Search

Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning techniques, can be time-consuming and resource-intensive. This paper presents an automated and rapid approach for estimating floodwater depth from on-site flood photos. A pre-trained large multimodal model, Generative pre-trained transformers (GPT-4) Vision, was used specifically for estimating floodwater. The input data were flood photos that contained referenced objects, such as street signs, cars, people, and buildings. Using the heights of the common objects as references, the model returned the floodwater depth as the output. Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos. Such rapid estimation is transformative in flood inundation mapping and assessing the severity of the flood in near-real time, which is essential for effective flood response strategies. [ABSTRACT FROM AUTHOR]

Setting up any agent-based model (ABM) requires not only theory to define the agents’ behavior, but also suitable methods for calibration, validation, and scenario analysis, which are highly dependent on the available data. When modelling aspects related to the COVID-19 pandemic during the pandemic itself, finding existing data and behavioral rules was rarely possible as conditions were fundamentally different from before and collecting data put people at risk. Here, we present a method to set up and calibrate an ABM using an immersive video environment (IVE). First, we collect data in this reproducible and safe setting. Based on derived behavior, we set up an ABM of pedestrians responding to one-way street signs, installed to stimulate physical distancing. Using bootstrapped regression, we integrate the IVE data into the ABM. Model experiments show that the street signs help to reduce pedestrian densities below critical distance-keeping thresholds, though only when the number of pedestrians is not too high. Our work contributes to the understanding of pedestrian movement dynamics during pandemics. In addition, the proposed data collection and calibration method using the IVE may be applied to other simulation models in which effects of interventions in the physical environment are modelled. [ABSTRACT FROM AUTHOR]

Copyright of Politologija is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many previous studies have investigated visual distance perception, especially for small to moderate distances. Few experiments, however, have evaluated the perception of large distances (e.g., 100 m or more). The studies that have been conducted have found conflicting results (diametrically opposite conclusions). In the current experiment, the functions relating actual and perceived distance were obtained for sixteen adult observers using the method of equal appearing intervals. These functions relating perceived and actual distance were obtained for outdoor viewing in a typical University environment—the experiment was conducted along a sidewalk adjacent to a typical street where campus buildings, trees, street signs, etc., were visible. The overall results indicated perceptual compression of distances in depth so that the stimulus distance intervals appeared significantly shorter than the actual (physical) distance intervals. It is important to note, however, that there were sizeable individual differences—the judgments of half of the observers were relatively accurate, whereas the judgments of the remaining half were inaccurate to varying degrees. The results of the experiment demonstrate that there is no single function that describes how human observers visually perceive large distance intervals in outdoor environments. [ABSTRACT FROM AUTHOR]

In recent years, access to digital images has been made easy, and the use and distribution of images have increased rapidly. Therefore, undesirable adversarial attacks such as FGSM and PGD can occur during the sharing or distribution of images. On the other hand, object detection, one of the widely used applications of deep learning in real life, is used to scan digital images to locate instances of every object. However, the CNN and transformer-based deep learning models trained using classical data augmentation may be unsatisfactory in obtaining the required object detection accuracy on attacked images. Therefore, to overcome the robustness problem of the deep learning-based object detection methods against attacks, the deep learning approach based on grayscale conversion and discrete wavelet transform is proposed. The proposed approach uses well-known Faster R-CNN, YOLOv5, and DETR as deep learning models. The performance of the proposed approach using grayscale and DWT-based data augmentation has been evaluated on the natural scene dataset containing street signs for object detection on attacked images. Against FGSM attack with 0.10 ϵ , f1 scores of Faster R-CNN, YOLOv5, and DETR models have increased by 0.18, 0.10, and 0.59, respectively. Also, against PGD attack with 0.10 ϵ , f1 scores of models have increased, respectively, by 0.19, 0.23, and 0.63 with the proposed technique. In addition, the performance of the proposed approach has also been evaluated on the open dataset taken from Kaggle. On the other hand, the memory size of the images processed by the models and the run times of deep learning models has decreased. [ABSTRACT FROM AUTHOR]

