Image-text-image transmission for accident scene communication: A generative AI approach

RESEARCH QUESTIONS 1. Impact of effective question design on model output accuracy ⚫ How do we design multi-angle questions (e.g., from the perspectives of police, insurance, and news) to ensure that linear models can provide accurate and detailed answers about accident scenes? ⚫ How do different types of question design (e.g., concise vs. complex) affect the quality of generated accident scene descriptions? 2. Selection and deployment of image-to-text and text-to-image models ⚫ In real-time accident communication systems, how do we select and evaluate image-totext and text-to-image models based on criteria such as accuracy, speed, and resource usage? ⚫ How can we improve the real-time accuracy of accident scene analysis by deploying lightweight generative models in edge devices and vehicle cloud systems? 3. Comparison of LLaVA and PixArt-Σ models in accident scene recovery ⚫ How do the text descriptions generated by LLaVA compare with the image recovery effects of the PixArt-Σ model in terms of accuracy, detail richness, and practicality, especially in accident scene understanding? ⚫ How does the quality of the descriptions generated by LLaVA affect the clarity of image recovery and the decision-making effect of drivers in high-speed scenarios? 4. The impact of multi-angle information fusion on model output ⚫ How can we improve the accuracy and richness of the generated accident descriptions by combining multi-angle data (such as news, police reports, and insurance perspectives)? ⚫ How can we optimize the description generation ability of the model in multi-angle information fusion of accident scenes to ensure that the output meets the needs of different users? 5. Impact of network conditions on transmission delay of accident images and text descriptions ⚫ How do different network conditions (e.g., bandwidth, latency) affect the transmission delay of accident images and text descriptions in a high-speed driving environment? ABSTRACT The main purpose of this study was to address the challenge of reducing the information transmission delay in accident scenarios during high-speed driving. To achieve this, the study proposes a method for converting accident images into text descriptions for transmission and then re-generating images based on those descriptions. This image-text-image approach optimizes bandwidth usage and reduces emergency response times. By comparing the transmission speeds of text and image data uploaded to the server under simulated similar network conditions, the study shows that text descriptions are significantly better than images in terms of speed and resource efficiency. In addition, the study combines different perspectives - news, insurance reports, and police accident analysis - to enrich the model's understanding of the accident scene and designs and compares the limitations on generating images from different perspectives to help develop an accurate and comprehensive description of the restored scene. The experiments mainly used generative AI models, such as LLaVA and PixArt Sigma, to test the feasibility and quality of information restoration from text to images. The results show that despite some limitations, such as model constraints and input truncation, the quality of images generated based on short questions and descriptions is very similar visually. The proposed method is feasible for improving accident response and communication in bandwidthconstrained environments, highlighting the potential of generative AI in enhancing road safety systems

Pineapple fields forever. [Decolonising the Filipino identity through screenwriting]

Bilingual playscript in English and Tagalog (Filipino

Challenges to achieving sustainability in social housing construction in New Zealand

The housing crisis resulting in a lack of affordable housing has become an urgent problem that New Zealand faces due to various socio-economic and historical-political reasons. Social housing is one of the ways to address this problem and currently the country is experiencing a significant increase in the need for affordable and quality social housing. Given the growing need for social housing, it is vital to understand what actions need to be taken and by whom to balance the various aspects of sustainability of the social housing sector. This is more important as housing providers face various interrelated challenges, including financial, political, social, and institutional factors that significantly influence their activities. This paper aimed to identify the factors influencing the achievement of sustainability and, where possible, make recommendations to the management of housing providers and the government on how to overcome the emerging challenges. The results of this study reflected the practical experience of housing providers and showed that achieving true sustainability in this sector is possible only with a radical transformation of the existing relations. The recommendations developed in this paper propose that the state take on the function of a social customer, determining the need, parameters, and characteristics of social housing, while housing providers are proposed to become exclusively project operators, ensuring their economic efficiency. This study should become an occasion to think about the role and importance of social housing for society and encourage the government to change the established practice of manipulating housing policy to please political ambitions, putting social and environmental aspects at the forefront, i.e., to do what any government must do

Sweet and sour: How can architecture be used as a medium for Girmitiya descendants to reconnect with their heritage?

