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C.C.S. Liem
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Analogical reasoning involves identifying and preserving relational structures across domains. However, existing approaches to AI-driven multimodal analogy generation lack an interpretable measure of whether this structure is understood and maintained in the generated output. We address this gap with Pullback Refinement via Interpretable Structural Mapping (PRISM), a modality-agnostic framework for measuring and improving relational alignment in multimodal analogies, evaluated on visual metaphor generation. PRISM represents analogies as explicit relational mappings grounded in category theory and uses VLMs to instantiate these structures across modalities. Its first component, the pullback score, quantifies relational alignment from the resulting graph representation. On the AnaloBench benchmark, selecting the correct analogy purely by pullback score achieves 82.5% accuracy, demonstrating that the score captures meaningful relational information. PRISM's second component is an iterative refinement loop that uses the pullback score as an in-context feedback signal to iteratively revise the generated image towards greater relational depth. VLM-as-a-judge and human evaluations show that PRISM consistently improves metaphor consistency and analogy appropriateness over zero-shot generation, with human participants preferring the refined output in 57.65% of pairwise comparisons. However, its advantage over a strong CLIPScore-based GEPA prompt optimiser is small, and qualitative analysis reveals that refinement can favour visually crowded compositions rather than genuinely deeper relational correspondences.
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Analogical reasoning involves identifying and preserving relational structures across domains. However, existing approaches to AI-driven multimodal analogy generation lack an interpretable measure of whether this structure is understood and maintained in the generated output. We address this gap with Pullback Refinement via Interpretable Structural Mapping (PRISM), a modality-agnostic framework for measuring and improving relational alignment in multimodal analogies, evaluated on visual metaphor generation. PRISM represents analogies as explicit relational mappings grounded in category theory and uses VLMs to instantiate these structures across modalities. Its first component, the pullback score, quantifies relational alignment from the resulting graph representation. On the AnaloBench benchmark, selecting the correct analogy purely by pullback score achieves 82.5% accuracy, demonstrating that the score captures meaningful relational information. PRISM's second component is an iterative refinement loop that uses the pullback score as an in-context feedback signal to iteratively revise the generated image towards greater relational depth. VLM-as-a-judge and human evaluations show that PRISM consistently improves metaphor consistency and analogy appropriateness over zero-shot generation, with human participants preferring the refined output in 57.65% of pairwise comparisons. However, its advantage over a strong CLIPScore-based GEPA prompt optimiser is small, and qualitative analysis reveals that refinement can favour visually crowded compositions rather than genuinely deeper relational correspondences.
Autonomous vehicle testing relies on safety-critical scenarios to expose edge cases that fall outside standard training distributions. Current LLM-based generation frameworks can produce valid scenario code but treat generation as a one-shot process or resort to basic parameter tuning, offering no mechanism to structurally refine scenarios in response to agent performance. In this paper we present ChatSceneEVO, a closed-loop evolutionary framework that addresses this by using a locally hosted large language model as a mutation and crossover operator over natural language scenario descriptions, grounded by fitness signals derived from CARLA simulation. Rather than perturbing numerical parameters, our approach evolves the high-level textual sub-descriptions of a scenario (adversarial object, behavior, geometry, and spawn position) before any simulation code is generated, enabling structural changes that parametric methods cannot express. Evaluated on ChatScene's 40-scenario benchmark, our pipeline achieved mean scenario criticality of 0.66 over ten generations, substantially outperforming the static ChatScene baseline of 0.09. Ablation studies confirm the pipeline reliably synthesizes failure-inducing scenarios regardless of whether the initial population is diverse, benign, or already critical.
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Autonomous vehicle testing relies on safety-critical scenarios to expose edge cases that fall outside standard training distributions. Current LLM-based generation frameworks can produce valid scenario code but treat generation as a one-shot process or resort to basic parameter tuning, offering no mechanism to structurally refine scenarios in response to agent performance. In this paper we present ChatSceneEVO, a closed-loop evolutionary framework that addresses this by using a locally hosted large language model as a mutation and crossover operator over natural language scenario descriptions, grounded by fitness signals derived from CARLA simulation. Rather than perturbing numerical parameters, our approach evolves the high-level textual sub-descriptions of a scenario (adversarial object, behavior, geometry, and spawn position) before any simulation code is generated, enabling structural changes that parametric methods cannot express. Evaluated on ChatScene's 40-scenario benchmark, our pipeline achieved mean scenario criticality of 0.66 over ten generations, substantially outperforming the static ChatScene baseline of 0.09. Ablation studies confirm the pipeline reliably synthesizes failure-inducing scenarios regardless of whether the initial population is diverse, benign, or already critical.
What Types of Hate Speech Samples Do LLMs Struggle With?
The Alignment of Large Language Models’ Responses to Subjective Variations in Hate Speech
Hate speech detection remains challenging because harmful language is often contextual, indirect, and difficult to distinguish from legitimate discussion, criticism, or reporting. While previous work has highlighted the influence of differing hate speech definitions on annotation and evaluation, less attention has been paid to the specific types of samples that remain difficult for large language models (LLMs), regardless of how hate speech is defined. This paper investigates which hate speech and non-hate speech samples are most challenging for LLaMA 3-8B-Instruct and Qwen 2.5-7B-Instruct using HateCheck Extended and seven hate speech definitions representing platform policies, legal frameworks, and theoretical perspectives.
The results show that overall performance remains relatively stable across definitions, but sample-level analysis reveals substantial differences in error patterns. Explicit hateful cues are generally classified correctly, whereas context-dependent phenomena remain difficult across definitions. Cross-definition analysis further identifies errors that persist regardless of definition, suggesting that these failures stem from model limitations rather than definitional ambiguity alone. These findings demonstrate that sample-level evaluation provides insights not visible through aggregate performance metrics alone and highlight the continuing challenge of contextual reasoning in LLM-based moderation systems. ...
The results show that overall performance remains relatively stable across definitions, but sample-level analysis reveals substantial differences in error patterns. Explicit hateful cues are generally classified correctly, whereas context-dependent phenomena remain difficult across definitions. Cross-definition analysis further identifies errors that persist regardless of definition, suggesting that these failures stem from model limitations rather than definitional ambiguity alone. These findings demonstrate that sample-level evaluation provides insights not visible through aggregate performance metrics alone and highlight the continuing challenge of contextual reasoning in LLM-based moderation systems. ...
Hate speech detection remains challenging because harmful language is often contextual, indirect, and difficult to distinguish from legitimate discussion, criticism, or reporting. While previous work has highlighted the influence of differing hate speech definitions on annotation and evaluation, less attention has been paid to the specific types of samples that remain difficult for large language models (LLMs), regardless of how hate speech is defined. This paper investigates which hate speech and non-hate speech samples are most challenging for LLaMA 3-8B-Instruct and Qwen 2.5-7B-Instruct using HateCheck Extended and seven hate speech definitions representing platform policies, legal frameworks, and theoretical perspectives.
The results show that overall performance remains relatively stable across definitions, but sample-level analysis reveals substantial differences in error patterns. Explicit hateful cues are generally classified correctly, whereas context-dependent phenomena remain difficult across definitions. Cross-definition analysis further identifies errors that persist regardless of definition, suggesting that these failures stem from model limitations rather than definitional ambiguity alone. These findings demonstrate that sample-level evaluation provides insights not visible through aggregate performance metrics alone and highlight the continuing challenge of contextual reasoning in LLM-based moderation systems.
