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Exercise effects on symptoms of depression and anxiety vary by patient, clinical, and intervention characteristics in cancer survivors:Results from pooled analyses of individual participant data of 26 RCTs
Get PDFPurpose: This study aimed to investigate whether socio-demographic, clinical, and intervention-related variables moderate the effects of exercise on depression and anxiety symptoms in cancer survivors. Methods: Data from 26 RCTs in the POLARIS database were analyzed using a one-step individual participant data (IPD) meta-analytic approach with linear mixed models to assess exercise effects on depression and anxiety symptoms (z-scores). Interaction terms were added to these models to explore moderators. Results are presented as betas (corresponding to Cohen’s d effect size). Results: Albeit statistically significant, exercise demonstrated negligible effects on symptoms of depression (β = − 0.11; 95% CI = − 0.16; − 0.06) and anxiety (β = − 0.07; 95% CI = − 0.12; − 0.02) compared to controls. The effects of exercise interventions on depressive symptoms were larger for patients who were not living with a partner (β = − 0.23; 95% CI = − 0.35; − 0.11), had a low/medium education level (β = − 0.14; 95% CI = − 0.21; − 0.07), and who had moderate-to-severe symptoms of depression at baseline (β = − 0.30; 95% CI = − 0.43; − 0.16). Patients with moderate-to-severe symptoms of depression at baseline combined with those not living with a partner or a low/medium education level yielded the largest effect size through exercise (β = − 0.61; 95% CI = − 0.89; − 0.33 and β = − 0.37; 95% CI = − 0.57; − 0.17, respectively). Effects on anxiety symptoms were larger for patients with moderate-to-severe symptoms of anxiety at baseline (β = − 0.17; 95% CI = − 0.32; − 0.01) compared to those with no-to-mild symptoms. Sex, age, cancer type, BMI, and intervention-related variables did not moderate the exercise effects. Conclusion: The findings of this study highlight the heterogeneous response to exercise interventions across various patient subgroups. Patients with moderate-to-severe anxiety or depression, those with a low/medium education, and those not living together with a partner may particularly benefit.</p
Toward Automatically Generating User-Specific Recovery Procedures after Windows Malware Infections
Get PDFDespite significant advancements in proactive malware detection and prevention, complete prevention of malware infiltration remains unattainable. Once malware is present on a system, it can make persistent changes that affect its stability, making user-specific recovery post-infection an important problem to address. Current solutions involve extensive monitoring to precisely pinpoint the changes that malware has made, which are impractical for home environments due to their high resource demands. This paper introduces a prototype for automatically generating userspecific malware recovery procedures that fully operates post-mortem. By leveraging forensic data collected on Windows by default, we replicate the original conditions under which the malware executed in a sandbox and automatically infer the exact system resources that the malware changed without imposing additional performance burdens on the user's machine. We test a prototype against 894 realworld malware samples and three real-world, environment-sensitive malware campaigns, and achieve a full recovery rate of 51.3 % even with no additional monitoring enabled. We conclude by sharing insights on the importance of machine replication and sandbox configurability in future malware research.</p
SoK:Automated TTP Extraction from CTI Reports – Are We There Yet?
Get PDFCyber Threat Intelligence (CTI) plays a critical role in sharing knowledge about new and evolving threats. With the increased prevalence and sophistication of threat actors, intelligence has expanded from simple indicators of compromise to extensive CTI reports describing high-level attack steps known as Tactics, Techniques and Procedures (TTPs). Such TTPs, often classified into the ontology of the MITRE ATT&CK framework, make CTI significantly more valuable, but also harder to interpret and automatically process. Natural Language Processing (NLP) makes it possible to automate large parts of the knowledge extraction from CTI reports; over 40 papers discuss approaches, ranging from named entity recognition over embedder models to generative large language models. Unfortunately, existing solutions are largely incomparable as they consider decisively different and constrained settings, rely on custom TTP ontologies, and use a multitude of custom, inaccessible CTI datasets. We take stock, systematize the knowledge in the field, and empirically evaluate existing approaches in a unified setting for fair comparisons. We gain several fundamental insights, including (1) the finding of a kind of performance limit that existing approaches seemingly cannot overcome as of yet, (2) that traditional NLP approaches (possibly counterintuitively) outperform modern embedder-based and generative approaches in realistic settings, and (3) that further research on understanding inherent ambiguities in TTP ontologies and on the creation of qualitative datasets is key to take a leap in the field
Collecting Object-level Affordance for RGBD Datasets
No full textAccurate interpretation of the environment is both essential to automated robots and highly beneficial for teleoperated robots. Going beyond obstacle recognition, interpreting the semantics of the environment and the actions it affords, enables robots to interact with environments made for humans in a human-like manner. This paper describes the collection of affordance labels at the object level for multiple indoors datasets, to train computer vision algorithms for detecting object affordances in indoor spaces. It is a first step towards determining high level “semantic” affordances, to allow reasoning about what to do with objects, rather than “functional” affordances, which allow reasoning about how to use the same. A baseline model is provided, which highlights the value of the affordance labels in a variety of robotics applications
Smart Business Technologies:21st International Conference, ICSBT 2024, Dijon, France, July 9–11, 2024, Revised Selected Papers
Get PDFThis book constitutes the refereed post proceedings of the 21st International Conference on Smart Business Technologies, ICSBT 2024, which took place in Dijon, France, during July 9–10, 2024.The 7 papers included focus on all aspects of foundational technological topics, such as technology platforms, internet of things, artificial intelligence, data science and web services, and application area needs and requirements, such as business processes, business intelligence, digital twins, value setting, and business strategy for manufacturing, service and trade domains
Exploring the Frontiers of Technology in Forensic Psychiatric Youth Care:Ethical Challenges
