The long‐term research project Posthuman AIchitectural Speculations is a collaboration between Alberto Fernández González and Mark Garcia, both of whom are lecturers at the Bartlett School of ...Architecture, University College London, and PhD candidates on the Architecture and Digital Theory programme there. Here they investigate how well artificial intelligence can help develop posthuman architectures and the symbiotic relationship between language and AI as a way of speculating about spatial futures.
Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, ...leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Drug side effects (DSEs) or adverse drug reactions (ADRs) are a major concern in the healthcare industry, accounting for a significant number of annual deaths in Europe alone. Identifying and ...predicting DSEs early in the drug development process is crucial to mitigate their impact on public health and reduce the time and costs associated with drug development. Objective: In this study, our primary objective is to predict multiple drug side effects using 2D chemical structures, especially for COVID-19, departing from the conventional approach of relying on 1D chemical structures. We aim to develop a novel model for DSE prediction that leverages the CNN-based transfer learning architecture of ResNet152V2. Motivation: The motivation behind this research stems from the need to enhance the efficiency and accuracy of DSE prediction, enabling the pharmaceutical industry to identify potential drug candidates with fewer adverse effects. By utilizing 2D chemical structures and employing data augmentation techniques, we seek to revolutionize the field of drug side-effect prediction. Novelty: This study introduces several novel aspects. The proposed study is the first of its kind to use 2D chemical structures for predicting drug side effects, departing from the conventional 1D approaches. Secondly, we employ data augmentation with both conventional and diffusion-based models (Pix2Pix), a unique strategy in the field. These innovations set the stage for a more advanced and accurate approach to DSE prediction. Results: Our proposed model, named CHEM2SIDE, achieved an impressive average training accuracy of 0.78. Moreover, the average validation and test accuracy, precision, and recall were all at 0.73. When evaluated for COVID-19 drugs, our model exhibited an accuracy of 0.72, a precision of 0.79, a recall of 0.72, and an F1 score of 0.73. Comparative assessments against established transfer learning and machine learning models (VGG16, MobileNetV2, DenseNet121, and KNN) showcased the exceptional performance of CHEM2SIDE, marking a significant advancement in drug side-effect prediction. Conclusions: Our study introduces a groundbreaking approach to predicting drug side effects by using 2D chemical structures and incorporating data augmentation. The CHEM2SIDE model demonstrates remarkable accuracy and outperforms existing models, offering a promising solution to the challenges posed by DSEs in drug development. This research holds great potential for improving drug safety and reducing the associated time and costs.
Facial image generation from textual generation is one of the most complicated tasks within the broader topic of Text-to-Image (TTI) synthesis. It is relevant in several fields of scientific ...research, cartoon and animation development, online marketing, game development, etc. There have been extensive studies on Text-to-Face (TTF) synthesis in the English language. However, the amount of relevant existing work in Bangla is limited and not comprehensive. As the TTF field is not vastly prospected for Bangla language, the objective of this study sets forth to explore the possibilities in the field of Bangla Natural Language Processing and Computer Vision. In this paper, a novel system for generating highly detailed facial images from textual descriptions in the Bangla language is proposed. The proposed system named Mukh-Oboyob consists of two essential components: a pre-trained language model, BanglaBERT, and Stable Diffusion. BanglaBERT, a transformer-based pre-trained text encoder, is a language model used to transform Bangla sentences into vector representations. Stable Diffusion is used by Mukh-Oboyob to generate facial images utilizing the text embedding of the Bangla sentences. Moreover, the work uti-lizes CelebA Bangla, a modified version of the CelebA dataset consisting of face images, Bangla facial attributes, and Bangla text descriptions to develop and train the proposed system. This paper establishes a system for image synthesis with excellent performance and detailed image outcomes, as evidenced by a comprehensive analysis incorporating both qualitative and quantitative measures, leading to the system under consideration achieving an impressive FID score of 34.6828 and an LPIPS score of 0.4541.