Copyright of Revista Universitaria de Historia Militar (RUHM) is the property of Centro de Estudios de la Guerra and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The street sign text information from natural scenes usually exists in a complex background environment and is affected by natural light and artificial light. However, most of the current text detection algorithms do not effectively reduce the influence of light and do not make full use of the relationship between high-level semantic information and contextual semantic information in the feature extraction network when extracting features from images, and they are ineffective at detecting text in complex backgrounds. To solve these problems, we first propose a multi-channel MSER (Maximally Stable Extreme Regions) method to fully consider color information in text detection, which separates the text area in the image from the complex background, effectively reducing the influence of the complex background and light on street sign text detection. We also propose an enhanced feature pyramid network text detection method, which includes a feature pyramid route enhancement (FPRE) module and a high-level feature enhancement (HLFE) module. The two modules can make full use of the network's low-level and high-level semantic information to enhance the network's effectiveness in localizing text information and detecting text with different shapes, sizes, and inclined text. Experiments showed that the F-scores obtained by the method proposed in this paper on ICDAR 2015 (International Conference on Document Analysis and Recognition 2015) dataset, ICDAR2017-MLT (International Conference on Document Analysis and Recognition 2017- Competition on Multi-lingual scene text detection) dataset, and the Natural Scene Street Signs (NSSS) dataset constructed in this study are 89.5%, 84.5%, and 73.3%, respectively, which confirmed the performance advantage of the method proposed in street sign text detection. [ABSTRACT FROM AUTHOR]

Current mobile systems are capable of efficiently acquiring dense urban point clouds. Still, operational use of such data is hampered by the lack of efficient object extraction methodology. Notably methodology is lacking for automatically extracting objects that do not belong to the road furniture like street signs and light poles but do belong to the street furniture. As an example, object we consider public garbage bins, that are installed and should be maintained in public areas in every city. However, information about types, locations, and condition of these public garbage bins are rarely updated and only obtained through manual measurements. Therefore, an efficient way of collecting information on such public objects is of interest not only for urban management but also when developing digital twins of a city. This study proposes a new method to automatically extract public garbage bins from large urban mobile laser scanning (MLS) point clouds. The proposed method consists of three main steps: (1) cell-, (2) sub-cell-, and (3) surface-based filtering, in which both spatial information of the point clouds and contextual knowledge of the public garbage bins are incorporated to efficiently remove irrelevant 3D points at an early phase and identify and classify different types of public garbage bins. Contextual knowledge includes shape and dimensions, and the relationship between the public garbage bins and the ground surface. A MLS dataset of the city centre of Rotterdam, the Netherlands, consisting of 2.84 billion points organised in 166 tiles of 50 × 75m, and covering an area of about 750 × 750m was used to test the proposed method. Results show that the method can automatically extract ~90 public garbage bins with an overall detection rate of 89.1%. Moreover, the executing time for the entire dataset was only about 163.6 minutes, which is equivalent to 3.46 seconds per one million points. Although the method was tested here one public garbage bins, it can be easily tuned for the detection of other street furniture objects, like benches, post boxes or bollards. [ABSTRACT FROM AUTHOR]

Object geo-localization from images is crucial to many applications such as land surveying, self-driving, and asset management. Current visual object geo-localization algorithms suffer from hardware limitations and impractical assumptions limiting their usability in real-world applications. Most of the current methods assume object sparsity, the presence of objects in at least two frames, and most importantly they only support a single class of objects. In this paper, we present a novel two-stage technique that detects and geo-localizes dense, multi-class objects such as traffic signs from street videos. Our algorithm is able to handle low frame rate inputs in which objects might be missing in one or more frames. We propose a detector that is not only able to detect objects in images, but also predicts a positional offset for each object relative to the camera GPS location. We also propose a novel tracker algorithm that is able to track a large number of multi-class objects. Many current geo-localization datasets require specialized hardware, suffer from idealized assumptions not representative of reality, and are often not publicly available. In this paper, we propose a public dataset called ARTSv2, which is an extension of ARTS dataset that covers a diverse set of roads in widely varying environments to ensure it is representative of real-world scenarios. Our dataset will both support future research and provide a crucial benchmark for the field. [ABSTRACT FROM AUTHOR]