RESEARCH QUESTION How can architecture be used as a medium for Girmitiya descendants to reconnect with their heritage? ABSTRACT Within the Girmitiya diaspora, there is a widespread disconnect with cultural identity, particularly amongst the younger generation. This disconnect has been further exacerbated by political instability in Fiji over the past 40 years which targeted Indo- Fijians, and which resulted in additional migration from Fiji to Aotearoa and abroad. This wave of migration has created a generational gap, particularly among the younger Indo-Fijians raised either in Fiji or abroad, who are increasingly detached from their cultural and intangible heritage. To address this situation, a research question was proposed: ‘How can architecture be used as a medium for Girmitiya descendants to reconnect with their heritage?’ The research examines the underlying problem affecting the community and how the impacts of colonial rule have shaped contemporary society and led to a sense of displacement. The theoretical realm of UNESCO’s Convention on Intangible Cultural Heritage has been explored with regard to the legacy of the Girmitiya experience. Equally, the work of respected theorists like Juhani Pallasmaa, Edwards Relph and Wilbert Gesler was investigated. Pallasmaa’s theories on sensory experiences further question why humans have become heavily reliant on a single sense – sight, whereas other senses are ignored, diminishing our perception of space. Relph’s theory of place and placelessness highlights the weakening of the identity to a place and how placelessness has gone through further transformation fuelled by displacement, colonialism, migration, policies and practices. Gesler, on the other hand, explains the notion of healing and its multidimensional nature, which needs to be factored in when dealing with confronting and traumatic past experiences. The research further analyses precedents such as the Melbourne Holocaust Museum by Kerstin Thompson Architects, Hyde Park Barracks by Francis Greenway, Jewish Museum by Daniel Libeskind, and Te Taumata o Kupe by Toa Architects. The precedents reflect the notion of a difficult past, displacement, preservation of a bygone era and how the architects have addressed the situation in a holistic manner. These are some of the key themes the Girmit diaspora faces. The resulting design is for a museum and cultural centre, a sanctuary for Girmitiya descendants located on the Chelsea Sugar Refinery site in Tāmaki Makaurau. The architecture tells the sweet and sour history of sugar production in the Pacific and provides a place where Girmitiya descendants can investigate their heritage, find a sense of belonging and celebrate their unique connection with Fiji and Aotearoa, New Zealand

In this paper...

This paper is a collection of responses from computer scientists to the silencing of science. Sustainability-driven computing research-encompassing equity, diversity, climate change, and social justice-is increasingly dismissed as 'woke' or even dangerous in many sociopolitical contexts. As misinformation, ideological polarisation, deliberate ignorance and reactionary narratives gain ground, how can sustainability research in computing continue to exist and make an impact? This paper explores these tensions through Fictomorphosis, a creative story retelling method that reframes contested topics through different genres and perspectives. By engaging computing researchers in structured narrative transformations, we investigate how sustainability-oriented computing research is perceived, contested, and can adapt in a post-truth world

Speech emotion recognition using transfer learning methods

RESEARCH QUESTIONS •What are the current limitations of the existing speech-emotion recognition models? • How data augmentation methods can be used to generate synthetic data that could improve the performance of SER systems? • How to use the Transfer learning methods with a fine-tuning process to utilize its benefits? ABSTRACT Identifying human emotions using machine learning techniques in this technological advancement era is more popular, and it offers practical contributions to different fields such as health and medicine. Identifying human emotions can be made possible by analyzing their facial expressions, body gestures, and speech. With the advancement of automatic speech recognition, emotion recognition using speech is more effective and accurate than other methods. Most existing systems trained with insufficient amounts of data face the issue of performing effectively when confronted with unseen data. These systems mostly used traditional machine learning techniques. This thesis focuses on identifying speech emotions using transfer learning, which uses pre-trained models with large-scale data. Therefore, due to the lessening of training duration and the computational cost, the efficiency of the process of speech emotion recognition will increase. Combining several datasets and using augmented data can help the existing limitation of lack of data for training

