Massive Data Collection Research Articles

Leveraging Full Count Census Data through Record Linkage

Academic researchers in the U.S. and Canada have partnered with genealogical organizations and statistical agencies to create massive new scientific data collections from census enumerations. These data are creating new opportunities for research across the social and health sciences. By linking individuals and families across censuses, analysts can create national longitudinal panels that trace the characteristics of individuals over their lives and families over multiple generations. IPUMS disseminates full count census enumerations for ten U.S. census years from 1850 to 1950. These full count data cover almost 800 million individual records and the IPUMS Multigenerational Longitudinal Panel (MLP) project links individuals' records across censuses. IPUMS data can be combined with data from the U.S. Census Bureau’s data linkage infrastructure to link historical records to numerous recent censuses, surveys and administrative data that measure social, economic and health outcomes. The Canadian Peoples (TCP) is a comprehensive public research database of 40 million coded and georeferenced records found in Canadian censuses from the middle of the nineteenth century until after the First World War. The data include personal, family and household characteristics of every individual enumerated in each census from 1852 to 1921. TCP investigators are exploring record linkage technology to solve challenges presented with these historical records. The workshop included one presentation from each organization, with these objectives: introduce the data collections, explain the challenges and opportunities of census linkage, describe the linking strategies, and provide an overview on how to access the data.

Confidential outsourced support vector machine learning based on well-separated structure

Support Vector Machine (SVM) has revolutionized various domains and achieved remarkable successes. This progress relies on subtle algorithms and more on large training samples. However, the massive data collection introduces security concerns. To facilitate secure integration of data efficiently for building an accurate SVM classifier, we present a non-interactive protocol for privacy-preserving SVM, named NPSVMT. Specifically, we define a new well-separated structure for computing gradients that can decouple the fusion matter between user data and model parameters, allowing data providers to outsource the collaborative learning task to the cloud. As a result, NPSVMT is capable of removing the multiple communications and eliminating the straggler’s effect (waiting for the last), thereby going beyond those developed with interactive methods, e.g., federated learning. To further decrease the data traffic, we introduce a high-efficient coding method to compress and parse training data. In addition, unlike outsourced schemes based on homomorphic encryption or secret sharing, NPSVMT exploits functional encryption to maintain the data confidentiality, achieving dropout-tolerant secure aggregation. The implementations verify that NPSVMT is faster by orders of magnitude than the existing privacy-preserving SVM schemes on benchmark datasets.

(Invited) Towards High-Performance Domain-Specific in-Sensor and in-Memory Accelerators

Internet of Things (IoT) devices are projected to attain an $1100B market by 2025, with a web of interconnection projected to comprise approximately 75+ billion IoT devices. The large number of IoTs consist of sensory systems that enable massive data collection from the environment and people. However, considerable portions of the captured sensory data are redundant and unstructured. Data conversion of such large raw data, storing in volatile memories, transmission, and computation in on-/off-chip processors, impose high energy consumption, latency, and a memory bottleneck at the edge. Therefore, high-speed, low-power, and normally-off computing domain-specific architectures should be explored and developed to overcome these issues. Motivated by the aforementioned concerns, we will be focusing on cross-layer (device/circuit/architecture/application) co-design of energy-efficient and high-performance processing-in-sensor and processing-in-memory platforms for implementing complex AI and machine learning tasks, bioinformatics tasks, graph processing, etc. We explain how to leverage innovations from circuits and architecture to integrate sensors, memory, and logic to break the existing memory and power walls.

Mixed event separation and identification based on a convolutional neural network trained with the domain transfer method for a φ-OTDR sensing system.

In phase-sensitive optical time domain reflectometer (φ-OTDR) based distributed acoustic sensing (DAS), correct identification of event types is challenging in complex environments where multiple events happen simultaneously. In this study, we have proposed a convolutional neural network (CNN) with a separation module and an identification module to simultaneously separate a mixed event into individual single-event components and identify each type of component contained in the mixed event. The domain transfer method is used in the training, fine-tuning, and testing of the proposed CNN, which saves 94% of the workload for massive DAS data collection and signal demodulation. A fine-tuning stage is added to minimize the impact of the dataset shift between the audio data and DAS data, hence enhancing the separation and identification performance. The model has good noise tolerance and achieves nearly 90% identification accuracy even at a relatively low signal-to-noise ratio (SNR). Compared with the conventional method using DAS data for training, domain transfer using a large amount of diverse audio data for training well generalizes the model to the target domain and hence provides more stable performance with only little degradation of identification accuracy.

Prediction of Human Physiological States by Using an Enhanced Recursive Feature Elimination Method

Human Action Recognition (HAR) is a vital part of the healthcare sector. Medical practitioners are experiencing difficulty in recognizing human physiological conditions due to the enormous quantity of sensory stimulation. Machine learning techniques help to predict human physiological conditions, providing medical practitioners to work more efficiently. Feature selection is an essential part of discovering new knowledge in the majority of real-world problems when there are a lot of features. Feature selection is extremely useful because it speeds up decisions and enhances classification performance. The importance of feature selection in machine learning is dimension reduction in a massive multivariate data collection. This paper presents an effective feature selection method known as the Enhanced Recursive Feature Elimination (EFRE) for selecting key features from the data set for HAR prediction. The experimental results reveal that the ERFE technique selects the most suitable features for HAR prediction. The performance of the proposed ERFE approach is tested using different performance evaluation metrics. The performance analysis shows that the ERFE method outperforms existing feature selection methods with 88% accuracy.

ADAPTATION OF THE ALGORITHM FOR DETECTING ANOMALIES IN TIME SERIES FOR NON-STATIONARY STREAMING DATA

Detection of anomalies in streaming time series data has become an important research topic due to the wide range of possible applications, including the detection of extreme weather conditions, malicious attacks on protected facilities, monitoring unauthorised gas and oil leaks, illegal pipeline connections, power cable faults, and water and other environmental pollution. Rapid detection of abnormal conditions and identifying these critical events is essential to protect lives and assets. Therefore, developing appropriate systems and detection methods is an urgent task. Anomaly detection in streaming data is challenging due to its large volume and high speed, presence of noise, and non-stationarity of the signal (or ‘concept drift’). The latter significantly complicates the identification of differences between new ‘typical’ behaviour and abnormal events. Solving this problem requires the algorithm for processing such data to learn and adapt to changing conditions. The paper proposes a modification of the algorithm for detecting anomalies in time series. This algorithm provides early detection of abnormal series in a massive collection of non-stationary streaming time series data. Anomalies are observations that are highly improbable given the previous time series values. The proposed approach is based on the primary detection of the predictive limit for the typical system behaviour using the theory of extreme values, followed by checking for the abnormality of the following series using the sliding window technique. The time series parameters are used as input data and compared by density distribution to detect any significant changes in the distribution of the characteristics. It allows the decision-making model to automatically adapt to the changing environment according to detected changes. Since anomalies are, by definition, exceptions to the typical behaviour of a system, most of the available stored data should reflect that typical behaviour of the system in question. It is unnecessary to have representative samples of all possible types of the standard behaviour of a given system for the algorithm to work well. The basic idea is to have a warm-up data set to obtain initial values for the decision model parameters. It makes it possible to determine if there is any significant difference between the last typical behaviour and the new typical behaviour. The proposed algorithm demonstrates its performance under conditions of noisy non-stationary data in several time series classes. Keywords: concept drift, extreme value theory, multivariate time series, outlier detection.

A Multimodal Fusion and Ensemble Approach for Robust Fake News Detection through Deep Learning, Reinforcement Learning, and Using Blockchain to Enhance Security and Authorization

Background: Computer science significantly influences modern culture, especially with the rapid breakthroughs and technology in social media networking. Social media platforms have become significant channels for sharing and exchanging daily news and information on many issues in the current digital environment, which is known for its massive data collection and transmission capabilities. While there are many benefits to this environment, there are also many false reports and information that deceive readers and users into believing they are receiving correct information. Objective: Nowadays, all users use social media to obtain news content, but sometimes some malicious users tamper with real news and then spread fake news, which may reduce the reputation of social media. Therefore, many existing models have been introduced to detect fake news, but these models are based on traditional machine learning algorithms, such as decision tree (DT), multilayer perceptron (MLP), random forest (RF), etc. These models Lack of performance, security, and authorization. Our proposed model can solve existing model problems using reinforcement learning and blockchain technology. objective: Nowadays, all users use social media to obtain news content, but sometimes some malicious users tamper with real news and then spread fake news, which may reduce the reputation of social media. Therefore, many existing models have been introduced to detect fake news, but these models are based on traditional machine learning algorithms, such as decision tree (DT), multilayer perceptron (MLP), random forest (RF), etc. These models Lack of performance, security, and authorization. Our proposed model can solve existing model problems using reinforcement learning and blockchain technology. Methods: In this research paper, we explain a new way to identify fake news. This new approach, combined with policy-based heuristic reinforcement learning (PHRL), where the model dynamically adjusts through iterative learning, is the key innovation and gradually improves classification accuracy. The same as our smart contract authorization method, which enhances the authenticity of content posted safely by authorized users and improves the transparency and accountability of information. Results: Our model was tested on real-time information collected from various sources with 70% accuracy and valid authentication. Conclusion: Our proposed model produced better results with a Mean Absolute Error (MAE) of 0.0811 and Root Mean Squared Error (RMSE) of 0.2847, both significantly lower values. Our proposed model performs better than multilayer perceptron (MLP), and random forest (RF).