The results show that overall performance remains relatively stable across definitions, but sample-level analysis reveals substantial differences in error patterns. Explicit hateful cues are generally classified correctly, whereas context-dependent phenomena remain difficult across definitions. Cross-definition analysis further identifies errors that persist regardless of definition, suggesting that these failures stem from model limitations rather than definitional ambiguity alone. These findings demonstrate that sample-level evaluation provides insights not visible through aggregate performance metrics alone and highlight the continuing challenge of contextual reasoning in LLM-based moderation systems.
Automated hate speech detection is crucial to keep up with the high demand for moderation online, yet current models struggle to produce stable and consistent results. While metrics such as accuracy evaluate a model's overall performance, they fail to detect instability, meaning predictions on identical inputs fluctuate. Better metrics exist that can detect this, such as micro-consistency, which looks at the consistency on the individual test case level. This paper looks at what the impact is of providing explicit definitions of hate speech to LLMs for hate speech classification, using micro-consistency metrics and uncertainty metrics. The research was done using the Llama-3-8B-Instruct model for binary classification on the HateCheck dataset. The results show that providing explicit definitions for hate speech classification using zero-shot prompting worsened micro-consistency and uncertainty, and that the differences are statistically significant. However, more research is required to conclude with certainty that this decline in stability is caused by the model's inherent limitations, rather than a suboptimal setup for this task.
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Automated hate speech detection is crucial to keep up with the high demand for moderation online, yet current models struggle to produce stable and consistent results. While metrics such as accuracy evaluate a model's overall performance, they fail to detect instability, meaning predictions on identical inputs fluctuate. Better metrics exist that can detect this, such as micro-consistency, which looks at the consistency on the individual test case level. This paper looks at what the impact is of providing explicit definitions of hate speech to LLMs for hate speech classification, using micro-consistency metrics and uncertainty metrics. The research was done using the Llama-3-8B-Instruct model for binary classification on the HateCheck dataset. The results show that providing explicit definitions for hate speech classification using zero-shot prompting worsened micro-consistency and uncertainty, and that the differences are statistically significant. However, more research is required to conclude with certainty that this decline in stability is caused by the model's inherent limitations, rather than a suboptimal setup for this task.
Hate speech lacks a single agreed definition across legal, social, and benchmark contexts, yet instruction-tuned large language models (LLMs) are increasingly used for hate speech detection. While recent work has explored definition-aware prompting, it remains unclear how different definitions interact with few-shot prompting strategies and model capacity. We investigate whether zero-shot and few-shot in-context learning can align LLMs with dataset-specific hate speech definitions without fine-tuning. Using the HateCheck benchmark, we evaluate three models (Gemma-2-2B, Llama-3.2-3B, and Qwen2.5-3B) under three definition settings (no definition, author-provided text, and structured criteria-based definition) and four prompting strategies (zero-shot and three few-shot variants). Results show that explicit definitions do not reliably improve performance and can sometimes reduce it. Furthermore, few-shot prompting is generally more effective, with the strongest performance often achieved by retrieving semantically similar examples for each query and including them in the prompt. In addition, higher-capacity models benefit more from richer prompts, whereas the smallest model frequently degrades as prompt complexity increases. Overall, definition wording, exemplar selection, and model capacity interact strongly and should be tuned jointly rather than considered in isolation.
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Hate speech lacks a single agreed definition across legal, social, and benchmark contexts, yet instruction-tuned large language models (LLMs) are increasingly used for hate speech detection. While recent work has explored definition-aware prompting, it remains unclear how different definitions interact with few-shot prompting strategies and model capacity. We investigate whether zero-shot and few-shot in-context learning can align LLMs with dataset-specific hate speech definitions without fine-tuning. Using the HateCheck benchmark, we evaluate three models (Gemma-2-2B, Llama-3.2-3B, and Qwen2.5-3B) under three definition settings (no definition, author-provided text, and structured criteria-based definition) and four prompting strategies (zero-shot and three few-shot variants). Results show that explicit definitions do not reliably improve performance and can sometimes reduce it. Furthermore, few-shot prompting is generally more effective, with the strongest performance often achieved by retrieving semantically similar examples for each query and including them in the prompt. In addition, higher-capacity models benefit more from richer prompts, whereas the smallest model frequently degrades as prompt complexity increases. Overall, definition wording, exemplar selection, and model capacity interact strongly and should be tuned jointly rather than considered in isolation.
Which definition of hate speech does the default behaviour of large language models align with most closely?
A Zero-Shot Probing Study of Two Open-Weight Models
What counts as hate speech varies and complicates automated detection systems. Large language models (LLMs) are increasingly used for this task in a zero-shot setting, yet the intrinsic definition of hate speech that such models apply when no definition is supplied remains poorly understood. This paper probes the intrinsic, unguided conception of hate speech that two open-weight instruction-tuned models, Meta Llama 3.1 and Google Gemma 4, apply by default. We combine three complementary measurements: zero-shot binary classification, structured elicitation of Hate Speech Criteria (HSC), and a contamination control that compares both tasks with a set of novel cases, and we add two follow-up analyses: a prompt-paraphrase robustness check and a definition-injection probe on the dominance criterion. Both models classify hateful content with high binary accuracy and demonstrate strong target group identification. However, they fail on the dominance criterion, defaulting instead to a misinterpretation where almost all hostile speech is labelled as dominating. We conclude that while the default definition these LLMs apply is target-aware, its tendency toward over-inclusive criterion application constrains the reliability of unguided models for fine-grained hate speech characterisation.
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What counts as hate speech varies and complicates automated detection systems. Large language models (LLMs) are increasingly used for this task in a zero-shot setting, yet the intrinsic definition of hate speech that such models apply when no definition is supplied remains poorly understood. This paper probes the intrinsic, unguided conception of hate speech that two open-weight instruction-tuned models, Meta Llama 3.1 and Google Gemma 4, apply by default. We combine three complementary measurements: zero-shot binary classification, structured elicitation of Hate Speech Criteria (HSC), and a contamination control that compares both tasks with a set of novel cases, and we add two follow-up analyses: a prompt-paraphrase robustness check and a definition-injection probe on the dominance criterion. Both models classify hateful content with high binary accuracy and demonstrate strong target group identification. However, they fail on the dominance criterion, defaulting instead to a misinterpretation where almost all hostile speech is labelled as dominating. We conclude that while the default definition these LLMs apply is target-aware, its tendency toward over-inclusive criterion application constrains the reliability of unguided models for fine-grained hate speech characterisation.
The Alignment of Large Language Models' Responses to Subjective Variations in Hate Speech
Comparing Alignment to Real-Life-Inspired Definitions in Zero-Shot Hate Speech Classification
Detecting hateful content on social media has become an active area of research, with recent approaches focusing on the use of Large Language Models (LLMs). Rather than using datasets to train classifiers, researchers are exploring methods that embed hate speech definitions directly in the model's prompt. However, hate speech is a subjective concept, and its definition varies across contexts. As a result, LLMs must align their classifications with the specific definition provided in the prompt. To make the creation process more systematic, frameworks for constructing context-specific definitions of hate speech have been proposed. Yet, no work has compared how framework-based formulations influence LLM alignment relative to the definitions used in real-life regulation, such as laws and social media policies. This study, therefore, compares definitions from the Hate Speech Criteria (HSC) framework, legal texts, and platform policies by evaluating how precisely two LLMs align with each type under a zero-shot prompting setup. Our results indicate that while the level of alignment is model-dependent, legal and policy definitions generally guide LLM behavior more effectively than framework-based formulations. Nevertheless, definitions created with the framework still steer models in the intended direction, suggesting that further refinement of these frameworks could improve their effectiveness in prompt-based hate speech detection.
