FUTURE OF THE FACE: THE PHOTOGRAPH AND THE ALGORITHM

The Documentary Photographer as Creator

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

The digital condition of photography: cameras, computers and display

Women and Photography in Africa: Creative Practices and Feminist Challenges

Seeing (surveillance) but not understanding (poor security performance) common problem in most security systems. Although in recent years significant progress has been achieved in biometric identification technology, the progress of single technologies does not dramatically improve the overall performance or solve the system-level problems of social security. Currently in addition to improvements of single technology, the following system-level technical bottlenecks must be solved to improve the overall performance of social security: 1. Blind spots of perception exist in surveillance area of due to the non-overlap of surveillance sensors, 2. The performance of single identification technology decreases sharply in complex surveillance scenarios such as poor lighting conditions or disguise, 3. Traditional warning technologies become invalid due to the multi-stage non-stationary evolution feature of complex events. Three challenges listed above closely relate to three scientific problems in the analysis technology of big visual data on three levels: sensing data, identification technology and pattern recognition. Our study aims at 1) exploiting the complete mapping mechanism between physical space and multivariate sensing spaces to fill-in the blind spots of sensing data, 2) exploring the correlative mechanism of multi-modal objects in multivariate sensing spaces to improve the analytical performance from single identification technology, 3) studying the spatial-temporal evolution mechanism in the entire lifecycle of complex events to extend pattern recognition from local space and time. The security space the physical space with comprehensive protective ability, which includes ubiquitous sensing, reliable identification, penetrating trend analysis and approaching danger warning at any time, at any location, for any object and for any behavior. Aiming to build the theory of security space, this study divided into three levels: information acquisition and perceptional computation, scene analysis and evolution prediction, resource scheduling and system applications. Then it further divided into five tasks: 1) task 1: visual object recognition and big data based identification, 2) task 2: situational awareness of groups and multi-scale revolution, 3) task 3: semantic analysis of scenes and correlative computation in multivariate spaces, 4) task 4: big scale visual retrieval and security risk analysis, 5) task 5: Warning system of the security space and its applications. Task 1 corresponds to the first level because sensing and identification of objects the basis for analysis. Task 2 and 3 correspond to the second level since group behavior, events, scene and their evolution are crucial for the prediction and warning of security events. Task 4 and 5 correspond to the third level of this study in order to develop the high performance computing platform, to conduct system evaluation and application demonstration. Big visual data contains massive high-dimensional sensing data, implying the complicated relationship among social objects. In fact, in the world of data, the spatial-temporal relationship between the big data objects more essential than the causal relationship, and these private and implicit relationships compose the core values of the big data social analysis. Only the analysis of individuals, groups and scenes in big visual data are based on the core element of social security analysis, that is social structure and social activities, can it supports the strategic transference of urban security system from investigation afterwards to warning in advance. The overall purpose of this study to build the big data analysis system in security space, realizing the intelligent big visual data system which supports data analysis of hundred billions of feature data, billions of image data, millions of visual sensing terminals. The expected achievements on the warning and protection system for large spaces can reach the international leading level. Based on the achievements above, the project plans to develop 10 intelligent big data analysis products of 3 categories, and the expected benefits of industrialization promotion can reach 100 billion, which promotes the upstream and downstream industry to realize economic benefits 3 billion. Also we strive to become the internationally leading industry in the field of big security data analysis.

Reverse engineering is a powerful tool for duplicating real objects from data acquired by tracking systems, industrial X-ray computed tomography (CT), laser scanners, and image captures. This work proposed a novel method using the three-dimensional (3D) reconstruction and 3D printing to duplicate a mechanical key based on only one single naturally-captured photograph. A reverse engineering system of the mechanical key was self-developed which includes functions of projective transformation, curve extraction, point clouds generation, and 3D geometry reconstruction. The database technology was introduced into the image-based 3D reconstruction process for the first time to store basic dimensional information of key blanks, which provided supplementary information during the 3D key reconstruction process. Five different types of duplicated keys verified the effectiveness of the proposed method. The maximum geometric deviation was less than 0.39 mm. The duplicating time cost was 5 minutes for 3D geometry reconstruction and 18.3 minutes for 3D printing.