In response to the rapid advancements in facial manipulation technologies, particularly facilitated by Generative Adversarial Networks (GANs) and Stable Diffusion-based methods, this paper explores ...the critical issue of deepfake content creation. The increasing accessibility of these tools necessitates robust detection methods to curb potential misuse. In this context, this paper investigates the potential of Vision Transformers (ViTs) for effective deepfake image detection, leveraging their capacity to extract global features. Objective: The primary goal of this study is to assess the viability of ViTs in detecting multiclass deepfake images compared to traditional Convolutional Neural Network (CNN)-based models. By framing the deepfake problem as a multiclass task, this research introduces a novel approach, considering the challenges posed by Stable Diffusion and StyleGAN2. The objective is to enhance understanding and efficacy in detecting manipulated content within a multiclass context. Novelty: This research distinguishes itself by approaching the deepfake detection problem as a multiclass task, introducing new challenges associated with Stable Diffusion and StyleGAN2. The study pioneers the exploration of ViTs in this domain, emphasizing their potential to extract global features for enhanced detection accuracy. The novelty lies in addressing the evolving landscape of deepfake creation and manipulation. Results and Conclusion: Through extensive experiments, the proposed method exhibits high effectiveness, achieving impressive detection accuracy, precision, and recall, and an F1 rate of 99.90% on a multiclass-prepared dataset. The results underscore the significant potential of ViTs in contributing to a more secure digital landscape by robustly addressing the challenges posed by deepfake content, particularly in the presence of Stable Diffusion and StyleGAN2. The proposed model outperformed when compared with state-of-the-art CNN-based models, i.e., ResNet-50 and VGG-16.
The integration of distributed renewable energy with high penetration is a trend in future power and energy systems. Exploring the impacts of the retail-side pricing mechanism on renewable energy ...consumption is of great significance for sustainable generation development. Considering the diffusion process of distributed renewable energy in the power system, the authors introduce the logistic innovation diffusion model and propose a dynamic evaluation method to quantify the user-side rooftop photovoltaic diffusion capability with the retail pricing mechanism. First, a decision model with Ramsey pricing theory is improved to address the energy transactions between retailers and consumers, in which the ladder price with a two-part structure tariff model is applied. The market positive feedback model of rooftop photovoltaic installation capacity and electricity price level are analysed using a system dynamics methodology. Also, the system evolution model considering dynamic diffusion of rooftop photovoltaic affected by the tariff is established. Finally, the criterion for rooftop photovoltaic stable diffusion based on Lyapunov functions is developed and the installation capacity of rooftop photovoltaics in the studied system is determined. The case studies show the impacts of retail electricity prices on the stability and sensitivity of rooftop photovoltaic diffusion in the power system from the medium-to-long-term perspective.
Gathering data from the real world involves time-consuming aspects of web scraping, data cleaning, and labelling. Aiming to alleviate these costly tasks, this paper proposes the utilization of rapid ...stable diffusion to synthesize images efficiently from text prompts, thereby eliminating the need for manual data collection and mitigating biases and mislabelling risks. Through extensive experimentation with a small-scale vision transformer across 4 downstream classification tasks, our study includes a comprehensive comparison of models pre-trained on conventional datasets, datasets enriched with synthetic images, and entirely synthetic datasets. The outcomes underscore the remarkable efficacy of stable diffusion-synthesized images to yield consistent model generalization and accuracy. Beyond the immediate benefits of fast dataset creation, our approach represents a robust solution for bolstering the performance of computer vision models. The findings underscore the transformative potential of generative image synthesis, offering a new paradigm for advancing the capabilities of machine learning in the realm of computer vision.
This paper introduces a novel approach that combines various technologies including LoCon, ControlNet, and MLSD with Stable Diffusion to generate images featuring furniture placements while ...preserving the integrity of room layouts. The visual presentation of room on property websites significantly impacts users' room selection choices. The art of strategically arranging furniture, plants, and other items within a room to enhance its appeal is known as home staging. Beyond simply observing the room layout, users are more likely to show interest in renting a room that has undergone effective home staging. While the conventional method of enhancing room images involves image editing, it often fails to preserve room layouts. Our experimental results were capable of generating images that retain the room layout and feature furniture placements. However, achieving the desired output within a single iteration remains a challenge.
In view of the possible problems of seal design, such as slow design speed, large error rate of seal identification, weak standardization and poor user satisfaction, how to use AI technology to solve ...these problems and improve the efficiency and automation of seal design is the focus of this research. This article used web crawlers and stable diffusion to collect exquisite seal images as the data source for research, and used median filtering to denoise the collected images. After graying the image using the weighted average method, histogram equalization is used for image enhancement. This article combined artificial intelligence technology and digital certificates to construct an electronic seal, and inputs the constructed electronic seal into the stable diffusion for further processing to obtain the final designed seal. The experiment showed that when the number of seals to be designed was 1, the time required to use stable diffusion for design was 1.11 seconds. At the same time, the average score of seal evaluators designed by stable diffusion in this article was 9.5 points (out of 10 points). With the help of artificial intelligence and stable diffusion, the study on seal design can improve the efficiency and accuracy of design, show good performance and stability in handling complex design tasks, and provide an effective solution to the limitations and efficiency of traditional seal design methods. At the same time, it also enhances the innovation and diversity of design, reduces the design cost, and improves the accuracy and efficiency of automated authenticity identification.
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