Variance-components tests for genetic association with multiple interval-censored outcomes.

Massive genetic compendiums such as the UK Biobank have become an invaluable resource for identifying genetic variants that are associated with complex diseases. Due to the difficulties of massive data collection, a common practice of these compendiums is to collect interval-censored data. One challenge in analyzing such data is the lack of methodology available for genetic association studies with interval-censored data. Genetic effects are difficult to detect because of their rare and weak nature, and often the time-to-event outcomes are transformed to binary phenotypes for access to more powerful signal detection approaches. However transforming the data to binary outcomes can result in loss of valuable information. To alleviate such challenges, this work develops methodology to associate genetic variant sets with multiple interval-censored outcomes. Testing sets of variants such as genes or pathways is a common approach in genetic association settings to lower the multiple testing burden, aggregate small effects, and improve interpretations of results. Instead of performing inference with only a single outcome, utilizing multiple outcomes can increase statistical power by aggregating information across multiple correlated phenotypes. Simulations show that the proposed strategy can offer significant power gains over a single outcome approach. We apply the proposed test to the investigation that motivated this study, a search for the genes that perturb risks of bone fractures and falls in the UK Biobank.

La ética de la inteligencia artificial en el campo educativo.

Abstract The ethics of artificial intelligence is a dynamic for the evaluation and regulatory orientation of artificial intelligence technologies, the objective of which is to analyze the ethics of artificial intelligence in the educational field. Furthermore, artificial intelligence has taken a crucial role in research ethics, as it poses ethical challenges and dilemmas that must be carefully addressed, massive data collection and people’s privacy are topics of great relevance. At the same time, it is essential to consider algorithmic bias and automated decision-making that may affect specific groups in society. On the other hand, it is essential to guarantee the privacy and security of student data, as well as address equity in access to technology and avoid exclusion or discrimination based on algorithms. Likewise, artificial intelligence is revolutionizing education by providing tools and resources that transform the way we teach and learn. Finally, personalization of learning, virtual tutoring, automated assessment, and intelligent educational resources are just some of the applications of AI that are improving the quality and accessibility of education. However, it is important to address the ethical and social challenges associated with its implementation. Keywords: Ethics, artificial intelligence, education

Database Management Systems: An Examination using NoSQL

Solutions that can accomplish infinite scalability, high availability, and vast parallelism are needed to meet the ever-changing demands of modern data management. Achieving excellent performance standards. When managing massive collections of both organized and unorganized data sets that traditional RDBMS are unable to handle, the new breed of applications such as business intelligence, enterprise analytics, customer relationship management, document processing, social networks, Web 2.0, and cloud computing needs to scale horizontally to thousands of nodes. The pace the point at which data is produced by interactive applications used by numerous concurrent users in distributed processing involving a vast number of servers and managing Big Data applications has surpassed the capacity of relational databases, emphasizing the adoption of databases NoSQL. Reducing has been addressed by NoSQL database systems.

Prospects and challenges of sensor materials: A comprehensive review

Sensor materials have become more important in several industries as they facilitate the development of advanced sensing technologies in the modern day. These materials, specifically engineered to transform physical, chemical, or biological stimuli into quantifiable signals, play a crucial role in gathering vital data for controlling, monitoring, and making informed decisions. It is essential to comprehend the obstacles and prospects linked to sensor materials, given the escalating demand for sensor systems that are both efficient and versatile. The sensing qualities of a material refer to its capacity to detect and quantify alterations in its surroundings. These properties encompass a range of approaches, including optical, chemical, piezoelectric, capacitive, and resistive interactions. Although these materials are utilized in various industries such as transportation, medicine, agriculture, industrial operations, and environmental monitoring, persistent issues are still related to improved reaction speed, selectivity, and sensitivity. Nevertheless, the outlook for sensor materials seems optimistic, as current research and advancements are anticipated to result in materials that exhibit enhanced performance, durability, and energy economy. Developing nanomaterials and sophisticated production processes facilitates the potential for creating highly selective and sensitive sensors with new functionalities. This advancement contributes to the growth of the Internet of Things (IoT) and its applications in massive data collection, leading to enhanced automation and decision-making capabilities. This review article examines the recent advancements, applications, and research in sensor materials while also discussing the obstacles they encounter and the potential opportunities that enable them to significantly impact multiple industries and society.

Airborne ultrasound for the contactless mapping of surface thoracic vibrations during human vocalizations: A pilot study

Physical examination of the thorax is key to the clinical diagnosis of respiratory diseases. Among other examination techniques, palpation evaluates the transmission of high-frequency vibrations produced by vocalizations (tactile fremitus), which helps the physicians to identify abnormalities within the respiratory system. We propose the use of an airborne ultrasound surface motion camera (AUSMC) to quantitatively map the vibrations induced by subject vocalization. This approach could make the examination of vocal fremitus quantifiable, reproducible, and archivable. Massive data collection of vocal fremitus could allow using artificial intelligence algorithms to isolate vibration patterns that could help disease identification. Until now, in contrast, the interpretation of vocal fremitus has been subject to the physician’s experience and remains subjective. In the present work, we demonstrate the capabilities of the AUSMC to measure vocal fremitus thoracic vibration maps on 77 healthy volunteers. We have observed a spatial dependence of vibration maps on vocalization frequency. We observed that the left lung generates fewer surface vibrations than the right one, which was expected according to their respective dimensions. We also discuss the implications of our findings.

Temporal Output Discrepancy for Loss Estimation-Based Active Learning.

While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset. Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this article we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. The core of our approach is a measurement temporal output discrepancy (TOD) that estimates the sample loss by evaluating the discrepancy of outputs given by models at different optimization steps. Our theoretical investigation shows that TOD lower-bounds the accumulated sample loss thus it can be used to select informative unlabeled samples. On basis of TOD, we further develop an effective unlabeled data sampling strategy as well as an unsupervised learning criterion for active learning. Due to the simplicity of TOD, our methods are efficient, flexible, and task-agnostic. Extensive experimental results demonstrate that our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks. In addition, we show that TOD can be utilized to select the best model of potentially the highest testing accuracy from a pool of candidate models.

Structural-Functional Correlations between Unique N-terminal Region and C-terminal Conserved Motif in Short-chain cis-Prenyltransferase from Tomato.

Neryl diphosphate (C10) synthase (NDPS1), a homodimeric soluble cis-prenyltransferase from tomato, contains four disulfide bonds, including two inter-subunit S-S bonds in the N-terminal region. Mutagenesis studies demonstrated that the S-S bond formation affects not only the stability of the dimer but also the catalytic efficiency of NDPS1. Structural polymorphs in the crystal structures of NDPS1 complexed with its substrate and substrate analog were identified by employing massive data collections and hierarchical clustering analysis. Heterogeneity of the C-terminal region, including the conserved RXG motifs, was observed in addition to the polymorphs of the binding mode of the ligands. One of the RXG motifs covers the active site with an elongated random coil when the ligands are well-ordered. Conversely, the other RXG motif was located away from the active site with a helical structure. The heterogeneous C-terminal regions suggest alternating structural transitions of the RXG motifs that result in closed and open states of the active sites. Site-directed mutagenesis studies demonstrated that the conserved glycine residue cannot be replaced. We propose that the putative structural transitions of the order/disorder of N-terminal regions and the closed/open states of C-terminal regions may cooperate and be important for the catalytic mechanism of NDPS1.

Bite Force Mapping Based on Distributed Fiber Sensing Network Approach.

Bite force measurements are crucial in the realm of biomedical research, particularly in the areas of dentistry and orthodontic care. Various intraoral devices have been used to assess biting force, but each has limitations and drawbacks. Fiber optic sensors (FOSs) offer advantages such as electrical inertness, immunity to electromagnetic interference, and high sensitivity. Distributed fiber optic sensing allows an increase in the number of sensing points and can interrogate numerous reflections from scattering events within an optical fiber. We present four dental bites with heights of 6 mm, which enabled bilateral measurements. U-shaped sensors were prepared by embedding fibers into silicone by folding a single-mode fiber into four lines and multiplexing eight parallel nanoparticle-doped fibers. Dental bite models were created using two silicone materials (Sorta Clear 18 and Sorta Clear 40). The developed sensors were calibrated by applying weights up to 900 g, resulting in a linear response. Experiments were conducted to compare the efficacy of the dental bites. The collection of massive data was enabled by constructing a 2D map of the dental bites during multi-point sensing.

Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy.

Forests are critical in the terrestrial carbon cycle, and the knowledge of their response to ongoing climate change will be crucial for determining future carbon fluxes and climate trajectories. In areas with contrasting seasons, trees form discrete annual rings that can be assigned to calendar years, allowing to extract valuable information about how trees respond to the environment. The anatomical structure of wood provides highly-resolved information about the reaction and adaptation of trees to climate. Quantitative wood anatomy helps to retrieve this information by measuring wood at the cellular level using high-resolution images of wood micro-sections. However, whereas large advances have been made in identifying cellular structures, obtaining meaningful cellular information is still hampered by the correct annual tree ring delimitation on the images. This is a time-consuming task that requires experienced operators to manually delimit ring boundaries. Classic methods of automatic segmentation based on pixel values are being replaced by new approaches using neural networks which are capable of distinguishing structures, even when demarcations require a high level of expertise. Although neural networks have been used for tree ring segmentation on macroscopic images of wood, the complexity of cell patterns in stained microsections of broadleaved species requires adaptive models to accurately accomplish this task. We present an automatic tree ring boundary delineation using neural networks on stained cross-sectional microsection images from beech cores. We trained a UNETR, a combined neural network of UNET and the attention mechanisms of Visual Transformers, to automatically segment annual ring boundaries. Its accuracy was evaluated considering discrepancies with manual segmentation and the consequences of disparity for the goals of quantitative wood anatomy analyses. In most cases (91.8%), automatic segmentation matched or improved manual segmentation, and the rate of vessels assignment to annual rings was similar between the two categories, even when manual segmentation was considered better. The application of convolutional neural networks-based models outperforms human operator segmentations when confronting ring boundary delimitation using specific parameters for quantitative wood anatomy analysis. Current advances on segmentation models may reduce the cost of massive and accurate data collection for quantitative wood anatomy.

A Genetic-Algorithm-Based Dynamic Transmission of Data for Communicable Disease in IoMT Environment

Recent advancements in the field of the Internet of medical things (IoMT) have enabled the real-time monitoring and treatment of patients with communicable infectious diseases while minimizing human intervention. However, IoMT devices face challenges such as unbalanced energy consumption, memory constraints, computation power, and low latency, which can deter the efficient transfer of patient monitoring data. Thus, there is an urgent need to establish an energy-efficient infrastructure for IoMT devices to remotely monitor and collect data on communicable diseases. For this, a genetic algorithm (GA) based dynamic transmission of data for communicable diseases in the IoMT environment is proposed in this paper. The energy utilization of the IoMT is enhanced by considering the GA evolutionary processing based on the dynamic sensor range. The proposed work incorporates a periphery of the fixed area for deploying the IoMT devices to settle the energy hole problem. Multiple sinks and direct information collection concepts are also introduced which further improve the performance and reduce the movement of data packets. The proposed protocols not only optimize energy usage but also provide a robust approach for massive data collection and communication.

Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey.

The potential for selection bias in nonrepresentative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision making. We developed an approach to bias adjust county-level COVID-19 vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey. We developed a multistep regression framework to adjust for selection bias in predicted county-level vaccination coverage plateaus. Our approach included poststratification to the American Community Survey, adjusting for differences in observed covariates, and secondary normalization to an unbiased reference indicator. As a case study, we prospectively applied this framework to predict county-level long-run vaccination coverage among children ages 5 to 11 y. We evaluated our approach against an interim observed measure of 3-mo coverage for children ages 5 to 11 y and used long-term coverage estimates to monitor equity in the pace of vaccination scale up. Our predictions suggested a low ceiling on long-term national vaccination coverage (46%), detected substantial geographic heterogeneity (ranging from 11% to 91% across counties in the United States), and highlighted widespread disparities in the pace of scale up in the 3 mo following Emergency Use Authorization of COVID-19 vaccination for 5- to 11-y-olds. We relied on historical relationships between vaccination hesitancy and observed coverage, which may not capture rapid changes in the COVID-19 policy and epidemiologic landscape. Our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time scale and geographic scale necessary for proactive decision making. Designing integrated health measurement systems that combine sources with different advantages across the spectrum of timeliness, spatial resolution, and representativeness can maximize the benefits of data collection relative to costs. The COVID-19 pandemic catalyzed massive survey data collection efforts that prioritized timeliness and sample size over population representativeness.The potential for selection bias in these large-scale, low-cost, nonrepresentative data has led to questions about their utility for population health measurement.We developed a multistep regression framework to bias adjust county-level vaccination coverage predictions from the largest public health survey conducted in the United States to date: the US COVID-19 Trends and Impact Survey.Our study demonstrates the value of leveraging differing strengths of multiple data sources to generate estimates on the time scale and geographic scale necessary for proactive public health decision making.

Data ecosystems in education: opportunities and challenges

The integration of ecosystems that promote the massive use of data and the large-scale processing of this data into educational processes represent an unprecedented social challenge. This change, which we could consider paradigmatic, has been promoted by various factors which mutually magnify each other's effects.
 In recent years the massive collection and storage of data has intensified through ubiquitous technologies, which co-exist with human actors. In addition, we have witnessed an increase in the intentional processing power of this data at a speed unimaginable a few years ago (through the application of Artificial Intelligence, hereafter AI) . This has turned many aspects of our lives into data, from which value is extracted by third parties through processes of datafication. Concurrently, the public at large - including the educational sector - has been promised educational ‘personalisation’, a concept derived directly from other industrial production scenarios where AI is already in use, and every experience is datafied. In addition, a rapidly growing economic sector - the EdTech industry - has emerged with a capacity to monetise the educational sphere at a global level, extracting not only profits from its present and future actions with private or institutional clients, but also generating income based on capturing the motivation of their users and achieving significant levels of social and political influence. All of the above is magnified by the proliferation of the use of online platforms and tools in educational spaces (such as learning analytics and online exam proctoring), accelerated by the demand for online educaiton resulting from ‘lockdowns’ during the CoVid-19 pandemic.
 This special issue aims to bring together articles that problematise the challenges and unintended effects that new mechanisms and dynamics fuelled by these data-driven technologies have introduced into education. This call aims to encourage researchers and practitioners to share studies, research, debates and academically well-founded reflections that propose critical visions. This aims to provide those who are interested in education (either as teachers, academic faculty, researchers or managers ) deeper analyses, to allow us to understand the current educational and technological landscape, and to foster a revaluation of relevant educational issues that should be part of our work in the coming years.

The Resilience of Borders: Law and Migration in Contemporary Performances

Legal philosopher Hans Lindahl argues that the regulation of immigration is, above all moral considerations, a political issue. When one tries to assess the problem of territorial boundaries – and their transgression – as a question of distributive justice, political philosophers easily mix, not even surreptitiously, moral arguments with political and legal considerations. Lindahl refers to Michael Walzer, who asserts the primacy of the community and consequently bounded justice, and to Jürgen Habermas, who’s idea of boundless justice makes the notion of a nation-state irrelevant: one world polity has, by definition, no boundaries and thus no immigration issues. But Lindahl replies that law, and immigration law in particular, is forced to create boundaries by its very nature. After all, law structurally defines diverse groups of interest, and the actions of individuals – belonging or not belonging to one group or another – are always placed or misplaced, i.e. situated inside or outside the realm of the law. Even a world legislature and a universal jurisdiction would have to decide who can claim her/his rights, or who cannot. But since law is also, by definition, contingent – it can be changed in any direction – the claim of distributive justice, as a form of moral pressure, cannot be discarded easily. It shouldn’t be discarded, to be sure, it should be politicized. In recent performances about immigration, the moral indignation has clearly had the upper hand, sometimes with a touch of cynicism. In Necropolis, Arkadi Zaides creates a fictitious city of the dead, where only those who died in their attempt to reach Europe are allowed. From a massive collection of data about the victims of Fortress Europe, his performance transforms into a horrifying portrait of their ‘human remains’. Het Salomonsoordeel, a documentary, participatory monologue by Ilay den Boer involves the audience in the moral dilemmas of the ‘decider’ of the Dutch immigration and asylum agency, where den Boer worked as an intern. In The Voice of Fingers, Thomas Bellinck confronts his friendship with asylum seeker Said Reza Adib with the harsh reality of migrants as ‘data subjects’, identified by their fingerprints. The question arises of whether artistic representations of immigration issues sufficiently tackle the political challenges of global mobility – in this collapsing world of (civil) wars, climate disasters, and economical inequalities – and the challenges it poses for the affluent societies we are living in. Is it possible, or even meaningful, for theater-makers to try to relate their compassion – as a moral sentiment – to the frameworks of contingent policies and, subsequently, to the strict taxonomies of legislation?