The existence of local languages in Indonesia, including Balinese language is often related to marginalization issue. Some previous research revealed that Balinese language is rarely used in public spaces. The local government implements Governor Regulation Number 80 of 2018 to officially protect the language, including its existence in public spaces. This research explores the existence of Balinese language and script on the lingual street signs in Singaraja, North Bali. Further, the distribution of the lingual street signs is also investigated to reveal the government's primary and secondary target areas in language protection. The subjects of the study are 151 street signs that are placed by the department of transportation of Buleleng regency. The data are obtained through photograph taking and observation. The research reveals that the presence of Indonesian language marginalizes Balinese language. The regulation only successfully improves the use of Balinese script instead of the use of Balinese language. In terms of the sign distribution, the government focuses on signs with Balinese script at the center of the town due to the intense language contact in the area. Abstrak Keberadaan bahasa daerah di Indonesia, termasuk bahasa Bali sering dihubungkan dengan isu marginalisasi. Beberapa penelitian sebelumnya mengungkapkan bahwa bahasa Bali jarang digunakan di ruang publik. Pemerintah daerah Bali telah menerapkan Peraturan Gubernur Nomor 80 Tahun 2018 untuk secara resmi melindungi bahasa dan aksara Bali, termasuk keberadaannya di ruang publik. Sehubungan dengan itu, penelitian ini menggali keberadaan bahasa dan aksara Bali pada rambu-rambu jalan lingual di Singaraja, Bali Utara. Selanjutnya, sebaran rambu-rambu jalan lingual juga diselidiki untuk mengungkap wilayah sasaran primer dan sekunder pemerintah dalam perlindungan bahasa Bali. Subyek penelitian adalah 151 rambu jalan yang dipasang oleh Dinas Perhubungan Kabupaten Buleleng. Data diperoleh melalui pengambilan foto dan observasi. Hasil penelitian mengungkapkan bahwa bahasa Bali terpinggirkan dengan kehadiran bahasa Indonesia. Peraturan gubernur tersebut hanya berhasil meningkatkan penggunaan aksara Bali ketimbang penggunaan bahasa Bali. Dalam hal distribusi tanda, pemerintah memfokuskan penggunaan tanda dengan aksara Bali di pusat kota karena kontak bahasa yang intens terjadi di daerah tersebut.

Two aspects of real-world visual search are typically studied in parallel: category knowledge (e.g., searching for food) and visual patterns (e.g., predicting an upcoming street sign from prior street signs). Previous visual search studies have shown that prior category knowledge hinders search when targets and distractors are from the same category. Other studies have shown that task-irrelevant patterns of non-target objects can enhance search when targets appear in locations that previously contained these irrelevant patterns. Combining EEG (N2pc ERP component, a neural marker of target selection) and behavioral measures, the present study investigated how search efficiency is simultaneously affected by prior knowledge of real-world objects (food and toys) and irrelevant visual patterns (sequences of runic symbols) within the same paradigm. We did not observe behavioral differences between locating items in patterned versus random locations. However, the N2pc components emerged sooner when search items appeared in the patterned location, compared to the random location, with a stronger effect when search items were targets, as opposed to non-targets categorically related to the target. A multivariate pattern analysis revealed that neural responses during search trials in the same time window reflected where the visual patterns appeared. Our finding contributes to our understanding of how knowledge acquired prior to the search task (e.g., category knowledge) interacts with new content within the search task. [ABSTRACT FROM AUTHOR]

Line-of-sight (LOS) probability is a key component of system-level simulations that are required for creating new standards and recommendations for wireless systems. In particular, for millimeter-wave systems, an unobstructed view between the base station at an elevation and a user at ground level is important to ensure high-throughput service. While statistical models for LOS blockage by buildings exist, such models for blockage by multiple small objects, such as trees and street signs, are currently lacking. In this work, we present a Poisson point process-based analytic solution in a form of an algorithm for evaluating LOS probability in the presence of any combination of arbitrarily shaped shadowing objects, taking into account carrier frequency. The algorithm is validated against the LOS probability obtained from ray tracing in a realistic urban microcell scenario. [ABSTRACT FROM AUTHOR]