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Detecting hateful content on social media has become an active area of research, with recent approaches focusing on the use of Large Language Models (LLMs). Rather than using datasets to train classifiers, researchers are exploring methods that embed hate speech definitions directly in the model's prompt. However, hate speech is a subjective concept, and its definition varies across contexts. As a result, LLMs must align their classifications with the specific definition provided in the prompt. To make the creation process more systematic, frameworks for constructing context-specific definitions of hate speech have been proposed. Yet, no work has compared how framework-based formulations influence LLM alignment relative to the definitions used in real-life regulation, such as laws and social media policies. This study, therefore, compares definitions from the Hate Speech Criteria (HSC) framework, legal texts, and platform policies by evaluating how precisely two LLMs align with each type under a zero-shot prompting setup. Our results indicate that while the level of alignment is model-dependent, legal and policy definitions generally guide LLM behavior more effectively than framework-based formulations. Nevertheless, definitions created with the framework still steer models in the intended direction, suggesting that further refinement of these frameworks could improve their effectiveness in prompt-based hate speech detection.
Learn together over time
Distributed Multi-frequency time series framework
Modern industrial systems, from wind-farm monitoring to economic indicators like GDP generate vast amounts of time series data from diverse sources. These data streams are sampled at varying and often inconsistent frequencies, presenting challenges for accurate forecasting. Furthermore, in many real-world scenarios, data are distributed across nodes or tasks, introducing complications due to heterogeneity across tasks. Existing forecasting approaches typically address frequency misalignment and decentralized learning as separate problems, limiting their ability to model real-world deployments effectively. We propose CrossFreqNet, a unified multitask encoder–decoder architecture that addresses both challenges: (i.) integrating multi-frequency data streams without the need of up or down sampling to match frequency, preserving signal integrity and (ii.) introducing GradBal, a gradient-balancing mechanism that mitigates learning conflicts caused by task heterogeneity and promoting stable convergence across tasks in a distributed learning environment. Across four public benchmarks and one industrial dataset, our model reduces forecasting errors by up to 72% over the best multi-task baseline (UniTS) and up to 48% over PCGrad, a SOTA gradient conflict mitigation method. Code is made available at https://github.com/arc-arnob/TS-MTL/.
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Modern industrial systems, from wind-farm monitoring to economic indicators like GDP generate vast amounts of time series data from diverse sources. These data streams are sampled at varying and often inconsistent frequencies, presenting challenges for accurate forecasting. Furthermore, in many real-world scenarios, data are distributed across nodes or tasks, introducing complications due to heterogeneity across tasks. Existing forecasting approaches typically address frequency misalignment and decentralized learning as separate problems, limiting their ability to model real-world deployments effectively. We propose CrossFreqNet, a unified multitask encoder–decoder architecture that addresses both challenges: (i.) integrating multi-frequency data streams without the need of up or down sampling to match frequency, preserving signal integrity and (ii.) introducing GradBal, a gradient-balancing mechanism that mitigates learning conflicts caused by task heterogeneity and promoting stable convergence across tasks in a distributed learning environment. Across four public benchmarks and one industrial dataset, our model reduces forecasting errors by up to 72% over the best multi-task baseline (UniTS) and up to 48% over PCGrad, a SOTA gradient conflict mitigation method. Code is made available at https://github.com/arc-arnob/TS-MTL/.
The Sim2Real gap poses significant challenges for testing autonomous vehicles, often becoming apparent only during high-risk real-world deployments. This research proposes a novel pipeline that leverages both high-fidelity (CARLA) and low-fidelity (Gym-Duckietown) simulators to estimate this gap prior to deployment. The results reveal a strong corelation between performance in Gym Duckietown and real-world outcomes, suggesting it can serve as potential estimation for real world performance and the Sim2Real gap. Nonetheless, real-world testing remains an essential part of the validation process. Future work should build on these findings to further explore and validate the approach.
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The Sim2Real gap poses significant challenges for testing autonomous vehicles, often becoming apparent only during high-risk real-world deployments. This research proposes a novel pipeline that leverages both high-fidelity (CARLA) and low-fidelity (Gym-Duckietown) simulators to estimate this gap prior to deployment. The results reveal a strong corelation between performance in Gym Duckietown and real-world outcomes, suggesting it can serve as potential estimation for real world performance and the Sim2Real gap. Nonetheless, real-world testing remains an essential part of the validation process. Future work should build on these findings to further explore and validate the approach.
What are the areas of improvement for data available for the development of disease outbreak forecasting ML models?
Machine Learning for humanitarian forecasting: a survey
This review surveys the current state of data used in the development of Machine Learning models for disease outbreak forecasting, with a focus on identifying systemic shortcomings and areas for improvement. A set of 26 development papers was selected and analyzed based on the dataset's attributes such as scope, type, accessibility, and quality. Through a thematic analysis technique, five dominant categories of data failure were identified: structural, procedural, accessibility, logistical and temporary. Hospital-collected data remains the dominant source but is hindered by under-sampling and latency, while non-traditional data sources offer improved responsiveness at the cost of increased pre-processing complexity. Supplementary datasets, such as climate or mobility data, were found to be underutilized, despite their potential to improve forecasting accuracy. Key areas for improvement include the standardization and public availability of datasets, integration of complementary data sources, and use of language models to manage linguistically ambiguous data. The findings suggest that the current data limitations are structural and widespread, requiring procedural and institutional reforms to improve model generalizability and reliability in disease outbreak forecasting.
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This review surveys the current state of data used in the development of Machine Learning models for disease outbreak forecasting, with a focus on identifying systemic shortcomings and areas for improvement. A set of 26 development papers was selected and analyzed based on the dataset's attributes such as scope, type, accessibility, and quality. Through a thematic analysis technique, five dominant categories of data failure were identified: structural, procedural, accessibility, logistical and temporary. Hospital-collected data remains the dominant source but is hindered by under-sampling and latency, while non-traditional data sources offer improved responsiveness at the cost of increased pre-processing complexity. Supplementary datasets, such as climate or mobility data, were found to be underutilized, despite their potential to improve forecasting accuracy. Key areas for improvement include the standardization and public availability of datasets, integration of complementary data sources, and use of language models to manage linguistically ambiguous data. The findings suggest that the current data limitations are structural and widespread, requiring procedural and institutional reforms to improve model generalizability and reliability in disease outbreak forecasting.
Keep It PG or Let It Go?
Exploring the Presence of (In)appropriateness in YouTube Videos for Young Children and Opportunities for Safeguarding
As online video platforms like YouTube and YouTube Kids continue to shape young children's daily media use, concerns about their exposure to inappropriate content persist. While these platforms implement various safeguards to protect young audiences, inappropriate videos continue to surface in search results and next-video recommendations, sometimes even at the top of the list. This study explores how metadata-derived features can be used to identify and address such content. Drawing on a ground-truth dataset of YouTube videos labeled for toddler appropriateness, we conduct a detailed feature analysis to uncover patterns linked to (in)appropriateness and train a classifier capable of predicting video appropriateness for young children. Building on these insights, we develop and evaluate score-based reranking strategies designed to reduce exposure to inappropriate videos while promoting age-appropriate content. Our findings show that metadata-informed reranking significantly improves the prioritization of suitable content, raising HitRate@1 of suitable videos from 14% to as high as 62%, but also reveal critical trade-offs: misclassified inappropriate videos, particularly when predicted with high confidence, may still appear in top-ranked positions. As such, detection and reranking methods like ours represent a first step that warrants further steps for safer recommendation environments for young children. This research provides a practical framework for improving recommendation outcomes and contributes to the broader conversation on designing safer, more transparent, and child-centered media systems.