Get PDFTechnologies such as virtual reality, wearables, and mobile apps have the potential to improve forensic psychiatric treatment of youths. Meanwhile, these technological advancements have given rise to new, complex ethical challenges. Paying attention to ethics is especially relevant in forensic psychiatric youth settings because of the often coercive context of treatment and the vulnerable patient population. The goal of this viewpoint paper is to identify and discuss important ethical challenges regarding the use of technology in forensic psychiatric youth care. In line with approaches within the domain of ethics, an analysis of relevant scholarly literature was used for this viewpoint paper. First, a general description, an overview of research on effectiveness, and examples from practice are provided for six technologies that can be used in forensic psychiatric youth care: virtual reality, internet-based interventions, mobile apps, wearables, neurotechnology, and games. Next, ethical challenges that are relevant for these technologies are explored, related to informed consent, privacy and data security, reliability and validity, equity, accessibility and usability, undesirable side effects, acceptability of content, persuasiveness, and evidence-based interventions. Interdisciplinary collaboration between researchers, patients, therapists, ethicists, technology developers, and forensic organizations is recommended for timely identification of ethical challenges and suitable solutions. We suggest that patients and therapists should be actively involved throughout all phases of the process, from development of the technology via co-creation to active participation in implementation and evaluation in practice.</p
Embracing Disagreement in Text Classification During Training and Prediction
Get PDFDisagreement between annotators is often viewed as a sign of low data quality. In reality, the idea that a single underlying “ground truth” exists is often simply not true. Still pursuing it, e.g., by majority voting, can remove valuable nuances and perspectives from the data, especially for inherently subjective tasks. Recent research increasingly started to leverage disagreement between annotators by using unaggregated annotations for the training of models. More often than not, these models are still used to predict one single output. In order to truly embrace different perspectives, they should not just be considered during the training, but also when making predictions and presenting them. This chapter compares three strategies to leverage disagreement for text classification: a probability-based multi-label method, an ensemble system, and instruction tuning. The approaches were evaluated on hate speech and abusive conversation detection. To compare the performance of embracing disagreement versus only using majority label during the training, we conducted an online survey. Additionally, the survey also investigated whether potential users prefer a single prediction or a multi-label distribution based on different perspectives. The results show that in hate speech detection, the multi-label method performs best, even though it is less complex than the other two approaches. In abusive conversation detection, instruction tuning achieves the best performance given the sparse available data. The results of the survey indicate that the output for the multi-label models are considered a better representation of the texts than a single-label model
Looking in a smart mirror:A phenomenological inquiry into self-experience and how it is shaped by smart personalised technology
No full textThis dissertation explores how the influence of smart personalised technology (SPT) on users’ self-experience can be understood. SPT refers to AI-driven systems that interact with users through adaptive personalisation algorithms. The first part develops a phenomenological account of self-experience, understood as experience of self, drawing on Husserl’s concept of intentionality, Merleau-Ponty’s reflections on embodiment, and Heidegger’s focus on being-in-the-world. Rather than an experience of one’s inner core or sense of self, self-experience is described as an intentional recognition of self, enabled by background meaning in the form of prior experiences and cultural standards.The second part examines how SPT shapes this self-experience. By combining an enactivist perspective on self-experience with the concept of technological affordances, the study shows how SPT subtly reshapes both user behaviour and self-experience. Successful personalization, such as in social media, turns SPT into a smart mirror: it creates a so-called echo chamber that not only reflects user behaviour but also modifies it. Looking into this mirror influences users’ self-experience, turning the echo chamber into an ego chamber where certain self-experiences recur—a process referred to as a form of algorithmic gaslighting. The likelihood of such ego chambers appears particularly high among young people who are still in the process of forming their identity.These insights are relevant not only for users but also for parents, educators, and professionals seeking to guide young people in their use of social media, and for designers aiming to create more transparent and participatory forms of personalization. Finally, the study contributes to a theoretical framework for future empirical research on the effects of social media use
Additive manufacturing of functionally graded foams for acoustic insulation and absorption
No full textAcoustic foams and foam-filled metamaterials excel at sound absorption but typically exhibit a low sound transmission loss (STL). Foams that precisely integrate tunable shapes, density gradients, and transitions between open-cell and closed-cell regions have the potential to simultaneously enhance absorption and STL as compared to uniform foams. However, fabrication of these materials is challenging even for small samples that consist of a few thousand unit cells. Here we show additive manufacturing of functionally graded foams via direct bubble writing, a method for generating and stacking bubbles into three-dimensional solid foam constructs with a throughput up to 100 ml/min. The density, pore morphology, flow resistivity, and dynamic mechanical behavior of homogeneous and graded foams are characterized. As a reference case, the STL and absorption of homogeneous samples were tested in an impedance tube for frequencies between 200 Hz and 2600 Hz. Graded samples were subsequently evaluated, revealing strongly enhanced peaks in STL (up to ∼ 68 dB) for closed-cell foams with a low-density core sandwiched between two high-density layers. A high-density core sandwiched between two low-density layers especially broadens the frequency range with high sound absorption and still enhances the STL. These results show that functionally graded closed-cell foams are a promising route towards structure-induced dissipation as required for materials that exhibit a high absorption and a high STL.</p