Laboratory tasks (e.g., the flanker task) reveal that incidental stimuli (e.g., distractors) can reliably trigger involuntary conscious imagery. Can such involuntary effects, involving competing representations, arise during dual-task conditions? Another concern about these laboratory tasks is whether such effects arise in highly ecologically-valid conditions. For example, do these effects arise from tasks involving dynamic stimuli (e.g., simulations of semi-automated driving experiences)? The data from our experiment suggest that the answer to our two questions is yes. Subjects were presented with video footage of the kinds of events that one would observe if one were seated in the driver's seat of a semi-automated vehicle. Before being presented with this video footage, subjects had been trained to respond to street signs according to laboratory techniques that cause stimulus-elicited involuntary imagery. After training, in the Respond condition, subjects responded to the signs; in the Suppress condition, subjects were instructed to not respond to the signs in the video footage. Subjects in the Suppress condition reported involuntary imagery on a substantive proportion of the trials. Such involuntary effects arose even under dual-task conditions (while performing the n -back task or psychomotor vigilance task). The present laboratory task has implications for semi-automated driving, because the safe interaction between driver and vehicle requires that the communicative signals from vehicle to driver be effective at activating the appropriate cognitions and behavioral inclinations. In addition, our data from the dual-task conditions provide constraints for theoretical models of cognitive resources. [ABSTRACT FROM AUTHOR]

The Old City of Jerusalem is likely the most hotly contested geographical location in the Israeli-Palestinian conflict. The linguistic landscape in the Old City, including street names and signs, can shed light on power relations and political agendas within the conflict. This article examines the linguistic landscape of the Old City after the Israeli occupation of the West Bank (including East Jerusalem) in 1967. It focuses on five different areas: four quarters (Jewish, Christian, Muslim, and Armenian) and al-Haram al-Sharif/Temple Mount compound. Based on an examination of several hundred street signs, the authors' findings indicate a clear dominance of Hebrew in signage throughout the Old City, evident in different linguistic aspects. Two linguistic behaviors were also obvious: firstly, in the Jewish quarter, the linguistic landscape promotes an Israeli nationalistic discourse including physical erasure of the Arabic language and Palestinian existence; secondly, all other areas lack national Palestinian content and aspirations. This indicates the official Israeli view that there is an exclusive Jewish right to national identity while Palestinians must make do with religious identity only. Our analysis of signs in the Old City indicates two Israeli-oriented, complementary features: pro-active Jewish-Israeli nationalization, and an Orientalist, British-inspired, colonial and religious-centered attempt to de-politicize the East. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles (IoV), numerous potential applications have come up with the use of the Internet of Things (IoT)-empowered smart devices. In IoV, vehicles, roads, street signs and traffic lights can accordingly adjust to changing conditions in order to assist drivers, and also to improve safety, ease congestion and pollution reduction. Since various entities in an IoV environment make communications over public channels, there are potential security threats. To deal with such serious threats, we design a new blockchain-assisted certificateless key agreement protocol for IoV in smart transportation context, called Block-CLAP. In Block-CLAP, through authentication key management, traffic-centric data reach to a cluster head (CH) and then to its nearby road-side unit (RSU) securely using the established secret keys. A cloud server (CS) then securely collects the information from its attached RSUs and create the transactions. Later, the transactions are formed into blocks by the CS in a Peer-to-Peer (P2P) cloud servers network, and the blocks are verified and added through voting-based consensus algorithm in the blockchain. The detailed security analysis through formal, informal and formal security verification, and comparative study show that Block-CLAP provides superior security and has low communication and computational overheads as compared with other existing competing authentication schemes in the IoV environment. Finally, the blockchain-based implementation of Block-CLAP has been performed to measure computational time needed for a varied number of transactions per block and also for a varied number of blocks mined in the blockchain. [ABSTRACT FROM AUTHOR]

Text detection in natural scenes is a current research hotspot. The Efficient and Accurate Scene Text (EAST) detector model has fast detection speed and good performance but is ineffective in detecting long text regions owing to its small receptive field. In this study, we built upon the EAST model by improving the bounding box's shrinking algorithm to make the model more accurate in predicting short edges of text regions; altering the loss function from balanced cross-entropy to Focal loss; improving the model's learning ability on hard, positive examples; and adding a feature enhancement module (FEM) to increase the receptive field of the EAST model and enhance its detection ability for long text regions. The improved EAST model achieved better detection results on both the ICDAR2015 dataset and the Street Sign Text Detection (SSTD) dataset proposed in this paper. The precision and F1 scores of the model also demonstrated advantages over other models on the ICDAR2015 dataset. A comparison of the text detection effects between the improved EAST model and the EAST model showed that the proposed FEM was more effective in increasing the EAST detector's receptive field, which indicates that it can improve the detection of long text regions. [ABSTRACT FROM AUTHOR]