Decolonised Professional Framework of Practice for Computing

Decolonised Professional Framework of Practice for Computing to support Story Re-telling: A decolonial approach to sustainable ICT. ICT4S Conference June 2025. Sustainable ICT is an emerging field that integrates ethical, environmental, and technical perspec- tives, yet there is little guidance on the role of computing professionals in advancing sustainable practice. Despite the increasing focus on sustainability in computing, there is no established framework to define the responsibilities of ICT professionals in this space. Existing research in ICT for Sustainability (ICT4S) has explored the environmental impacts of computing and the role of technology in supporting sustainability goals, but it has not adequately addressed how ICT professionals should navigate their ethical and professional responsibilities in this evolving field. This research introduces a story re-telling methodology, drawing from Indigenous and decolonial storytelling traditions, to bridge the gap between sustainability principles and professional practice in ICT. Using an action-based, iterative process, we used storytelling cycles to critically reflect on their roles, responsibilities, and ethical decision-making in sustainable computing, capturing insights through qualitative narratives, and articulating a decolonised professional framework of practice for ICT professionals. This approach provides a decolonial framework that clarifies the role of computing professionals in sustainable ICT, offering practical guidance for future practitioners and contributing to a more inclusive and ethically grounded professional practice

The fake beast: A post-anthropocentric representation of wildlife in picture book illustration

This creative project explores illustration practices in children's storytelling, focusing on the visual representation of wildlife through a post-anthropocentric lens. My aim is to foster empathy and connections with nature by portraying animals authentically, free from anthropomorphic attributes in storybook form. I believe the way the animal kingdom has historically been introduced to young readers in many children's stories has reinforced a hierachical human perspective toward wildlife. Through the creation of a wordless illustrated storybook, I aim to present the natural world in a way inspired by post-anthropocentric theorists and their notions of co-existence and reciprocity in the human-nature relationship. Told entirely through illustrations, there is no text included in the picture book. The images are the sole form of communication. The absence of text was a critical decision to convey the narrative through tone, rhythm and feeling. This approach gives readers the space to interpret and reconstruct the story in their own way, engaging with the images to fill in the gaps and reimagine their own narrative. The story I am illustrating was written by Vladimir Arseniev, a renowned Russian geographer and naturalist whose passion was to study and explore the diversity of life and the land around them. It recounts an expedition in the forest of my home region, Primirskiy kray, where, at the time the story was written, indigenous world views and the influence of the Industrial Revolution were colliding. The methodology is a practice-led exploration of visual storytelling strategies, aiming to address the following questions: how might illustration portray the world of wildlife in the context of a post-anthropocentric view point, how do I develop an engaging illustrative language for a young audience in this context, and how might a story be told through images only. The creative practice-led research journey explores illustration strategies and techniques through experimentation with content and media

Genre prompts for professional practice creative non-fiction

This file is the data behind this paper which is accepted for publication: Guruge, D., Mann, S., Myers, R., Bates, O., Goldweber, M., Williamson, A., Lasenby, J., & Brooks, I. (2025). Surviving the narrative collapse: Sustainability and justice in computing within limits. LIMITS ’25, June 26 –27

Apple leaf disease detection: A comprehensive analysis of pre-trained models and platform development

Apples are one of the most popular and valuable fruits in the world, but they are also susceptible to many diseases affecting their leaves, for which it is important to identify and control them at an early stage. Conventional methods of disease classification are restrictive in the way that they are tedious, time-consuming, and require the expertise of professionals. This study explores the feasibility of applying deep learning models for apple leaf disease classification and how transfer learning can enhance the model accuracy across different datasets. It is important to determine how various architectures deal with real-world issues such as lighting variations, shadows, similar-looking disease symptoms, and intraclass variability. For this purpose, different pre-trained Convolutional Neural Networks (CNNs), including InceptionV3, ResNet50V2, Xception, MobileNetV2, and InceptionResNetV2 were evaluated. These models were trained and then tested on various datasets with a set of different characteristics in terms of class imbalances, image quality, and augmentation techniques. Accuracy, precision, recall, and F1-score were used to measure the performance of the models and the study explored how the models perform with data augmentation, environmental noise, and class imbalances. Some models were efficient in distinguishing between different diseases to a certain level of detail, while others were more stable with respect to augmentation-induced distortions. In addition, the research explores the practical implications through the development of an apple leaf disease detection platform. The platform enables users to upload an image and then this image can be processed to achieve an automated classification result. The platform integrates outputs from multiple models using a composite scoring approach to improve the reliability of predictions. This application shows how deep learning can be effectively applied in farming, plant disease control, and other areas of agricultural production. The study contributes to precision agriculture by providing insights into model robustness, dataset diversity, and deep learning-based disease classification. This research also reveals the strengths and weaknesses of different CNN architectures. The research demonstrates the effectiveness of using advanced deep-learning approaches for identifying apple diseases thus improving the scalability of disease management in agriculture