Full Codebase available on the following repository - https://github.com/JoeydeW/KeepItPGorLetItGo ...
Full Codebase available on the following repository - https://github.com/JoeydeW/KeepItPGorLetItGo ...
As online video platforms like YouTube and YouTube Kids continue to shape young children's daily media use, concerns about their exposure to inappropriate content persist. While these platforms implement various safeguards to protect young audiences, inappropriate videos continue to surface in search results and next-video recommendations, sometimes even at the top of the list. This study explores how metadata-derived features can be used to identify and address such content. Drawing on a ground-truth dataset of YouTube videos labeled for toddler appropriateness, we conduct a detailed feature analysis to uncover patterns linked to (in)appropriateness and train a classifier capable of predicting video appropriateness for young children. Building on these insights, we develop and evaluate score-based reranking strategies designed to reduce exposure to inappropriate videos while promoting age-appropriate content. Our findings show that metadata-informed reranking significantly improves the prioritization of suitable content, raising HitRate@1 of suitable videos from 14% to as high as 62%, but also reveal critical trade-offs: misclassified inappropriate videos, particularly when predicted with high confidence, may still appear in top-ranked positions. As such, detection and reranking methods like ours represent a first step that warrants further steps for safer recommendation environments for young children. This research provides a practical framework for improving recommendation outcomes and contributes to the broader conversation on designing safer, more transparent, and child-centered media systems.
Full Codebase available on the following repository - https://github.com/JoeydeW/KeepItPGorLetItGo
Full Codebase available on the following repository - https://github.com/JoeydeW/KeepItPGorLetItGo
Gesture-based sonification games offer a promising medium for motor-skill rehabilitation by transforming therapeutic movements into interactive musical experiences. However, creating custom levels for such games remains inaccessible to therapists and researchers, who often lack the musical or technical expertise required to shape musical material into meaningful motor tasks. This thesis investigates how non-technical and non-musician users can be supported in generating music, authoring interaction structures, and validating playable levels for gesture-based sonification games.
To address this challenge, this work introduces an end-to-end authoring pipeline that combines symbolic music generation with an intuitive level-editing system. A hierarchical diffusion model is adapted to produce structured, multi-track musical material through high-level controls such as key, tempo, and song form. A web-based authoring interface then allows users to refine this material, simplify dense passages through a trigger--support note mechanism, and map notes to spatially and temporally aligned gesture targets. A target-configuration module provides synchronized previews and export functions that integrate directly with the PIZZICATO runtime, enabling real-time testing and performance logging.
A qualitative expert evaluation with nine therapists and researchers examined the usability, flexibility, and therapeutic potential of the system. Participants found the workflow accessible and intuitive, valued the direct manipulation of musical and spatial elements, and highlighted the potential of the tool to streamline content creation for motor-rehabilitation studies. The evaluation also surfaced conceptual limitations, including the need for broader musical genres beyond pop-derived structures, and the opportunity to incorporate clinically informed automation such as predefined motor-exercise patterns.
This thesis contributes (i) a novel, integrated workflow for non-technical authoring of gesture-based sonification levels, (ii) interface techniques that translate symbolic musical structure into spatial--temporal interaction tasks, and (iii) empirical insights into the needs of therapists and psychologists designing movement-based therapeutic content. ...
To address this challenge, this work introduces an end-to-end authoring pipeline that combines symbolic music generation with an intuitive level-editing system. A hierarchical diffusion model is adapted to produce structured, multi-track musical material through high-level controls such as key, tempo, and song form. A web-based authoring interface then allows users to refine this material, simplify dense passages through a trigger--support note mechanism, and map notes to spatially and temporally aligned gesture targets. A target-configuration module provides synchronized previews and export functions that integrate directly with the PIZZICATO runtime, enabling real-time testing and performance logging.
A qualitative expert evaluation with nine therapists and researchers examined the usability, flexibility, and therapeutic potential of the system. Participants found the workflow accessible and intuitive, valued the direct manipulation of musical and spatial elements, and highlighted the potential of the tool to streamline content creation for motor-rehabilitation studies. The evaluation also surfaced conceptual limitations, including the need for broader musical genres beyond pop-derived structures, and the opportunity to incorporate clinically informed automation such as predefined motor-exercise patterns.
This thesis contributes (i) a novel, integrated workflow for non-technical authoring of gesture-based sonification levels, (ii) interface techniques that translate symbolic musical structure into spatial--temporal interaction tasks, and (iii) empirical insights into the needs of therapists and psychologists designing movement-based therapeutic content. ...
Gesture-based sonification games offer a promising medium for motor-skill rehabilitation by transforming therapeutic movements into interactive musical experiences. However, creating custom levels for such games remains inaccessible to therapists and researchers, who often lack the musical or technical expertise required to shape musical material into meaningful motor tasks. This thesis investigates how non-technical and non-musician users can be supported in generating music, authoring interaction structures, and validating playable levels for gesture-based sonification games.
To address this challenge, this work introduces an end-to-end authoring pipeline that combines symbolic music generation with an intuitive level-editing system. A hierarchical diffusion model is adapted to produce structured, multi-track musical material through high-level controls such as key, tempo, and song form. A web-based authoring interface then allows users to refine this material, simplify dense passages through a trigger--support note mechanism, and map notes to spatially and temporally aligned gesture targets. A target-configuration module provides synchronized previews and export functions that integrate directly with the PIZZICATO runtime, enabling real-time testing and performance logging.
A qualitative expert evaluation with nine therapists and researchers examined the usability, flexibility, and therapeutic potential of the system. Participants found the workflow accessible and intuitive, valued the direct manipulation of musical and spatial elements, and highlighted the potential of the tool to streamline content creation for motor-rehabilitation studies. The evaluation also surfaced conceptual limitations, including the need for broader musical genres beyond pop-derived structures, and the opportunity to incorporate clinically informed automation such as predefined motor-exercise patterns.
This thesis contributes (i) a novel, integrated workflow for non-technical authoring of gesture-based sonification levels, (ii) interface techniques that translate symbolic musical structure into spatial--temporal interaction tasks, and (iii) empirical insights into the needs of therapists and psychologists designing movement-based therapeutic content.
To address this challenge, this work introduces an end-to-end authoring pipeline that combines symbolic music generation with an intuitive level-editing system. A hierarchical diffusion model is adapted to produce structured, multi-track musical material through high-level controls such as key, tempo, and song form. A web-based authoring interface then allows users to refine this material, simplify dense passages through a trigger--support note mechanism, and map notes to spatially and temporally aligned gesture targets. A target-configuration module provides synchronized previews and export functions that integrate directly with the PIZZICATO runtime, enabling real-time testing and performance logging.