This paper argues that where appropriations or invocations of the past have contributed to projects of social and political change, they have usually done so with little or no recourse to the historical past. Instead, activists and campaigners have used various forms of vernacular past-talk to unsettle those temporary fixings of ‘common sense’ that limit thinking about current political and social problems. The example of such past-talk discussed here is the work of the art-activist collective REPOhistory, which sought between 1989 and 2000 to disrupt the symbolic patterning of New York’s official and homogenized public memory culture by making visible (‘repossessing’) overlooked and repressed episodes from the city’s past. In effect, they challenged the ways in which history’s dominance of past-talk within the public sphere was constituted by exclusions of subjects on grounds of gender, ethnicity and socio-economic status. REPOhistory fused politically-engaged art practices with Walter Benjamin’s belief in the redemptive potential of dialectical encounters between past and present. To assess the value of their art-as-activism projects (“artivism”), this article will situate REPOhistory’s practices within a frame of ideas provided by Benjamin, Jacques Derrida, Ernesto Laclau, and Chantal Mouffe. In a series of street sign installations that mixed visual art, urban activism, social history, and radical pedagogy, REPOhistory exemplified why the past is too important to be trusted to professional historians.

During the 1970s and 1980s, several Latin American countries went through U.S.-backed military dictatorships. In Argentina alone the number of people who disappeared between 1976 and 1983 is estimated to be at around 30,000. In the late-1980s activist and artistic efforts to preserve, archive and make memory visible began to take shape alongside criminal prosecutions of military perpetrators of crimes against humanity. An ongoing city-wide network of memory projects in Buenos Aires continues to function alongside the pursuit of justice and human rights in the courts. In this photo essay, I explore the activist art project known as the Carteles de la Memoria, a series of 53 street signs created by the Grupo de Arte Callejero (Street Art Group). These unsettling street signs are designed to confront passersby at various points throughout Buenos Aires with active memories of dictatorship violence. Like sentries of memory, these signs now line the edge of the very river that served as a place of disappearance for thousands of people. This open-air archive joins the network of memory projects that make willful amnesia impossible. [ABSTRACT FROM AUTHOR]

Due to a combination of removal campaigns and comprehensive urban interventions over the years, the streetscape of Hong Kong has been cleared of most of its illegal façade structures, including its iconic street signs and neon billboards. As a consequence, the semiotic richness and immaterial heritage value that once reflected the dynamics of local businesses, collective memories and popular crafts has disappeared from what is now an increasingly sanitised urban landscape. Titled in reference to Jane Jacobs’ landmark work, 'The Death and Life of Great American Cities' (1961), this essay studies the rise, persecution and contemporary reappraisal of Hong Kong’s illegal façades within the city’s historical and legal context – with a special focus on the activism of the not-for-profit neon preservation group called Street Sign HK.

Road signage is an important component of traffic safety and convenient driving. It is a substantial tool placed on roads which target multiple users and give awareness about traffic situation to prevent casualties. This research aims to analyze the road signage comprehensibility of drivers with twenty different signs in Lahore City, Pakistan. Five hypothesis are made to assess comprehensibility in relation to personal characteristics of drivers (gender, marital status, age, education and driving experience). Total 400 questionnaires were filled from active driving license holders of Lahore for comprehensibility analysis. The research also incorporates interviews from officials of public sector organizations involved in managing road signs in Lahore. The findings of the study shows that, there is a strong correlation of driver comprehensibility with age, education and driving experience. The analysis depict that 14% people had high comprehensibility, 68% on medium level and 18% drivers with low road signage comprehension for the selected signs. The research claims that road sign comprehensibility should be significantly improved in drivers for effective traffic management in the city. The recommendations in this regard includes, adding section of road signage in formulation of urban plans and considering it especially for new infrastructural developments, as an element of road safety. Intergovernmental cooperation should be enhanced and awareness raising workshops must be organized for both novice and regular drivers. [ABSTRACT FROM AUTHOR]