Martha G. Anderson and Lisa Aronson's edited volume on the work of J.A. Green is a welcome contribution to the rapidly burgeoning history of African photography. Published in the wake of thematic anthologies that more broadly address the continent's photographic histories and its corresponding archives, this volume begins to redress the relative dearth of monographs dedicated to examining the particular lives and oeuvres of Africa's early photographers.1 Beyond helping to fill a lacuna in the history of photography for focused studies on the continent's practitioners, this richly illustrated publication troubles simplistic understandings of the “colonial gaze,” affords new opportunities for exploring the malleability of photographic meaning, and presents a cogent framework for engaging Africa's fragmented photographic archives.Over the course of four thematic sections, this volume's five contributors situate Green, his images, and their legacies within the social and political contexts in which they first emerged and into which they have evolved. In the book's first section, Ebiegberi Joe Alagoa eases non-Nigerian specialists into the photographer's milieu by providing a valuable historical overview of the Niger River Delta in the early colonial period. Following Alagoa's concise essay is a deft examination of his predecessors and contemporaries by Christraud Geary, whose contribution affords those unfamiliar with the history of photography and the medium's early mobilization on the continent's West and Central coasts a firm base from which to further explore Green's oeuvre.In addition to providing the reader with information pertinent to understanding Green's photographs and their circulation, Geary introduces a number of themes that run through the volume's other essays—namely, how indigenous photographers navigated the dense intersection of local and colonial life as “visual intermediaries” and how scholars have been and are contending with what she aptly refers to as the “vagaries of the African archive” (pp. 47, 38). While the latter of these two themes readily appears throughout the book in discussions of Green's diverse and scattered imagery, the former more provocatively emerges over the course of the authors' close engagements with his life and work.Building upon Alagoa and Geary's introduction to the region and its larger photographic histories, this volume's second part—“Green and His Oeuvre” by Lisa Aronson—delves more explicitly into the particulars of Green's life and profession. In the first of this section's two chapters, Aronson pairs oral histories with a close attention to what Elizabeth Edwards has called the “object biographies” of photographs in order to diligently reconstruct Green's personal history and the legacy of his photo studio.2 In tracing the lineage of his business through two generations, she relates the ways in which his successors (Gobo Ada Green and James Adagogo Green) modified his studio to accommodate both the medium's rapid technological advances and subsequent changes in local demands. Aronson's detailed examination of these men's photographic practices—her discussion of the different cameras, processes, and techniques they used—is valuable to a field that too often disregards the materiality of photographs and instead focuses on the images embedded in their substrates. This is not to say, however, that Aronson does not insightfully attend to the complexities of Green's images. Indeed, in chapter 4, her second contribution to this section, Aronson analyzes the semantics of Green's portraiture and the social and political meanings his sitters' backdrops, attire, and poses suggest. Ultimately, her discussion of how Green's expatriate and Ijo clientele adopted and adapted Western conventions of portraiture in the context of the Niger Delta serves as an important contribution to recent scholarship on what John Peffer has astutely referred to as the genre's “creolization” (p. 121).Moving beyond a consideration of portraiture's local idioms, this volume's third section (“Viewing Green through Expatriate Eyes”) comprises two essays by Martha G. Anderson that expand our purview of Green's photographic practice. Accessible and smartly written, her contributions contain some of this publication's most compelling theoretical insights. In “Differing Views: Imperial Agendas and Personal Histories,” Anderson examines photographs that Green made of colonialism's physical and social structures. Referring to an impressive array of archival sources, she explores expatriate experiences in the Niger Delta—and the roles photography played within these—with a nuance that effectively challenges notions of a univocal “colonial gaze.”In this section's second chapter, “Envisioning Africa: From Ethnographic Types to Picturesque Views,” Anderson reconsiders characterizations of the ethnographic from within the context of Green's production. Over the course of her essay, she addresses a wide range of Green's imagery—“type” photographs that he made of women participating in coming-of-age ceremonies, portraits that he made of “native chiefs,” images of African material culture, etc.—and examines how these pictures were variously mobilized by those who commissioned them. In revisiting earlier readings of some of Green's photographs before his African identity became known, she provocatively reveals the extent to which a photographer's subjectivity effects our framing, and subsequent understanding, of their work's content, aesthetic, and politics.Anderson's consideration of the photographs Green made to commemorate and celebrate empire and her discussion of the ethnographic images he produced for his European clientele in this section bring to the fore one of this volume's most pressing questions—namely, how we might make sense of Green's ambiguous relationship with the Niger Delta's expatriate communities and colonialism more broadly. In lieu of crafting a narrative that positions Green as either entirely aligned with or against the colonial regime, Anderson and her colleagues deftly, though perhaps at times too prudently, examine his position through the various articulations of his diffuse archive.Because no texts written by Green himself are known to exist, it is impossible to know the exact intentions that lay behind his image making or what his thoughts may have been regarding how his pictures were used in the process of empire building. Although the contributors to this volume ultimately suggest that Green's acceptance of commissions from colonial officials and participation in imperial events should not be seen as indicative of his support for colonial rule, these aspects of his business certainly complicate how we understand his oeuvre. In probing the ambivalences of Green's archive, the authors lead their readers to contend with larger questions specific to the history of photography, such as what factors underlie, contribute to, or lend an image its politics. Though the valences of the medium's politics have been grappled with by photo historians (Benjamin, Sekula, Solomon-Gadeau, Sontag, etc.) for decades, as the authors of this publication show with respect to Green's oeuvre and Africa's photographic histories more broadly, the issue remains ripe for interrogation.Comprising three essays, this volume's fourth and final section (“The Performative Aspect of Green's Photographs”) considers how Green's photographs have circulated throughout regional and global visual economies. In her contribution, Anderson provocatively explores the instability of photographic meaning by examining how Green's pictures were used by expatriates in their photo albums and variously mobilized in Western publications. Through numerous examples, she shows how Green's images became anchored in certain contexts (photo albums, newspapers, books, etc.) that opened them up to a wide variety of (often racialized) fictions. In “Green's Photos and the Visualizing and Reinventing of Ijo Histories,” Aronson offers an engaging analysis of how his images have been translated across time, space, and media (including painting and sculpture). In particular, she discusses how his pictures have been used throughout the Niger Delta to both reconstruct and commemorate familial and political histories and affirm important social and political networks. The book's concluding chapter is written by Tam Fiofori, who approaches Green's oeuvre from the perspective of a contemporary photographer and practitioner. Tracing a general history of photography in Nigeria from J.A. Green and Solomon Osagie Alonge through J.D. Okhai Ojeikere to the contemporary moment, Fiofori asserts the importance of Green's legacy and potential place in the history of modern Nigerian art.In addition to critically chronicling Green's life and work, this volume's five contributors examine the complex and somewhat contentious position he occupied in the Niger Delta during the colonial period. Through multifaceted considerations of Green's relationship vis-à-vis British officials and colonialism more broadly, the authors collectively reframe and, as the book's title suggests, compellingly reimagine conceptions of the indigenous and colonial. Apart from bringing to light one of Africa's underexposed photographers, this much-needed volume offers profoundly generative theoretical frameworks for considering the roles photography has played both on and off the continent in the colonial period and beyond.

