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AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games
No full textCheating in online video games compromises the integrity of gaming experiences. Anti-cheat systems, such as VAC (Valve Anti-Cheat), face significant challenges in keeping pace with evolving cheating methods without imposing invasive measures on users' systems. This paper presents AntiCheatPT_256, a transformer-based machine learning model designed to detect cheating behaviour in Counter-Strike 2 using gameplay data. To support this, we introduce and publicly release CS2CD: A labelled dataset of 795 matches. Using this dataset, 90,707 context windows were created and subsequently augmented to address class imbalance. The transformer model, trained on these windows, achieved an accuracy of 89.17% and an AUC of 93.36% on an unaugmented test set. This approach emphasizes reproducibility and real-world applicability, offering a robust baseline for future research in data-driven cheat detection
From Smør-re-brød to Subwords:Training LLMs on Danish, One Morpheme at a Time
No full textThe best performing transformer-based language models use subword tokenization techniques, such as Byte-Pair-Encoding (BPE). However, these approaches often overlook linguistic principles, such as morphological segmentation, which we believe is fundamental for understanding language-specific word structure. In this study, we leverage an annotated Danish morphological dataset to train a semisupervised model for morphological segmentation, enabling the development of tokenizers optimized for Danish morphology. We evaluate four distinct tokenizers, including two custom morphological tokenizers, by analyzing their performance in morphologically segmenting Danish words. Additionally, we train two generative transformer models, \textit{CerebrasGPT-111M} and \textit{LLaMA-3.2 1B}, using these tokenizers and evaluate their downstream performance. Our findings reveal that our custom-developed tokenizers substantially enhance morphological segmentation, achieving an F1 score of 58.84, compared to 39.28 achieved by a Danish BPE tokenizer. In downstream tasks, models trained with our morphological tokenizers outperform those using BPE tokenizers across different evaluation metrics. These results highlight that incorporating Danish morphological segmentation strategies into tokenizers leads to improved performance in generative transformer models on Danish languag
Faster, Deterministic and Space Efficient Subtrajectory Clustering
No full textGiven a trajectory T and a distance Δ, we wish to find a set C of curves of complexity at most , such that we can cover T with subcurves that each are within Fréchet distance Δ to at least one curve in C. We call C an (,Δ)-clustering and aim to find an (,Δ)-clustering of minimum cardinality. This problem variant was introduced by Akitaya et al. (2021) and shown to be NP-complete. The main focus has therefore been on bicriteria approximation algorithms, allowing for the clustering to be an (, Θ(Δ))-clustering of roughly optimal size.We present algorithms that construct (,4Δ)-clusterings of (k log n) size, where k is the size of the optimal (, Δ)-clustering. We use (n³) space and (k n³ log⁴ n) time. Our algorithms significantly improve upon the clustering quality (improving the approximation factor in Δ) and size (whenever ∈ Ω(log n / log k)). We offer deterministic running times improving known expected bounds by a factor near-linear in . Additionally, we match the space usage of prior work, and improve it substantially, by a factor super-linear in n, when compared to deterministic results
Using Psychophysiological Insights to Evaluate the Impact of Loot Boxes on Arousal
No full textThis study investigates the psychophysiological effects of loot box interactions in video games and their potential similarities to gambling-related behaviours. Using electrodermal activity (EDA) measurements, the research examines player arousal during loot box interactions and explores whether individuals with higher Internet Gaming Disorder (IGD) severity exhibit reduced sensitivity to these random reward mechanisms. The study employs a custom-designed game, ``A Minute Outside,'' to control experimental conditions and standardise loot box interactions. Participants' IGD severity is assessed using the Internet Gaming Disorder Scale – Short Form (IGDS9-SF), while arousal is measured through EDA, analysing skin conductance responses (SCRs), area under the curve (AUC), and skin conductance level (SCL). By leveraging psychophysiological methods commonly used in gambling research, this investigation aims to provide objective evidence on whether loot boxes function as gambling-like mechanisms at a physiological level. The study contributes to the ongoing debate surrounding gaming disorder and loot box harms, offering insights for game developers and policymakers on the potential risks associated with these monetisation strategies
The Centers and Margins of Modeling Humans in Well-being Technologies
No full textThis paper critically examines the machine learning (ML) modeling of humans in three case studies of well-being technologies. Through a critical technical approach, it examines how these apps were experienced in daily life (technology in use) to surface breakdowns and to identify the assumptions about the “human” body entrenched in the ML models (technology design). To address these issues, this paper applies agential realism to decenter foundational assumptions, such as body regularity and health/illness binaries, and speculates more inclusive design and ML modeling paths that acknowledge irregularity, human-system entanglements, and uncertain transitions. This work is among the first to explore the implications of decentering theories in computational modeling of human bodies and well-being, offering insights for more inclusive technologies and speculations toward posthuman-centered ML modeling