A qualitative expert evaluation with nine therapists and researchers examined the usability, flexibility, and therapeutic potential of the system. Participants found the workflow accessible and intuitive, valued the direct manipulation of musical and spatial elements, and highlighted the potential of the tool to streamline content creation for motor-rehabilitation studies. The evaluation also surfaced conceptual limitations, including the need for broader musical genres beyond pop-derived structures, and the opportunity to incorporate clinically informed automation such as predefined motor-exercise patterns.
This thesis contributes (i) a novel, integrated workflow for non-technical authoring of gesture-based sonification levels, (ii) interface techniques that translate symbolic musical structure into spatial--temporal interaction tasks, and (iii) empirical insights into the needs of therapists and psychologists designing movement-based therapeutic content.
Due to the rapid introduction of Autonomous Driving Systems into safety-critical domains, we have seen an increasing demand for their proper validation.
Simulation-based testing has emerged as a means to address this challenge by generating and selecting test scenarios that target safety-critical behaviors.
However, efficient and interpretable evaluation is still a challenge in the field, due to the vastness of possible operating conditions.
In this work we introduce a simulation-based safety boundary exploration method designed to efficiently identify critical scenarios within a search space. In addition, we use the results of a Careful and Competent (CnC) driver model to inform our search approach.
Our approach identifies and contours the boundary, separating safe and unsafe operational regions in a scenario parameter space.
We evaluated four different variants of this approach based on number of samples traversed for each and compared to a baseline exhaustive grid search approach.
All proposed methods efficiently separate the safe from the unsafe operational regions within the parameter space. Among them, the combination of CnC border initialization with binary search demonstrates the highest efficiency in identifying the safety boundary by
reducing the samples traversed compared to our baseline approach by 90% on average. ...
Simulation-based testing has emerged as a means to address this challenge by generating and selecting test scenarios that target safety-critical behaviors.
However, efficient and interpretable evaluation is still a challenge in the field, due to the vastness of possible operating conditions.
In this work we introduce a simulation-based safety boundary exploration method designed to efficiently identify critical scenarios within a search space. In addition, we use the results of a Careful and Competent (CnC) driver model to inform our search approach.
Our approach identifies and contours the boundary, separating safe and unsafe operational regions in a scenario parameter space.
We evaluated four different variants of this approach based on number of samples traversed for each and compared to a baseline exhaustive grid search approach.
All proposed methods efficiently separate the safe from the unsafe operational regions within the parameter space. Among them, the combination of CnC border initialization with binary search demonstrates the highest efficiency in identifying the safety boundary by
reducing the samples traversed compared to our baseline approach by 90% on average. ...
Due to the rapid introduction of Autonomous Driving Systems into safety-critical domains, we have seen an increasing demand for their proper validation.
Simulation-based testing has emerged as a means to address this challenge by generating and selecting test scenarios that target safety-critical behaviors.
However, efficient and interpretable evaluation is still a challenge in the field, due to the vastness of possible operating conditions.
In this work we introduce a simulation-based safety boundary exploration method designed to efficiently identify critical scenarios within a search space. In addition, we use the results of a Careful and Competent (CnC) driver model to inform our search approach.
Our approach identifies and contours the boundary, separating safe and unsafe operational regions in a scenario parameter space.
We evaluated four different variants of this approach based on number of samples traversed for each and compared to a baseline exhaustive grid search approach.
All proposed methods efficiently separate the safe from the unsafe operational regions within the parameter space. Among them, the combination of CnC border initialization with binary search demonstrates the highest efficiency in identifying the safety boundary by
reducing the samples traversed compared to our baseline approach by 90% on average.
Simulation-based testing has emerged as a means to address this challenge by generating and selecting test scenarios that target safety-critical behaviors.
However, efficient and interpretable evaluation is still a challenge in the field, due to the vastness of possible operating conditions.
In this work we introduce a simulation-based safety boundary exploration method designed to efficiently identify critical scenarios within a search space. In addition, we use the results of a Careful and Competent (CnC) driver model to inform our search approach.
Our approach identifies and contours the boundary, separating safe and unsafe operational regions in a scenario parameter space.
We evaluated four different variants of this approach based on number of samples traversed for each and compared to a baseline exhaustive grid search approach.
All proposed methods efficiently separate the safe from the unsafe operational regions within the parameter space. Among them, the combination of CnC border initialization with binary search demonstrates the highest efficiency in identifying the safety boundary by
reducing the samples traversed compared to our baseline approach by 90% on average.
Master thesis
(2024)
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M.R. Tromp, P.A.N. Bosman, T. Alderliesten, C.C.S. Liem, E.M.C. Sijben, M. Domnich
Counterfactual explanations are a useful tool to explain trained models. They are based on counterfactual thoughts, which are a natural human thought process that helps us reason about the past. When applied to trained models they show how to make minimal changes to a data point in order to obtain a desired output.
Most methods find these counterfactuals by optimizing a set of objectives. Previously these objectives were often combined into a loss function using an aggregation operator. This operator implicitly decides the priority between the objectives, but this ordering is not always in line with the user’s preferences.
To mitigate this the Multi-Objective Counterfactuals (MOC) method was introduced. MOC turns counterfactual generation into a multi-objective optimization problem and presents the user with a diverse set of counterfactuals that have different trade-offs for the objectives. It optimizes the set of objectives with an evolutionary algorithm called Nondominated Sorting Genetic Algorithm II.
In this thesis we optimize this problem using Multi-Objective Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm, which is a different evolutionary algorithm. We present a single-modal method and two multi-modal methods. We compare the performance of our methods to a counterfactual generation method named Diverse Counterfactual Explanations (DiCE), which focusses on feasibility and diversity within a set of generated counterfactuals. Additionally, we also present a visualization tool for sets of counterfactuals.
The single-modal method generates counterfactuals that are realistic, but do not consistently perform well in other areas. The first multi-modal method generates diverse sets of counterfactuals, but overall performs worse. The second multi-modal method generates counterfactuals that perform similarly to the single-modal method, but are more diverse. ...
Most methods find these counterfactuals by optimizing a set of objectives. Previously these objectives were often combined into a loss function using an aggregation operator. This operator implicitly decides the priority between the objectives, but this ordering is not always in line with the user’s preferences.
To mitigate this the Multi-Objective Counterfactuals (MOC) method was introduced. MOC turns counterfactual generation into a multi-objective optimization problem and presents the user with a diverse set of counterfactuals that have different trade-offs for the objectives. It optimizes the set of objectives with an evolutionary algorithm called Nondominated Sorting Genetic Algorithm II.
In this thesis we optimize this problem using Multi-Objective Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm, which is a different evolutionary algorithm. We present a single-modal method and two multi-modal methods. We compare the performance of our methods to a counterfactual generation method named Diverse Counterfactual Explanations (DiCE), which focusses on feasibility and diversity within a set of generated counterfactuals. Additionally, we also present a visualization tool for sets of counterfactuals.
The single-modal method generates counterfactuals that are realistic, but do not consistently perform well in other areas. The first multi-modal method generates diverse sets of counterfactuals, but overall performs worse. The second multi-modal method generates counterfactuals that perform similarly to the single-modal method, but are more diverse. ...
Counterfactual explanations are a useful tool to explain trained models. They are based on counterfactual thoughts, which are a natural human thought process that helps us reason about the past. When applied to trained models they show how to make minimal changes to a data point in order to obtain a desired output.
Most methods find these counterfactuals by optimizing a set of objectives. Previously these objectives were often combined into a loss function using an aggregation operator. This operator implicitly decides the priority between the objectives, but this ordering is not always in line with the user’s preferences.
To mitigate this the Multi-Objective Counterfactuals (MOC) method was introduced. MOC turns counterfactual generation into a multi-objective optimization problem and presents the user with a diverse set of counterfactuals that have different trade-offs for the objectives. It optimizes the set of objectives with an evolutionary algorithm called Nondominated Sorting Genetic Algorithm II.
In this thesis we optimize this problem using Multi-Objective Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm, which is a different evolutionary algorithm. We present a single-modal method and two multi-modal methods. We compare the performance of our methods to a counterfactual generation method named Diverse Counterfactual Explanations (DiCE), which focusses on feasibility and diversity within a set of generated counterfactuals. Additionally, we also present a visualization tool for sets of counterfactuals.
The single-modal method generates counterfactuals that are realistic, but do not consistently perform well in other areas. The first multi-modal method generates diverse sets of counterfactuals, but overall performs worse. The second multi-modal method generates counterfactuals that perform similarly to the single-modal method, but are more diverse.
Most methods find these counterfactuals by optimizing a set of objectives. Previously these objectives were often combined into a loss function using an aggregation operator. This operator implicitly decides the priority between the objectives, but this ordering is not always in line with the user’s preferences.
To mitigate this the Multi-Objective Counterfactuals (MOC) method was introduced. MOC turns counterfactual generation into a multi-objective optimization problem and presents the user with a diverse set of counterfactuals that have different trade-offs for the objectives. It optimizes the set of objectives with an evolutionary algorithm called Nondominated Sorting Genetic Algorithm II.
In this thesis we optimize this problem using Multi-Objective Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm, which is a different evolutionary algorithm. We present a single-modal method and two multi-modal methods. We compare the performance of our methods to a counterfactual generation method named Diverse Counterfactual Explanations (DiCE), which focusses on feasibility and diversity within a set of generated counterfactuals. Additionally, we also present a visualization tool for sets of counterfactuals.
The single-modal method generates counterfactuals that are realistic, but do not consistently perform well in other areas. The first multi-modal method generates diverse sets of counterfactuals, but overall performs worse. The second multi-modal method generates counterfactuals that perform similarly to the single-modal method, but are more diverse.
Master thesis
(2024)
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M. de Schipper, J.C. van Gemert, J.C. van Gemert, C.C.S. Liem, O. Strafforello, X. Liu
Object detectors have come a long way and are used for various applications. In pictures and videos, an object detector must deal with the background. In some settings, this background is indicative of the object; in others, it’s not and can even be disruptive. For models trained on data containing correlations between objects and backgrounds (background bias), it makes sense that changing the background can disrupt learned correlations. This paper is interested in how sensitive object detectors are to background changes, specifically when the training data does not contain correlations between objects and backgrounds. Models were trained on carefully controlled synthetic data, so only the backgrounds differed and correlations could be controlled. The results show that models perform better when tested with seen backgrounds than unseen backgrounds. This performance difference diminishes when the model is trained on more unique backgrounds.
...
Object detectors have come a long way and are used for various applications. In pictures and videos, an object detector must deal with the background. In some settings, this background is indicative of the object; in others, it’s not and can even be disruptive. For models trained on data containing correlations between objects and backgrounds (background bias), it makes sense that changing the background can disrupt learned correlations. This paper is interested in how sensitive object detectors are to background changes, specifically when the training data does not contain correlations between objects and backgrounds. Models were trained on carefully controlled synthetic data, so only the backgrounds differed and correlations could be controlled. The results show that models perform better when tested with seen backgrounds than unseen backgrounds. This performance difference diminishes when the model is trained on more unique backgrounds.
In 2022, the Dutch Open Government Act (Wet open overheid, Woo) has required that government institutions share requested documents with citizens, thereby enhancing government transparency and public access to information.
However, current document retrieval processes often struggle to meet the legal requirements of the Woo, as they frequently fail to respond to requests within the legally mandated time frame due to the lengthy retrieval process.
This study addresses the technical challenges of optimizing information retrieval systems in the context of the Woo, by focusing primarily on document precision and recall.
By critically analyzing existing workflows, we identify key inefficiencies and propose enhancements.
Our research includes a comparative evaluation of dense and sparse retrieval methods to assess their effectiveness in this domain.
Additionally, we explore different preprocessing techniques, investigating their impact on retrieval performance on both sparse and dense retrieval systems, to determine the optimal approach for handling noisy, unstructured government data.
Our results show that these changes in retrieval methods can significantly improve retrieval accuracy and reduce response times.
BM25 in particular, shows strong performance, effectively handling the noisy data often present in government documents, highlighting its suitability for this context.
These insights provide insights for government institutions to improve and streamline their information retrieval workflows, and reduce delays of the Woo requests.
...
However, current document retrieval processes often struggle to meet the legal requirements of the Woo, as they frequently fail to respond to requests within the legally mandated time frame due to the lengthy retrieval process.
This study addresses the technical challenges of optimizing information retrieval systems in the context of the Woo, by focusing primarily on document precision and recall.
By critically analyzing existing workflows, we identify key inefficiencies and propose enhancements.
Our research includes a comparative evaluation of dense and sparse retrieval methods to assess their effectiveness in this domain.
Additionally, we explore different preprocessing techniques, investigating their impact on retrieval performance on both sparse and dense retrieval systems, to determine the optimal approach for handling noisy, unstructured government data.
Our results show that these changes in retrieval methods can significantly improve retrieval accuracy and reduce response times.
BM25 in particular, shows strong performance, effectively handling the noisy data often present in government documents, highlighting its suitability for this context.
These insights provide insights for government institutions to improve and streamline their information retrieval workflows, and reduce delays of the Woo requests.
...
In 2022, the Dutch Open Government Act (Wet open overheid, Woo) has required that government institutions share requested documents with citizens, thereby enhancing government transparency and public access to information.
However, current document retrieval processes often struggle to meet the legal requirements of the Woo, as they frequently fail to respond to requests within the legally mandated time frame due to the lengthy retrieval process.
This study addresses the technical challenges of optimizing information retrieval systems in the context of the Woo, by focusing primarily on document precision and recall.
By critically analyzing existing workflows, we identify key inefficiencies and propose enhancements.
Our research includes a comparative evaluation of dense and sparse retrieval methods to assess their effectiveness in this domain.
Additionally, we explore different preprocessing techniques, investigating their impact on retrieval performance on both sparse and dense retrieval systems, to determine the optimal approach for handling noisy, unstructured government data.
Our results show that these changes in retrieval methods can significantly improve retrieval accuracy and reduce response times.
BM25 in particular, shows strong performance, effectively handling the noisy data often present in government documents, highlighting its suitability for this context.
These insights provide insights for government institutions to improve and streamline their information retrieval workflows, and reduce delays of the Woo requests.
However, current document retrieval processes often struggle to meet the legal requirements of the Woo, as they frequently fail to respond to requests within the legally mandated time frame due to the lengthy retrieval process.
This study addresses the technical challenges of optimizing information retrieval systems in the context of the Woo, by focusing primarily on document precision and recall.
By critically analyzing existing workflows, we identify key inefficiencies and propose enhancements.
Our research includes a comparative evaluation of dense and sparse retrieval methods to assess their effectiveness in this domain.
Additionally, we explore different preprocessing techniques, investigating their impact on retrieval performance on both sparse and dense retrieval systems, to determine the optimal approach for handling noisy, unstructured government data.