Due to a combination of removal campaigns and comprehensive urban interventions over the years, the streetscape of Hong Kong has been cleared of most of its illegal façade structures, including its iconic street signs and neon billboards. As a consequence, the semiotic richness and immaterial heritage value that once reflected the dynamics of local businesses, collective memories and popular crafts has disappeared from what is now an increasingly sanitised urban landscape. Titled in reference to Jane Jacobs' landmark work, The Death and Life of Great American Cities (1961), this essay studies the rise, persecution and contemporary reappraisal of Hong Kong's illegal façades within the city's historical and legal context - with a special focus on the activism of the not-for-profit neon preservation group called Street Sign HK. [ABSTRACT FROM AUTHOR]

In a context of decreasing resources and of climate change, lowering road vehicles consumption is a key point to meet CO2 reduction requirements. In addition to car technological advances, eco-driving is part of the solution but the road infrastructure should ensure its development. This work aims to demonstrate that road energy demand and associated pollutant emissions can be reduced by working out minor optimization of road infrastructure itself. For this to happen, a simple eco-driving potential criterion is built upon infrastructure parameters such as slopes and sight distances. This criterion aims to detect Misplaced Speed-sectioning Positions (MSP) with regard to the Starting Point of Deceleration (SPD); with speed-sectioning being the succession of speed changes along a given route. An enhanced energy waste formulation is then developed to quantify the vehicles energy waste due to misplaced road-signs. Thirdly, a traffic simulation constitutes a framework for energy evaluation; considering a full flow of vehicles, based on real traffic data, and by modeling several driver behaviors. Simulation results show that a significant fuel reduction of up to 5.5% can be achieved locally, simply by moving a road sign, for rural areas and without degrading road safety. [ABSTRACT FROM AUTHOR]

"Induction provides a path to first principles" (Aristotle): so we approach our topic by sampling three distinct sorts of data--rules in actions as exemplified in games; rules as directives for manufacture; as laws not only for maintaining order among people but also relations between citizens and governments--finding in each case the parts that nonverbal expressions of rules play. While words are essential to formulating constitutive rules defining sporting games, they seem less important than emulation for recreational uses. They drop out in children's games of make-believe, which developmental psychology shows to be crucial to early development, since ours is a naturally rule making and following species. Industrial artifacts, thereby the modern world, depend on graphic systems, here exemplified by origami notation, which feature isolation and sequence in simultaneity, lacked by words. Such notations also exhibit a five-order pattern of intentionality, whose importance is demonstrated by communication breakdowns in road signage, undermining civic life. [ABSTRACT FROM AUTHOR]

This article brings together and analyzes two toponyms: Brazzaville Street in Holon, Israel, and Quay of Brazza in Bordeaux, France. In the light of historical and contemporary developments at different points of time, geographies and socio-political contexts, the crisscrossing between both Brazza-related toponyms is useful in promoting a critical and more nuanced interpretation of space and nomenclature. In exploring the varied flows of power, ambiguities and tensions behind the two place names, and in bringing the Congo to the fore, the article also relates to geographies beyond Europe. This aspect of spatial intertwining contributes to the de-Eurocentrization of recent place names studies, which tend to be focused on Europe/the West and on nationalistic aspects. In addition, a variety of primary and secondary materials is employed, including visual and archival sources, and fieldwork. [ABSTRACT FROM AUTHOR]

The paper analyses traffic organisation at non-signalised intersections. In such locations, the use of correct road signs is crucial. Traffic engineers should consider a balance between traffic fluency and safety. Sometimes, limited visibility necessitates the use of a 'stop' sign. The paper includes case studies for selected intersections in the Silesia Province, Poland. [ABSTRACT FROM AUTHOR]

References Flynn, Elizabeth A., & Allen, Ira J. (2020). This special issue of Across the Disciplines features the theme of Unsettling Archival Research Across the Disciplines and is composed of two parts. Here, Fukushima contends, as have Flynn and Allen, that there are different kinds of witnessing. [Extracted from the article]