The AffectToolbox: Affect Analysis for Everyone

In the field of affective computing, where research continually advances at a rapid pace, the demand for user-friendly tools has become increasingly apparent. In this paper, we present the AffectToolbox, a novel software system that aims to support researchers in developing affect-sensitive studies and prototypes. The proposed system addresses the challenges posed by existing frameworks, which often require profound programming knowledge and cater primarily to power-users or skilled developers. Aiming to facilitate ease of use, the AffectToolbox requires no programming knowledge and offers its functionality to reliably analyze the affective state of users through an accessible graphical user interface. The architecture encompasses a variety of models for emotion recognition on multiple affective channels and modalities, as well as an elaborate fusion system to merge multi-modal assessments into a unified result. The entire system is open-sourced and will be publicly available to ensure easy integration into more complex applications through a well-structured, Python-based code base - therefore marking a substantial contribution toward advancing affective computing research and fostering a more collaborative and inclusive environment within this interdisciplinary field

Browser fingerprinting: how to protect machine learning models and data with differential privacy?

As modern communication networks grow more and more complex, manually maintaining an overview of deployed soft- and hardware is challenging. Mechanisms such as fingerprinting are utilized to automatically extract information from ongoing network traffic and map this to a specific device or application, e.g., a browser. Active approaches directly interfere with the traffic and impose security risks or are simply infeasible. Therefore, passive approaches are employed, which only monitor traffic but require a well-designed feature set since less information is available. However, even these passive approaches impose privacy risks. Browser identification from encrypted traffic may lead to data leakage, e.g., the browser history of users. We propose a passive browser fingerprinting method based on explainable features and evaluate two privacy protection mechanisms, namely differentially private classifiers and differentially private data generation. With a differentially private Random Decision Forest, we achieve an accuracy of 0.877. If we train a non-private Random Forest on differentially private synthetic data, we reach an accuracy up to 0.887, showing a reasonable trade-off between utility and privacy

Browser Fingerprinting: How to Protect Machine Learning Models and Data with Differential Privacy?

As modern communication networks grow more and more complex, manually maintaining an overview of deployed soft- and hardware is challenging. Mechanisms such as fingerprinting are utilized to automatically extract information from ongoing network traffic and map this to a specific device or application, e.g., a browser. Active approaches directly interfere with the traffic and impose security risks or are simply infeasible. Therefore, passive approaches are employed, which only monitor traffic but require a well-designed feature set since less information is available. However, even these passive approaches impose privacy risks. Browser identification from encrypted traffic may lead to data leakage, e.g., the browser history of users. We propose a passive browser fingerprinting method based on explainable features and evaluate two privacy protection mechanisms, namely differentially private classifiers and differentially private data generation. With a differentially private Random Decision Forest, we achieve an accuracy of 0.877. If we train a non-private Random Forest on differentially private synthetic data, we reach an accuracy up to 0.887, showing a reasonable trade-off between utility and privacy

Reversible Barrier Switching of ZnO/RuO₂ Schottky Diodes

The current-voltage characteristics of ZnO/RuO₂ Schottky diodes prepared by magnetron sputtering are shown to exhibit a reversible hysteresis behavior, which corresponds to a variation of the Schottky barrier height between 0.9 and 1.3 eV upon voltage cycling. The changes in the barrier height are attributed to trapping and de-trapping of electrons in oxygen vacancies

MultiMediate '22: Backchannel Detection and Agreement Estimation in Group Interactions

Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, automatic analysis of backchannels in group interactions has been largely neglected so far. The MultiMediate challenge addresses, for the first time, the tasks of backchannel detection and agreement estimation from backchannels in group conversations. This paper describes the MultiMediate challenge and presents a novel set of annotations consisting of 7234 backchannel instances for the MPIIGroupInteraction dataset. Each backchannel was additionally annotated with the extent by which it expresses agreement towards the current speaker. In addition to a an analysis of the collected annotations, we present baseline results for both challenge tasks.Comment: ACM Multimedia 202

MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions

Automatic analysis of human behaviour is a fundamental prerequisite for the creation of machines that can effectively interact with- and support humans in social interactions. In MultiMediate'23, we address two key human social behaviour analysis tasks for the first time in a controlled challenge: engagement estimation and bodily behaviour recognition in social interactions. This paper describes the MultiMediate'23 challenge and presents novel sets of annotations for both tasks. For engagement estimation we collected novel annotations on the NOvice eXpert Interaction (NOXI) database. For bodily behaviour recognition, we annotated test recordings of the MPIIGroupInteraction corpus with the BBSI annotation scheme. In addition, we present baseline results for both challenge tasks.Comment: ACM MultiMedia'2

α1A-Adrenergic Receptor-Directed Autoimmunity Induces Left Ventricular Damage and Diastolic Dysfunction in Rats

BACKGROUND: Agonistic autoantibodies to the alpha(1)-adrenergic receptor occur in nearly half of patients with refractory hypertension; however, their relevance is uncertain. METHODS/PRINCIPAL FINDINGS: We immunized Lewis rats with the second extracellular-loop peptides of the human alpha(1A)-adrenergic receptor and maintained them for one year. Alpha(1A)-adrenergic antibodies (alpha(1A)-AR-AB) were monitored with a neonatal cardiomyocyte contraction assay by ELISA, and by ERK1/2 phosphorylation in human alpha(1A)-adrenergic receptor transfected Chinese hamster ovary cells. The rats were followed with radiotelemetric blood pressure measurements and echocardiography. At 12 months, the left ventricles of immunized rats had greater wall thickness than control rats. The fractional shortening and dp/dt(max) demonstrated preserved systolic function. A decreased E/A ratio in immunized rats indicated a diastolic dysfunction. Invasive hemodynamics revealed increased left ventricular end-diastolic pressures and decreased dp/dt(min). Mean diameter of cardiomyocytes showed hypertrophy in immunized rats. Long-term blood pressure values and heart rates were not different. Genes encoding sarcomeric proteins, collagens, extracellular matrix proteins, calcium regulating proteins, and proteins of energy metabolism in immunized rat hearts were upregulated, compared to controls. Furthermore, fibrosis was present in immunized hearts, but not in control hearts. A subset of immunized and control rats was infused with angiotensin (Ang) II. The stressor raised blood pressure to a greater degree and led to more cardiac fibrosis in immunized, than in control rats. CONCLUSIONS/SIGNIFICANCE: We show that alpha(1A)-AR-AB cause diastolic dysfunction independent of hypertension, and can increase the sensitivity to Ang II. We suggest that alpha(1A)-AR-AB could contribute to cardiovascular endorgan damage

Patterns of antibody responses to nonviral cancer antigens in head and neck squamous cell carcinoma patients differ by human papillomavirus status

There have been hints that nonviral cancer antigens are differentially expressed in human papillomavirus (HPV)-positive and HPV-negative head and neck squamous cell carcinoma (HNSCC). Antibody responses (AR) to cancer antigens may be used to indirectly determine cancer antigen expression in the tumor using a noninvasive and tissue-saving liquid biopsy. Here, we set out to characterize AR to a panel of nonviral cancer antigens in HPV-positive and HPV-negative HNSCC patients. A fluorescent microbead multiplex serology to 29 cancer antigens (16 cancer-testis antigens, 5 cancer-retina antigens and 8 oncogenes) and 29 HPV-antigens was performed in 382 HNSCC patients from five independent cohorts (153 HPV-positive and 209 HPV-negative). AR to any of the cancer antigens were found in 272/382 patients (72%). The ten most frequent AR were CT47, cTAGE5a, c-myc, LAGE-1, MAGE-A1, -A3, -A4, NY-ESO-1, SpanX-a1 and p53. AR to MAGE-A3, MAGE-A9 and p53 were found at significantly different prevalences by HPV status. An analysis of AR mean fluorescent intensity values uncovered remarkably different AR clusters by HPV status. To identify optimal antigen selections covering a maximum of patients with ≤10 AR, multiobjective optimization revealed distinct antigen selections by HPV status. We identified that AR to nonviral antigens differ by HPV status indicating differential antigen expression. Multiplex serology may be used to characterize antigen expression using serum or plasma as a tissue-sparing liquid biopsy. Cancer antigen panels should address the distinct antigen repertoire of HPV-positive and HPV-negative HNSCC

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO