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Thermal Cards: From Classical Chinese Poetry to Designerly Lenses on Thermal-Affective Technology
No full textA "warm" smile? A "cold" rage? Thermal and emotional experiences are often tightly connected. In recent years HCI has investigated opportunities for thermal technologies to enrich user experience. However, our vocabulary for these experiences has remained limited and inexpressive: "hot", "cold", "warm", "cool". This limits our ability to describe and conceptualise experience, and impedes the exploration of design features for thermal and affective interaction. Our pictorial offers resources to enrich language and clarify the design space for thermal and affective technologies. We expand a recently developed theoretical framework based on Chinese classical poetry to present six lenses on thermal and affective experience. The pictorial also functions as a deck of design cards with a design exemplar. Together these resources can help researchers and designers conceptualise thermal experience, explore parameters for experiential design, and design technologies for rich thermal and affective experience
Approximate Single-Linkage Clustering Using Graph-Based Indexes: MST-Based Approaches and Incremental Searchers.
Get PDFCurrent exact single-linkage clustering algorithms have asymptotically quadratic complexity. We present algorithms for approximate single-linkage clustering with empirically near-linear scalability. We explore both graph index-based incremental nearest neighbor search and an iterative exploration scheme on the graph index approximating the MST of the reachability graph similar to Kruskal. As graph index, we use both the bottom layer and a combination of all layers of an HNSW as a stand-in for connected search graphs. We provide experiments comparing the clusterings to baselines such as exact single linkage implementation and an algorithm using metric tree-based searchers. We explore the impact of the HNSW hyperparameters on the performance in terms of running time and clustering quality and evaluate the empirical asymptotic complexity
In Contamination. Disturbance Ecology and Organization Studies
No full textOur contemporary world is marked by dense interdependencies and ecological crisis. In response, organization studies has drawn on ecological thinking to reconceptualize the relationship between organization, technology, and environment, making significant contributions to our understanding of organization in socio-ecological, media-ecological, and process-ecological terms. Yet, this reconceptualization overemphasizes the importance of stabilization and balance, leaving the field ill-equipped to address the escalating precarity of our contemporary world. Therefore, we draw on disturbance ecology to develop an understanding of organization based on contamination and disturbance, rather than stabilization and balance. We argue that this approach allows organization studies to better engage with precarity as a necessary condition of existence, of being vulnerable to others rather than a lack of stability that needs balancing. We discuss the implications of such a disturbance-ecological approach for organization studies, focusing on vulnerability, resilience, and limits as our central themes
DistaLs: a Comprehensive Collection of Language Distance Measures
No full textLanguages vary along a wide variety of dimensions. In Natural Language Processing (NLP), it is useful to know how “distant” languages are from each other, so that we can inform NLP models about these differences or predict good transfer languages. Furthermore, it can inform us about how diverse language samples are. However, there are many different perspectives on how distances across languages could be measured, and previous work has predominantly focused on either intuition or a single type of distance, like genealogical or typological distance. Therefore, we propose DistaLs, a toolkit that is designed to provide users with easy access to a wide variety of language distance measures. We also propose a filtered subset, which contains less redundant and more reliable features. DistaLs is designed to be accessible for a variety of use cases, and offers a Python, CLI, and web interface. It is easily updateable, and available as a pip package. Finally, we provide a case-study in which we use DistaLs to measure correlations of distance measures with performance on four different morphosyntactic tasks
Learning to Harmonize Cross-Vendor X-ray Images by Non-linear Image Dynamics Correction
No full textIn this paper, we explore how conventional image enhancement can improve model robustness in medical image analysis. By applying commonly used normalization methods to images from various vendors and studying their influence on model generalization in transfer learning, we show that the nonlinear characteristics of domain-specific image dynamics cannot be addressed by simple linear transforms. To tackle this issue, we reformulate the image harmonization task as an exposure correction problem and propose a method termed Global Deep Curve Estimation (GDCE) to reduce domain-specific exposure mismatch. GDCE performs enhancement via a pre-defined polynomial function and is trained with a “domain discriminator”, aiming to improve model transparency in downstream tasks compared to existing black-box methods. Code available at https://github.com/YCL92/GDCE
Macro-types in multi-scale feedback systems: a conceptual framework