As industrial 4.0 advances such as Internet of Things (IoT), Big Data, Cloud Computing , and Artificial Intelligent (AI) has prompted reseachers to innovate in various fields, including security systems. The security system is an important issue due to rise of theft in a residence. A security system is needed for home authorizon to prevent the crime of theft. Security systems are built using facial recognition. The research proposes to develop a security system using facial recognition based Raspberry Pi with Python programming and utilize the OpenCV. System testing includes training function testing, facial recognition function, image delevery function, decision-making function, and system performance testing, system performance for facial recognition is calculated using confusion matrix formula that produces 100% sensitivity, 13% specitificity, and 97% accuracy.

Emerging Marketing Research on Healthcare and Medical Decision Making: Toward a Consumer-Centric and Pluralistic Methodological Perspective

This article describes and evaluates the curricular design and learning outcomes of History and Practice of Photography. This is an undergraduate course that integrates creative and theoretical components, which are traditionally taught separately, and often in different departments, in the study of the history of photography. Through interrogating contemporary visual culture with their eyes, words and cameras, students simultaneously study history and theory, examine divergent representational strategies through photographic assignments and deploy digital technologies as crucial tools for effecting personal, cultural and socio-political change. As such, the course offers a pedagogical shift away from the conventional delivery of art history surveys, in which lectures, examinations and research papers tend to predominate. This integrated approach sets out to create a multifaceted learning experience that fosters students' visual literacy, critical thinking, creative exploration and technological proficiency, all in the context of one class. The author proposes this curricular design as a template that could be extrapolated from, and applied to, other areas of visual studies in higher education.