Solving Polynomial Equations Over Finite Fields
Get PDFWe present a randomized algorithm for solving low-degree polynomial equation systems over finite fields faster than exhaustive search. In order to do so, we follow a line of work by Lokshtanov, Paturi, Tamaki, Williams, and Yu (SODA 2017), Björklund, Kaski, and Williams (ICALP 2019), and Dinur (SODA 2021). In particular, we generalize Dinur’s algorithm for F2 to all finite fields, in particular the “symbolic interpolation” of Björklund, Kaski, and Williams, and we use an efficient trimmed multipoint evaluation and interpolation procedure for multivariate polynomials over finite fields by Van der Hoeven and Schost (AAECC 2013). The running time of our algorithm matches that of Dinur’s algorithm for F2 and is significantly faster than the one of Lokshtanov et al. for q > 2.We complement our results with tight conditional lower bounds that, surprisingly, we were not able to find in the literature. In particular, under the strong exponential time hypothesis, we prove that it is impossible to solve n-variate low-degree polynomial equation systems over Fq in time O((q −ε) n). As a bonus, we show that under the counting version of the strong exponential time hypothesis, it is impossible to compute the number of roots of a single n-variate low-degree polynomial over Fq in time O((q − ε)n); this generalizes a result of Williams (SOSA 2018) from F2 to all finite fields
BECOMING IN/FERTILE: Data Practices and Reproductive Re-con-figurations
Get PDFThis dissertation explores how bodies, temporalities, and orientations become figured, configured, and reconfigured within everyday practices of fertility sensemaking. Fertility sensemaking refers to the ways people understand their bodies through data (e.g., about them, or in relation to statistics), alongside socio-cultural norms of reproduction and temporal scripts. Such practices become increasingly supported by various technologies that enter homes, clinics, and bodies, to generate and analyze data around reproductive bodies. It is thus necessary to understand how such data practices, and the technologies they enroll, augment and afford relations to, and understandings of, fertility. Throughout this dissertation I develop a qualitative analysis of fertility sensemaking that is grounded in interdisciplinary engagements with work in feminist theory, Human-computer interaction (HCI), and Science and Technology Studies (STS), and anchors in theories on posthumanism and crip/queer temporalities. I build on a range of empirical material, including bodily experiences around data obtained through mundane reproductive technologies, such as Menstruation and Fertility Tracking Applications (MFTAs), online forums, as well as medicalized datafication practices in Fertility Awareness Counseling (FAC), to scrutinize how different sites of datafication (the intimate, the shared, the medicalized) participate in the re-con-figuration of fertility. Rather than only being a ‘quality of the body’, this dissertation brings forth a conception of fertility as entangled, material, and relational practices. The three papers included in this dissertation contribute to HCI, STS, as well as feminist theory, and argue respectively 1) how reproductive bodies become figured through the datafication technologies; 2) how different objects and subjects come together, and configure fertile time and temporalities through relational and distributed practices of fertility sensemaking; and 3) how orientations to fertility become reconfigured in terms of possibility, time, and space, as infertility rather than fertility becomes anticipated
The Cultural Complexity of Carbon
No full textThis volume discusses the transformational role that carbon – both as a concept and as a distinct set of material forms and effects – has come to play in social and cultural life.As a proxy for greenhouse gas emission data, carbon has grown to become a phenomenon that can no longer be accounted for solely within the technoscientific vocabulary of climate scientists. The Cultural Complexity of Carbon examines the extent to which our knowledge of carbon affects the way that human beings relate to each other and to the climate and/or the environment. It draws on case studies from a diverse range of topics including peatland restoration, religion and energy systems to explore questions that have so far been under-explored in the current literature. These questions include whether the recognition of carbon’s role in climate change leads to an incremental adaptation of lifestyles or to cultural or existential transformations, but also more concretely how carbon is made meaningful, and how these meanings are attached to ideals of cultural change or continuity. Spanning multiple perspectives and disciplinary positions, this volume provides a go-to point for the next generation of ethnographic studies of carbon and climate change. It cuts across what has hitherto been largely separate literatures in anthropology, geography and sociology to provide a meta-level orientation to how contemporary narratives of the role of carbon are being told.By addressing the intimate social and cultural changes that stem from humanity’s involvement with its natural and climatic resources, this volume is of interest to students and scholars of climate change within the social sciences and environmental humanities