Our results show that these changes in retrieval methods can significantly improve retrieval accuracy and reduce response times.
BM25 in particular, shows strong performance, effectively handling the noisy data often present in government documents, highlighting its suitability for this context.
These insights provide insights for government institutions to improve and streamline their information retrieval workflows, and reduce delays of the Woo requests.
The tumor composition of breast cancer determines how tumors behave. Yet, there is a limited understanding of the arrangement of tumor cells in relation to cells in the tumor microenvironment (TME). In this research, we have characterized distance relationships between 324 cell-type pairs in 749 tissue samples of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) study using Weibull distribution estimations, summarizing comprehensive spatial relationships with two parameters. The research showcased the first application of the method to a dataset of this substantial size and a dataset acquired with imaging mass cytometry. We identified distinct spatial relationships among breast cancer subtypes, particularly for basal, HER2-enriched, and luminal A tumors. The spatial relationships indicate attractive and repulsive interactions between different cell types and define cellular arrangements regardless of cellular abundance.
Moreover, several spatial relationships had significant associations with survival outcomes. Both findings could improve patient stratification and prognosis and emphasize the wealth of information that spatial analyses can retrieve. The results also confirm that Weibull distribution estimations are a suitable and effective method to summarize distance distributions. The application to other cohorts could lead to new insights into the tumor composition of different cancer types. Finally, the spatial profiling method was used to characterize neighborhoods and revealed distinct spatial relationships consistent with neighborhood characteristics but also provided new hallmarks. ...
Moreover, several spatial relationships had significant associations with survival outcomes. Both findings could improve patient stratification and prognosis and emphasize the wealth of information that spatial analyses can retrieve. The results also confirm that Weibull distribution estimations are a suitable and effective method to summarize distance distributions. The application to other cohorts could lead to new insights into the tumor composition of different cancer types. Finally, the spatial profiling method was used to characterize neighborhoods and revealed distinct spatial relationships consistent with neighborhood characteristics but also provided new hallmarks. ...
The tumor composition of breast cancer determines how tumors behave. Yet, there is a limited understanding of the arrangement of tumor cells in relation to cells in the tumor microenvironment (TME). In this research, we have characterized distance relationships between 324 cell-type pairs in 749 tissue samples of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) study using Weibull distribution estimations, summarizing comprehensive spatial relationships with two parameters. The research showcased the first application of the method to a dataset of this substantial size and a dataset acquired with imaging mass cytometry. We identified distinct spatial relationships among breast cancer subtypes, particularly for basal, HER2-enriched, and luminal A tumors. The spatial relationships indicate attractive and repulsive interactions between different cell types and define cellular arrangements regardless of cellular abundance.
Moreover, several spatial relationships had significant associations with survival outcomes. Both findings could improve patient stratification and prognosis and emphasize the wealth of information that spatial analyses can retrieve. The results also confirm that Weibull distribution estimations are a suitable and effective method to summarize distance distributions. The application to other cohorts could lead to new insights into the tumor composition of different cancer types. Finally, the spatial profiling method was used to characterize neighborhoods and revealed distinct spatial relationships consistent with neighborhood characteristics but also provided new hallmarks.
Moreover, several spatial relationships had significant associations with survival outcomes. Both findings could improve patient stratification and prognosis and emphasize the wealth of information that spatial analyses can retrieve. The results also confirm that Weibull distribution estimations are a suitable and effective method to summarize distance distributions. The application to other cohorts could lead to new insights into the tumor composition of different cancer types. Finally, the spatial profiling method was used to characterize neighborhoods and revealed distinct spatial relationships consistent with neighborhood characteristics but also provided new hallmarks.
Blockchain technology has allowed for the emergence of a new type of organization, the Decentralized Autonomous Organization (DAO). They have gained significant traction in recent years, reaching market capitalizations of up to 60 billion USD in 2021. These organizations coordinate economic activity by an unbounded group of people within an adversarial environment. However, despite their potential, currently deployed DAOs face notable challenges related to centralization in governance and infrastructure. This work addresses these limitations by proposing a novel architecture for a fully decentralized DAO with no compromises. We introduce a scalable governance protocol utilizing multi-signature schemes to manage shared assets effectively. To demonstrate the feasibility of our approach, we implement, deploy, and evaluate a real-world DAO called Music DAO. Music DAO serves as a compelling use case, enabling listeners to collectively invest in and listen to their favorite artists. This research represents a significant advancement in the field of decentralized organizations, with the potential to revolutionize the way people collaborate and organize themselves.
...
Blockchain technology has allowed for the emergence of a new type of organization, the Decentralized Autonomous Organization (DAO). They have gained significant traction in recent years, reaching market capitalizations of up to 60 billion USD in 2021. These organizations coordinate economic activity by an unbounded group of people within an adversarial environment. However, despite their potential, currently deployed DAOs face notable challenges related to centralization in governance and infrastructure. This work addresses these limitations by proposing a novel architecture for a fully decentralized DAO with no compromises. We introduce a scalable governance protocol utilizing multi-signature schemes to manage shared assets effectively. To demonstrate the feasibility of our approach, we implement, deploy, and evaluate a real-world DAO called Music DAO. Music DAO serves as a compelling use case, enabling listeners to collectively invest in and listen to their favorite artists. This research represents a significant advancement in the field of decentralized organizations, with the potential to revolutionize the way people collaborate and organize themselves.
Searching the web on debated topics, which are issues under active discussion and where individual opinions diverge, can be highly challenging. It requires users to approach their queries objectively, browse many resources, and accept a certain level of uncertainty, even if it conflicts with personal values. In particular, confirmation bias, the tendency to favor information that reinforces beliefs or attitudes, impacts how people gather and interpret information retrieved from search engines when searching on debated topics. It can have far-reaching effects, potentially leading to conflicts, extremism, and polarization.
In an innovative approach towards mitigating the negative effects of confirmation bias on web search on debated topics, we propose implementing a boosting intervention aimed at enhancing Intellectual Humility (IH) – an individual’s ability to acknowledge the fallibility of one’s own beliefs and the limits of one’s knowledge while remaining open to learning from others’ perspectives even if they differ from their own viewpoint. While previous research has highlighted the potential benefits of boosting IH as a means to mitigate confirmation bias, its impact on users’ search behavior has yet to be explored.
Our work bridges this gap through two randomized preregistered user studies, gaining valuable insights into the effectiveness of IH-boosting interventions in mitigating confirmation bias. In the first study, we assessed the effect of three boosting interventions with different levels of complexity on users’ context dependent IH. In the second study, we examined the effects of these interventions on web search behavior.
The first experiment successfully demonstrated the effectiveness of the interventions in boosting participants’ IH across all three treatment groups. However, applying these interventions to web search, no significant differences in search behavior were observed. Our exploratory findings reveal that both individual and environmental factors, including occupation, personal viewpoints, and search results order, shape the impact of IH-boosting interventions on online search behavior, with varying effects observed across different debated topics. We hope this study inspires, and is an initial basis for continued efforts to explore the multifaceted relationship between IH, information-seeking behavior, and responsible opinion formation, ultimately promoting a more informed and unbiased online discourse. ...
