by jsendak | Feb 27, 2024 | Computer Science
arXiv:2402.15513v1 Announce Type: new
Abstract: Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address this ambiguity, we evaluated the generalizability of physiological features that have been shown to be correlated with anxiety and stress to high-arousal emotions. Specifically, we examine features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from the following three datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. We aim to understand whether these features are specific to anxiety or general to other high-arousal emotions through a statistical regression analysis, in addition to a within-corpus, cross-corpus, and leave-one-corpus-out cross-validation across instances of stress and arousal. We used the following classifiers: Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of the aforementioned models. We found that models trained on an arousal dataset perform relatively well on a previously unseen stress dataset, and vice versa. Our experimental results suggest that the evaluated models may be identifying emotional arousal instead of stress. This work is the first cross-corpus evaluation across stress and arousal from ECG and EDA signals, contributing new findings about the generalizability of stress detection.
Expert Commentary: Evaluating the Generalizability of Physiological Features in Stress Detection
In recent years, machine learning (ML) techniques have shown promise in detecting anxiety and stress using physiological signals. However, it is important to determine whether these ML models are truly learning features specific to stress or if they are detecting a more general state of high arousal. This article presents a study that aims to address this ambiguity by evaluating the generalizability of physiological features associated with anxiety and stress to other high-arousal emotions.
The study examines features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from three different datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. By analyzing these features, the researchers seek to understand whether they are specific to anxiety or applicable to other high-arousal emotions.
To evaluate the generalizability of these features, the researchers conducted a statistical regression analysis in addition to various cross-validation techniques. They used several classifiers, including Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of these models to train and test their models on different combinations of stress and arousal datasets.
The findings from this study provide valuable insights into the nature of stress detection through physiological signals. The results indicate that models trained on datasets related to arousal perform well on stress datasets, and vice versa. This suggests that the evaluated models may be identifying emotional arousal rather than specifically detecting stress.
This is a significant contribution to the field as it is the first cross-corpus evaluation that explores the relationship between stress and arousal using ECG and EDA signals. By highlighting the generalizability of stress detection methods, this work advances our understanding of the broader implications of physiological signal analysis in the field of multimedia information systems.
The concepts explored in this study have significant interdisciplinary relevance. The field of multimedia information systems encompasses various disciplines such as computer science, psychology, and human-computer interaction. By applying machine learning techniques to physiological signals, researchers bridge the gap between these disciplines, paving the way for innovative applications in areas like augmented reality, virtual realities, and artificial reality.
Animations in virtual and augmented reality environments can be intelligently adjusted based on the user’s stress or arousal levels. For example, if a user is becoming overly stressed, the virtual environment can adapt by providing calming visuals or sounds to alleviate their anxiety. Similarly, in artificial reality applications such as medical simulations, the system can respond to the user’s stress levels to provide personalized feedback and guidance.
Overall, this study contributes to the broader field of multimedia information systems by providing insights into the generalizability of stress detection methods and highlighting the interdisciplinary nature of the concepts explored. It opens up possibilities for integrating physiological signal analysis into various multimedia applications, paving the way for more immersive and personalized experiences in virtual, augmented, and artificial realities.
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by jsendak | Feb 27, 2024 | Computer Science
The Sixth International Workshop on Languages for Modelling Variability (MODEVAR 2024) was recently held in Bern, Switzerland on February 6th, 2024. This workshop is a significant event for researchers and practitioners in the field of variability modelling, as it provides a platform for exchanging ideas, discussing challenges, and exploring new advancements in the area.
Importance of Variability Modelling
Variability modelling plays a crucial role in various domains, including software development, product line engineering, and system design. It enables organizations to manage and represent the diverse features and options that can be configured or customized in a system or product.
Having a well-defined and robust variability modelling approach helps organizations to efficiently handle the complexity of variability, thereby enhancing product quality, reducing development time, and increasing customer satisfaction. Therefore, it is imperative to have a deep understanding of the challenges and opportunities in this field.
Higlights from MODEVAR 2024
The MODEVAR 2024 workshop provided a platform for researchers and industry experts to present their latest findings and share their experiences in variability modelling. The workshop featured several informative sessions and discussions on a range of topics.
New Approaches and Techniques
A key highlight of MODEVAR 2024 was the presentation of new approaches and techniques in variability modelling. Researchers showcased innovative techniques for representing, managing, and reasoning about variability in complex systems and products. These advancements have the potential to revolutionize the way organizations handle variability and improve their product development processes.
Industry Case Studies
The workshop also featured insightful industry case studies that demonstrated the practical application of variability modelling in real-world scenarios. These case studies provided valuable insights into the challenges faced by organizations and how they successfully implemented variability modelling techniques to overcome these challenges.
Open Discussion and Future Directions
Furthermore, MODEVAR 2024 included open discussions and brainstorming sessions on the future directions of variability modelling. Experts from academia and industry shared their visions and perspectives on emerging trends, research priorities, and potential collaborations. This collaborative approach ensures that the research in this field aligns with the practical needs of the industry.
