame of plasma image. We propose a method for predicting sequential future frames containing the image of the visible light from the plasma based on a single given frame by using a conditional GAN model. We experimentally evaluate this approach and find that our proposed method accurately forecasts the future states of plasma images in each frame of the videos.