Computational photography refers broadly to imaging techniques that enhance or extend the capabilities of digital photography. This new and rapidly developing research field has evolved from computer vision, image processing, computer graphics and applied opticsand numerous commercial products capitalizing on its principles have already appeared in diverse market applications, due to the gradual migration of computational algorithms from computers to imaging devices and software. Computational Photography: Methods and Applications provides a strong, fundamental understanding of theory and methods, and a foundation upon which to build solutions for many of today's most interesting and challenging computational imaging problems. Elucidating cutting-edge advances and applications in digital imaging, camera image processing, and computational photography, with a focus on related research challenges, this book: Describes single capture image fusion technology for consumer digital cameras Discusses the steps in a camera image processing pipeline, such as visual data compression, color correction and enhancement, denoising, demosaicking, super-resolution reconstruction, deblurring, and high dynamic range imaging Covers shadow detection for surveillance applications, camera-driven document rectification, bilateral filtering and its applications, and painterly rendering of digital images Presents machine-learning methods for automatic image colorization and digital face beautification Explores light field acquisition and processing, space-time light field rendering, and dynamic view synthesis with an array of cameras Because of the urgent challenges associated with emerging digital camera applications, image processing methods for computational photography are of paramount importance to research and development in the imaging community. Presenting the work of leading experts, and edited by a renowned authority in digital color imaging and camera image processing, this book considers the rapid developments in this area and addresses very particular research and application problems. It is ideal as a stand-alone professional reference for design and implementation of digital image and video processing tasks, and it can also be used to support graduate courses in computer vision, digital imaging, visual data processing, and computer graphics, among others.

Michael Halliday and Christian Matthiessen’s term ‘semogenesis’ refers to how meaning potentials are created through processes on many co-occurring time frames, most prominently those referred to as ‘phylogenesis’, ‘ontogenesis’ and ‘logogenesis’. The concept was originally infused with linguistic concern in an attempt to link a Systemic Functional Linguistics (SFL) account of the lexico-grammatical and semantic strata with lived experience. In this article, the authors rethink the concept in order to (1) broaden its scope to the concerns of multimodal studies, and (2) accommodate how digital technology impacts on our communication practices. They do so by discussing semogenesis from a vantage point of ‘ecosocial semiotics’, a perspective that asks us to blend both sociological, technological, material and biological understandings of human activity. Taking digital photography as an example, the authors argue that digital media afford an acceleration of processes of multimodal semogenesis on all semogenetic time frames. Picking up the notion of ‘microgenesis’, a fourth, faster-than-logogenesis time frame that serves as a placeholder for any process enabling logogenesis, they suggest that this acceleration is driven by the global-scale introduction of digital technology. Through a discussion of select examples from the history of photography, specifically contrasting nascent photographic practice with contemporary photography, they propose that the development from camera to digital camera and the subsequent consolidation in recent decades of digital cameras into smartphones has had a profound impact, not only on practices of photography, but also on the processes of meaning making with photographic material.

Historical photographs are proved to be very useful to provide a dimensional and geometrical analysis of buildings as well as to generate 3D reconstruction of the whole structure. The paper addresses the problem of single historical photograph analysis and modelling of an architectural object from it. Especially, it focuses on reconstruction of the original look of New-Town synagogue from the single historic photograph, when camera calibration is completely unknown. Due to the fact that the photograph faithfully followed the geometric rules of perspective, it was possible to develop and apply the method to obtain a correct 3D reconstruction of the building. The modelling process consisted of a series of familiar steps: feature extraction, determination of base elements of perspective, dimensional analyses and 3D reconstruction. Simple formulas were proposed in order to estimate location of characteristic points of the building in 3D Cartesian system of axes on the base of their location in 2D Cartesian system of axes. The reconstruction process proceeded well, although slight corrections were necessary. It was possible to reconstruct the shape of the building in general, and two of its facades in detail. The reconstruction of the other two facades requires some additional information or the additional picture. The success of the presented reconstruction method depends on the geometrical content of the photograph as well as quality of the picture, which ensures the legibility of building edges. The presented method of reconstruction is a combination of the descriptive method of reconstruction and computer aid; therefore, it seems to be universal. It can prove useful for single-image-based modelling architecture.