What’s Next for Variability Modelling?
As we look ahead, there are several potential future developments in variability modelling that may arise from the discussions and insights shared at the MODEVAR 2024 workshop. One important direction could be the integration of artificial intelligence and machine learning techniques in variability modelling to automate and optimize the modelling process.
Another potential advancement could be the development of standardized modelling languages and tools that enable seamless integration of variability modelling across different phases of the software development lifecycle. This would enhance communication and collaboration among stakeholders, leading to more efficient and effective variability management.
Overall, the MODEVAR 2024 workshop has played a pivotal role in advancing the field of variability modelling. The exchange of knowledge and ideas among researchers and industry professionals has paved the way for exciting developments in the years to come, and it will be fascinating to witness the impact of these advancements on various domains.
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by jsendak | Feb 26, 2024 | Computer Science
arXiv:2402.14947v1 Announce Type: cross
Abstract: Governments use propaganda, including through visual content — or Politically Salient Image Patterns (PSIP) — on social media, to influence and manipulate public opinion. In the present work, we collected Telegram post-history of from 989 Russian milbloggers to better understand the social and political narratives that circulated online in the months surrounding Russia’s 2022 full-scale invasion of Ukraine. Overall, we found an 8,925% increase (p<0.001) in the number of posts and a 5,352% increase (p<0.001) in the number of images posted by these accounts in the two weeks prior to the invasion. We also observed a similar increase in the number and intensity of politically salient manipulated images that circulated on Telegram. Although this paper does not evaluate malice or coordination in these activities, we do conclude with a call for further research into the role that manipulated visual media has in the lead-up to instability events and armed conflict.
Expert Commentary: Analyzing Politically Salient Image Patterns (PSIP) on Social Media
Introduction
In today’s digital age, social media has become a powerful platform for governments and political entities to disseminate their messages and shape public opinion. A recent study titled “Politically Salient Image Patterns (PSIP) on Social Media: A Case Study of the Russian Invasion of Ukraine” focuses on the use of visual content or PSIP on Telegram, a popular messaging app. Through the analysis of data from 989 Russian milbloggers, the study aims to shed light on the social and political narratives that emerged surrounding Russia’s invasion of Ukraine in 2022.
Understanding the Impact of Visual Content
The study highlights the significant role played by visual content in influencing public opinion. Images possess the power to evoke emotions, convey messages, and shape narratives more effectively than text alone. As such, understanding the patterns and messages within PSIP is crucial in comprehending the impact of propaganda on social media platforms.
Multi-Disciplinary Nature of PSIP Analysis
An analysis of PSIP involves a multidisciplinary approach, encompassing various fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
- Multimedia Information Systems: The study relies on data collection and analysis methodologies used in the field of multimedia information systems. By examining post-history on Telegram, researchers gain insights into the types of visual content employed by milbloggers.
- Animations: The use of animated images, GIFs, or short videos can be a powerful method to captivate and influence viewers. Analyzing PSIP can reveal if such techniques were utilized during Russia’s invasion of Ukraine.
- Artificial Reality and Virtual Realities: The manipulation of visual content can extend beyond the physical realm through the use of augmented reality or virtual reality technologies. By examining PSIP, researchers can assess whether such technologies were harnessed to amplify propaganda efforts.
Implications and Future Directions
The findings of this study provide valuable insights into the role of PSIP in shaping social and political narratives during Russia’s invasion of Ukraine. Understanding the techniques employed by governments and political entities in manipulating public opinion is crucial for safeguarding the integrity of digital platforms and democracy at large.
Furthermore, this research opens up avenues for future studies. By expanding the dataset to include different regions or conflicts, researchers can compare the strategies employed and identify common patterns or approaches utilized in different geopolitical contexts.
In conclusion, the analysis of Politically Salient Image Patterns (PSIP) on social media offers a deeper understanding of the influence of visual content in shaping public opinion. The interdisciplinary nature of this analysis connects various fields and provides a comprehensive perspective on the dynamics at play in this digital age.
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by jsendak | Feb 26, 2024 | Computer Science
The article provides a comprehensive overview of the Bin Packing Problem, highlighting its significance in discrete optimization and its relevance to real-world problems. It acknowledges that various theoretical and practical tools have been used to address this problem, with the most effective approaches being based on Linear Programming. Furthermore, it mentions how Constraint Programming can be valuable when the Bin Packing Problem is part of a larger problem.
One interesting aspect addressed in this work is the exploration of how GPUs (Graphics Processing Units) can enhance the propagation algorithm of the Bin Packing constraint. The article presents two approaches motivated by knapsack reasoning and alternative lower bounds, respectively. It is crucial to mention that GPUs are known for their high parallel processing power, which makes them potentially suitable for improving the performance of certain algorithms.