In an innovative approach towards mitigating the negative effects of confirmation bias on web search on debated topics, we propose implementing a boosting intervention aimed at enhancing Intellectual Humility (IH) – an individual’s ability to acknowledge the fallibility of one’s own beliefs and the limits of one’s knowledge while remaining open to learning from others’ perspectives even if they differ from their own viewpoint. While previous research has highlighted the potential benefits of boosting IH as a means to mitigate confirmation bias, its impact on users’ search behavior has yet to be explored.
Our work bridges this gap through two randomized preregistered user studies, gaining valuable insights into the effectiveness of IH-boosting interventions in mitigating confirmation bias. In the first study, we assessed the effect of three boosting interventions with different levels of complexity on users’ context dependent IH. In the second study, we examined the effects of these interventions on web search behavior.
The first experiment successfully demonstrated the effectiveness of the interventions in boosting participants’ IH across all three treatment groups. However, applying these interventions to web search, no significant differences in search behavior were observed. Our exploratory findings reveal that both individual and environmental factors, including occupation, personal viewpoints, and search results order, shape the impact of IH-boosting interventions on online search behavior, with varying effects observed across different debated topics. We hope this study inspires, and is an initial basis for continued efforts to explore the multifaceted relationship between IH, information-seeking behavior, and responsible opinion formation, ultimately promoting a more informed and unbiased online discourse. ...
Searching the web on debated topics, which are issues under active discussion and where individual opinions diverge, can be highly challenging. It requires users to approach their queries objectively, browse many resources, and accept a certain level of uncertainty, even if it conflicts with personal values. In particular, confirmation bias, the tendency to favor information that reinforces beliefs or attitudes, impacts how people gather and interpret information retrieved from search engines when searching on debated topics. It can have far-reaching effects, potentially leading to conflicts, extremism, and polarization.
In an innovative approach towards mitigating the negative effects of confirmation bias on web search on debated topics, we propose implementing a boosting intervention aimed at enhancing Intellectual Humility (IH) – an individual’s ability to acknowledge the fallibility of one’s own beliefs and the limits of one’s knowledge while remaining open to learning from others’ perspectives even if they differ from their own viewpoint. While previous research has highlighted the potential benefits of boosting IH as a means to mitigate confirmation bias, its impact on users’ search behavior has yet to be explored.
Our work bridges this gap through two randomized preregistered user studies, gaining valuable insights into the effectiveness of IH-boosting interventions in mitigating confirmation bias. In the first study, we assessed the effect of three boosting interventions with different levels of complexity on users’ context dependent IH. In the second study, we examined the effects of these interventions on web search behavior.
The first experiment successfully demonstrated the effectiveness of the interventions in boosting participants’ IH across all three treatment groups. However, applying these interventions to web search, no significant differences in search behavior were observed. Our exploratory findings reveal that both individual and environmental factors, including occupation, personal viewpoints, and search results order, shape the impact of IH-boosting interventions on online search behavior, with varying effects observed across different debated topics. We hope this study inspires, and is an initial basis for continued efforts to explore the multifaceted relationship between IH, information-seeking behavior, and responsible opinion formation, ultimately promoting a more informed and unbiased online discourse.
In an innovative approach towards mitigating the negative effects of confirmation bias on web search on debated topics, we propose implementing a boosting intervention aimed at enhancing Intellectual Humility (IH) – an individual’s ability to acknowledge the fallibility of one’s own beliefs and the limits of one’s knowledge while remaining open to learning from others’ perspectives even if they differ from their own viewpoint. While previous research has highlighted the potential benefits of boosting IH as a means to mitigate confirmation bias, its impact on users’ search behavior has yet to be explored.
Our work bridges this gap through two randomized preregistered user studies, gaining valuable insights into the effectiveness of IH-boosting interventions in mitigating confirmation bias. In the first study, we assessed the effect of three boosting interventions with different levels of complexity on users’ context dependent IH. In the second study, we examined the effects of these interventions on web search behavior.
The first experiment successfully demonstrated the effectiveness of the interventions in boosting participants’ IH across all three treatment groups. However, applying these interventions to web search, no significant differences in search behavior were observed. Our exploratory findings reveal that both individual and environmental factors, including occupation, personal viewpoints, and search results order, shape the impact of IH-boosting interventions on online search behavior, with varying effects observed across different debated topics. We hope this study inspires, and is an initial basis for continued efforts to explore the multifaceted relationship between IH, information-seeking behavior, and responsible opinion formation, ultimately promoting a more informed and unbiased online discourse.
Survey of Affect Representation Schemes in Physiological Automatic Affect Recognition
A Systematic Literature Review
Physiological signals, such as Electroencephalogram (EEG), Glavic Skin Response (GSR), or Body Temperature, are common inputs for Automatic Affect Recognition (AAR) systems. One of the crucial elements of AAR is the Affect Representation Scheme (ARS) used to define the affective states recognized by the system (e.g., happiness, sadness, fear, anger). Throughout the years many AAR reviews have been published. However, most of them do not include a detailed analysis of ARSs and the motivation behind them. This paper aims to fill this knowledge gap by performing a Systematic Literature Review (SLR) of Computer Science papers that propose a Physiological-signal-based AAR (PAAR) system. We explore how researchers discuss and choose an ARS and whether they base it on actual psychological theories. Eligible papers are retrieved from 4 databases: Web Of Science, IEEExplore, Scopus, and ACM Digital Library. Due to time limitations, the review is done rapidly and some additional search constraints are applied for feasibility. The most significant restrictions are: considering papers published since 2020 and performing experiments on specific benchmarking datasets. We take these constraints and their possible impact into consideration when interpreting the results. The presented review procedure can be stripped from the additional filters and reused for a full review. In total 115 papers are processed. The majority of papers introduce an EEG-based emotion recognition system. The analysis of the extracted information reveals that dimensional ARSs, in particular, Valence/Arousal model is the most popular. Moreover, authors often choose to reduce the dimensions to high/low values. Categorical ARSs are less frequent and usually are adopted from the dataset. Lastly, the authors do not provide extensive motivation for the choice of ARS and rarely refer to psychological theories.
...
Physiological signals, such as Electroencephalogram (EEG), Glavic Skin Response (GSR), or Body Temperature, are common inputs for Automatic Affect Recognition (AAR) systems. One of the crucial elements of AAR is the Affect Representation Scheme (ARS) used to define the affective states recognized by the system (e.g., happiness, sadness, fear, anger). Throughout the years many AAR reviews have been published. However, most of them do not include a detailed analysis of ARSs and the motivation behind them. This paper aims to fill this knowledge gap by performing a Systematic Literature Review (SLR) of Computer Science papers that propose a Physiological-signal-based AAR (PAAR) system. We explore how researchers discuss and choose an ARS and whether they base it on actual psychological theories. Eligible papers are retrieved from 4 databases: Web Of Science, IEEExplore, Scopus, and ACM Digital Library. Due to time limitations, the review is done rapidly and some additional search constraints are applied for feasibility. The most significant restrictions are: considering papers published since 2020 and performing experiments on specific benchmarking datasets. We take these constraints and their possible impact into consideration when interpreting the results. The presented review procedure can be stripped from the additional filters and reused for a full review. In total 115 papers are processed. The majority of papers introduce an EEG-based emotion recognition system. The analysis of the extracted information reveals that dimensional ARSs, in particular, Valence/Arousal model is the most popular. Moreover, authors often choose to reduce the dimensions to high/low values. Categorical ARSs are less frequent and usually are adopted from the dataset. Lastly, the authors do not provide extensive motivation for the choice of ARS and rarely refer to psychological theories.