Get PDFLarge Complex Adaptive Systems (CAS) operate at multiple scales - processing various information flows, encoded at different granularities, over different substrates. Often, system scales are interconnected by feedback cycles, with micro-scales generating macro-scales, in turn influencing the micro-scales, and so on. E.g. foraging ants (micro) contribute to a pheromone trail (macro), which in turn guides their movement (micro) and updates the trail (macro). As these processes take widely different forms when occurring in different environments and accomplishing different functions, they may be difficult to detect, compare and transfer across CAS domains. We previously generalised such diverse processes, defining them explicitly as a Multi-Scale Feedback Systems (MSFS) design pattern. In this paper, we propose a conceptual framework for categorising macroprocesses in MSFS. This helps identify and analyse micro-macro feedback cycles across CAS domains; and engineer new CAS with specific constraints. We illustrate these concepts via CAS examples that fit the MSFS pattern via different variants. This contribution extends the theoretical basis of MSFS for dealing with the increasing complexity of modern environments
ProbTest: Unit Testing for Probabilistic Programs
No full textTesting probabilistic programs is non-trivial due to their stochastic nature. Given an input, the program may produce different outcomes depending on the underlying stochastic choices in the program. This means testing the expected outcomes of probabilistic programs requires repeated test executions unlike deterministic programs where a single execution may suffice for each test input. This raises the following question: how many times should we run a probabilistic program to effectively test it? This work proposes a novel black-box unit testing method, ProbTest, for testing the outcomes of probabilistic programs. Our method is founded on the theory surrounding a well-known combinatorial problem, the coupon collector’s problem. Using this method, developers can write unit tests as usual without extra effort while the number of required test executions is determined automatically with statistical guarantees for the results. We implement ProbTest as a plug-in for PyTest, a well-known unit testing tool for python programs. Using this plug-in, developers can write unit tests similar to any other Python program and the necessary test executions are handled automatically. We evaluate the method on case studies from the Gymnasium reinforcement learning library and a randomized data structure
Radioactive Eye Information:Guarding Eye-Image Datasets through Radioactive Watermarking for Unauthorized-Use Detection
No full textThis paper explores radioactive watermarking as a technique for embedding invisible information in eye-tracking data, ensuring that any model trained on the modified samples retains an identifiable mark. Large-scale datasets have enabled robust deep learning models for appearance-based gaze estimation, but no reliable methods currently exist to detect unauthorized use of datasets. To address this, we evaluate radioactive watermarking, which embeds a watermark into eye data using pre-trained convolutional neural networks commonly used in gaze estimation models. We assess watermark robustness through gaze classification experiments, testing multiple neural architectures in different embedding and detection setups. Results demonstrate that training with watermarked data can be detected with high confidence, depending on the proportion of watermarked samples and the training setup. Detection is reliable with at least 10% watermarked data, while exceeding 15% degrades performance without significantly improving detection. Watermarks that retain high image quality preserve network performance and enable consistent detection
Uncovering large inconsistencies between machine learning derived gridded settlement datasets
No full textHigh-resolution human settlement maps provide detailed delineations of where people live and are vital for scientific and practical purposes, such as rapid disaster response, allocation of humanitarian resources, and international development. The increased availability of high-resolution satellite imagery, combined with powerful techniques from machine learning and artificial intelligence (AI), has spurred the creation of a wealth of settlement datasets. The agreement and alignment between these datasets has not been studied in detail. We compare three settlement maps developed by Google (Open Buildings), Meta (High Resolution Population Density Maps) and Microsoft (Global Building Footprints), and uncover which factors drive mismatch. Our study focuses on 44 African countries. We build a global machine learning model to predict where datasets agree, and find that geographic and socio-economic factors considerably impact overlap. However, we also find there is great variability across countries, suggesting complex interactions between country morphology and dataset overlap. It is vital to understand the shortcomings of AI-derived settlement layers as international organizations, governments, and NGOs are already experimenting with incorporating these into programmatic work. We anticipate our work to be a starting point for more critical and detailed analyses of AI derived datasets for humanitarian, policy, and scientific purposes
University students learning to model through large amounts of data, simulations and ChatGPT
No full textDigital technologies have been studied in the context of modelling for a couple of decades, with recent discussions focusing on the potentials and pitfalls of AI tools such as ChatGPT. In this paper, we present a preliminary analysis of university students’ engagement with a modelling activity that encourages them to utilise various digital technologies to develop autonomy in applied statistics, a core field within data science, and a modelling field in itself. The activity is designed to initially draw on students’ prior knowledge before requiring the acquisition of new knowledge. We observe varied approaches among students and point to further research needed regarding how to teach students to integrate AI tools in their modelling practices