Optimization of face recognition algorithm based on deep learning multi feature fusion driven by big data

The Against Erasure project, delivered a 3D digital model reconstruction of Manus Island Offshore Processing Centre, for use in teaching and learning, via a multidisciplinary Canvas Teaching Resource in the humanities and social sciences. The digital model is a "Simulation" of an historical site, supported by Seekbeak activities, reflecting learner engagement and learning environments that support innovative teaching and learning The 3D digital reconstruction preserves an historical record of the detention centre on Manus Island, where thousands of refugees were imprisoned under Australia’s harsh offshore processing regime. After its 2017 closure to comply with PNG laws, the centre was dismantled. Today, the jungle has overtaken the site, as if it had never existed. Yet several men died there, due to homicide, self-harm, suicide, or untreated medical conditions. Preserving a record of the site was important. (Loughnan et al, 2021; Giannacopoulos and Loughnan, 2020).The simulation is resonant of the work of Forensic Architecture, which adopts an archeological approach to the digital representation of sites of state-sponsored violence, providing ‘forensic’ evidence of human rights violations. The digitally reconstructured site delivers rich insights into the impacts of systemic injustice, through concepts like ‘performation’, showing how digital technology can enhance memory through the (re)creation of place (Mandelossi, 2021). (re) Through ‘performation’, place is ’actualised in the digital sphere. However its representation is also the effect of those engaged in its re-creation. That is, these digital sites are ‘never just an imitation or a reproduction of the physical place to which they refer. Rather, the physical place is staged through an interaction with the virtual place.' The Against Erasure simulation, shows that 'we do things with places, as the work of Forensic Architecture (discussed below) convincingly shows. The digital does something with place and can enhance it by adding layers of meaning allowed by the specific affordances of digital media.This project is a significant example of how learning technologies can advance research, of current research being used in teaching, and the nexus between research and teaching for restorative justice purposes. The Against Erasure project presents a 3D digital reconstruction of the Manus Island Offshore Processing Centre, developed as a multidisciplinary teaching resource for humanities and social sciences. The model preserves an historical record of the now-dismantled detention centre, where thousands of refugees were imprisoned under Australia's offshore processing regime. Despite limited access to official documents, the reconstruction was created using archival materials, interviews, Google Maps, film footage, and audio recordings. The accompanying Canvas community site demonstrates research-led teaching, utilising H5P, Seekbeak, and other learning technologies to contextualise the model across disciplines and delivery modes. Students engage in co-constructing interactive hotspots, becoming active contributors to the simulated site.In 2024 and 2025, students applied methods described above for a 3rd year Arts subject: Digital Humanities and the Social Sciences. It was clear that the simulation functioned as a provocation, with student feedback indicated that this simulation was quite distinct from other simulations: they weren’t just working with a simulator in an abstract way. Rather, they were engaged in contributing human dimensions to the simulation, through Seekbeak activities, becoming active participants in countering historical erasure. The model prompted students to consider the value of simulation, not just as a digital tool, but as a technique of memorialisation, honouring past suffering, and institutional violence, especially when the physical site has disappeared, and there is no concrete reminder of that history. This is an example of a ‘digital’ site of conscience. Students were also co-creators of the model, as noted above (methods), in which their engagement also reflected the idea of performation as an effect of such digital simulations. Although simulation techniques are common in the sciences, it is relatively rare to see them used in the humanities and social sciences. This is the only known 3D model of the detention centre, making it a significant contribution to collective knowledge about the facility and its location. It functions as a historical reminder of the suffering of those imprisoned there as especially, in the face of the Australia’s refusal to admit to its complicity in this violence. Students are asked to consider the value of simulating the former prison in 3D, including what is enabled by the 3D graphics, the critical implications of 3D modelling for scholarship in Criminology and Memory Studies and how the critical perspectives Criminology and Memory Studies have transformed the use of 3D modelling. This project is a significant example of how learning technologies can advance research, of current research being used in teaching, and the nexus between research and teaching for restorative justice purposes.