By evaluating the implementations of these GPU-accelerated approaches, the research team compares them to state-of-the-art techniques on different benchmarks from the literature. The results obtained suggest that the GPU-accelerated lower bounds offer a promising alternative for tackling large instances of the Bin Packing Problem.
This study contributes to the field of discrete optimization by introducing GPU-accelerated techniques for enhancing the Bin Packing constraint’s propagation algorithm. By leveraging the parallel processing capabilities of GPUs, these approaches show potential for significantly improving the efficiency and scalability of solving large instances of the problem.
In terms of future developments, it would be interesting to see how these GPU-accelerated techniques could be further optimized and extended. Additionally, it would be valuable to explore their applicability to other optimization problems and investigate how different problem characteristics may influence their effectiveness.
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by jsendak | Feb 24, 2024 | Computer Science
Analyzing Prediction Markets and Their Limitations
Prediction markets have proven to be valuable tools for estimating probabilities of claims that can be resolved at a specific point in time. These markets excel in predicting uncertainties related to real-world events and even values of primitive recursive functions. However, their direct application to questions without a fixed resolution criterion is challenging, leading to predictions about whether a sentence will be proven rather than its truth.
When it comes to questions that lack a fixed resolution criterion, a different approach is necessary. Such questions often involve countable unions or intersections of more basic events or are represented as First-Order-Logic sentences on the Arithmetical Hierarchy. In more complex cases, they may even transcend First-Order Logic and fall into the realm of hyperarithmetical sentences.
In this paper, the authors propose an alternative approach to betting on events without a fixed resolution criterion using options. These options can be viewed as bets on the outcome of a “verification-falsification game,” offering a new framework for addressing logical uncertainty. This work stands in contrast to the existing framework of Garrabrant induction and aligns with the constructivist stance in the philosophy of mathematics.
By introducing the concept of options in prediction markets, this research has far-reaching implications for both philosophy and mathematical logic. It provides a fresh perspective on addressing uncertainties in a broader range of questions and challenges the traditional methods by offering an alternative framework that accommodates events lacking fixed resolution criteria. These findings encourage further exploration and could lead to significant advancements in our understanding and utilization of prediction markets.
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by jsendak | Feb 23, 2024 | Computer Science
arXiv:2402.14326v1 Announce Type: new
Abstract: Offloading computing to edge servers is a promising solution to support growing video understanding applications at resource-constrained IoT devices. Recent efforts have been made to enhance the scalability of such systems by reducing inference costs on edge servers. However, existing research is not directly applicable to pixel-level vision tasks such as video semantic segmentation (VSS), partly due to the fluctuating VSS accuracy and segment bitrate caused by the dynamic video content. In response, we present Penance, a new edge inference cost reduction framework. By exploiting softmax outputs of VSS models and the prediction mechanism of H.264/AVC codecs, Penance optimizes model selection and compression settings to minimize the inference cost while meeting the required accuracy within the available bandwidth constraints. We implement Penance in a commercial IoT device with only CPUs. Experimental results show that Penance consumes a negligible 6.8% more computation resources than the optimal strategy while satisfying accuracy and bandwidth constraints with a low failure rate.
Analysis of Penance: Edge Inference Cost Reduction Framework
In this article, the authors introduce Penance, a new framework for reducing edge inference costs in video semantic segmentation (VSS) tasks. With the growing demand for video understanding applications on resource-constrained IoT devices, offloading computing to edge servers has become a promising solution. However, existing research is not directly applicable to pixel-level vision tasks like VSS, mainly due to the dynamic nature of video content, which leads to fluctuating accuracy and segment bitrate.
Penance addresses this challenge by leveraging the softmax outputs of VSS models and the prediction mechanism of H.264/AVC codecs. By optimizing model selection and compression settings, Penance aims to minimize the inference cost while meeting the required accuracy within the available bandwidth constraints. It is worth noting that Penance is implemented on a commercial IoT device with only CPUs, making it accessible to a wide range of devices.
The multi-disciplinary nature of this work is evident in its integration of computer vision (specifically VSS), video codecs (H.264/AVC), and edge computing. It combines knowledge from these diverse domains to develop a novel solution that addresses the specific challenges faced in edge inference for VSS.
When considering the wider field of multimedia information systems, Penance contributes to the efficiency and scalability of video understanding applications on IoT devices. By reducing inference costs at the edge, it enables resource-constrained devices to perform complex vision tasks like semantic segmentation without relying heavily on cloud resources. This can lead to improved response times, reduced latency, and increased privacy.
Furthermore, Penance has relevance to various aspects of multimedia technologies such as animations, artificial reality, augmented reality, and virtual realities. These technologies often involve real-time video processing and analysis, where efficient edge inference is crucial for a seamless and immersive user experience. By optimizing inference costs, Penance can support the delivery of rich multimedia content in these applications without compromising on performance.
In conclusion, Penance is an innovative framework that addresses the challenges of edge inference for video semantic segmentation tasks. Its integration of various technologies and its impact on the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities make it a significant contribution to the advancement of edge computing in the context of video understanding applications.
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