Miracle-dz/UniFAR-FTUs huggingface dataset:
Einsatz von erweiterten Realitäten (XR) in der Thoraxchirurgie

Zusammenfassung Erweiterte Realitäten (XR) mit den Unterbegriffen Virtual Reality (VR), Augmented Reality (AR) und Mixed Reality (MR) beschreiben interaktive und immersive Technologien, welche die reelle Welt durch digitale Elemente ersetzen oder diese um solche nahtlos erweitern. Die XR bieten somit ein sehr breites mögliches Anwendungsspektrum in der Medizin. In der Chirurgie und insbesondere in der Thoraxchirurgie lassen sich XR-Technologien für Anwendungen der Behandlungsplanung, Navigation, Ausbildung und Patienteninformationen nutzbar machen. Solche Anwendungsfälle befinden sich zunehmend in der Erprobung und müssen hinsichtlich ihrer Wertigkeit evaluiert werden. Wir geben einen Überblick über den Status quo der technischen Entwicklung, aktuelle chirurgische Einsatzgebiete der XR und blicken in die Zukunft der medizinischen XR-Landschaft mit Integration von künstlicher Intelligenz (KI).
Denoising method of explosion vibration signal by deep learning

In the explosion experiment, the explosion vibration signal is the important information to evaluate the explosion equivalent or to locate the explosion point. However, the environment at the scene of the explosion is very complicated. The explosion vibration signal collected by the Distributed fiber Acoustic Sensor (DAS) system not only contains optical noise, but also contains a lot of non-stationary and non-Gaussian environmental background noise. In order to solve this issue, feed-forward denoising convolutional neural network (DnCNN) are used to denoise the signals during the noise suppression of explosion vibration signals. The initial application of this network was to purge images of additive Gaussian white noise. In order to make DnCNN adapt to the de-noising of explosion vibration data, we have performed numerous optimization tasks. Firstly, the input data is processed by an reversible downsampling factor in this paper, expanding the network’s perceptual field while also making training easier. Secondly, rather than using Gaussian white noise, the training set of DnCNN is rebuilt using background noise collected in the actual environment. Finally, the DnCNN parameters that impact network performance are modified and improved. From the experimental results in this paper, the DnCNN can effectively suppress the noise in the explosion vibration signal and preserve the effective signal.
Assessing health risk associated with airborne polycyclic aromatic hydrocarbons by chemometrics and toxic equivalency factors

Some polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in air and have been implicated as carcinogenic materials. Therefore, literature is replete with studies that are focused on their occurrence and profiles in indoor and outdoor air samples. However, because the relative potency of individual PAHs vary widely, health risks associated with the presence of PAHs in a particular environment cannot be extrapolated directly from the concentrations of individual PAHs in that environment. In addition, database on the potency of PAH mixtures is currently limited. In this paper, we have utilized multi-criteria decision making methods (MCDMs) to simultaneously correlate PAH-related health risk in some microenvironments to the concentration levels, ethoxyresorufin-O-deethylase (EROD) activity induction equivalency factors and toxic equivalency factors (TEFs) of PAHs found in those microenvironments. The results showed that the relative risk associated with PAHs in different air samples depends on the index used. Nevertheless, this approach offers a promising tool that could help identify microenvironments of concern and assist the prioritisation of control strategies.
MPIGDB: A Flexible Debugging Infrastructure for MPI Programs

The advent of flexible science workflows that span traditional HPC, the cloud, and beyond require more than ever efficient, scalable, portable, feature-ful debugging tools. This work presents the design and implementation of MPIGDB a flexible debugging infrastructure for MPI programs. MPIGDB is designed to be highly capable at scale, available across platforms, easily interactive, and extensible way to debug distributed MPI programs. This work demonstrates that scalability of this our approach to 128 processes on debugging memory access violations in a heat diffusion code as an example use case, while providing features competitive with proprietary debugging tools such as GPU kernel debugging, mixed language support, and scripting abilities. Together these tools allow users to quickly debug challenges where-ever their science is run.
Accessible Document Layout: An Interface for 2D Tactile Displays

Reading and processing documents is a challenging task for people with blindness and visual impairment (BVI). Despite various methods and research being conducted on converting text and multimedia content to an accessible format, the task of layout navigation remains under-explored. Within a document, a demanding task for people with BVI is to understand the layout and navigate, so as to form a proper reading order. This is specifically challenging in documents with complex layouts, such as those with multiple columns and arbitrarily distributed elements. Traditional methods, such as screen readers, can be limited in their ability to navigate complex layouts and unreliable for various types of documents such as newspapers and slides. A new tactile layout reader has been developed in this work to enhance document navigation by providing pinpointed audio-tactile explanations. Our approach, as shown in Figure 1, uses state-of-the-art AI object detection to create a high-level abstract of the document structure, and an optimized interface suitable for both 2D refreshable displays and braille-embossed documents, with both audio and tactile representations. In order to enable the research community and contribute to the field, we will make our codes, models, and annotated data publicly available.
A Preliminary Cross-Language Investigation of Empathy in # Disability Discourse on Twitter

Existing research targeting disability on social media has looked primarily at the English language across the Global North. This has caused significant data gaps in the understanding of disability awareness and cultural mindsets elsewhere. This work presents a cross-language analysis of English and Arabic #disability tweets over a period of time. A dictionary of words was adopted to help in understanding the linguistics surrounding the term ”empathy”. The finding suggests that disability mentions in English tweets have slightly more negative empathetic emotions than those found in Arabic tweets. However, more investigations are needed to explore cultural variations towards empathy and disability, and how they differ linguistically in online conversations.
Explainable AI for Medical Image Processing: A Study on MRI in Alzheimer’s Disease

Alzheimer’s disease is the most common type of dementia, characterised by memory-related brain changes that impair the patient’s cognitive abilities. Early detection of this disease is critical from a clinical point of view to increase the chances of treating a patient at risk of further cognitive degeneration. The rate of development and expansion in the field of deep learning for medical analysis is rapidly increasing alongside the increased incidences of neurodegenerative diseases. Machine learning algorithms are often applied to automate tasks and alleviate issues, but modern methods such as neural networks often present as black boxes. In the field of medicine, it is crucial to understand why a machine learning algorithm has made a prediction. In this study, we initially utilise a CNN-based approach for computer vision in the detection of Alzheimer’s disease from the ADNI MRI dataset. On unseen magnetic resonance imaging scans, our algorithm achieves a classification accuracy of 94.96%. We then implement the Local Interpretable Model Agnostic Explanations (LIME) algorithm to reveal visual evidence to support the predictions made by the model, and automatically visualise image segments contributing to predictions via Felzenszwalb’s segmentation algorithm. The objectives of explainable AI in this field are to provide medical professionals with specific, easy-to-understand information to support efficient, consistent, and convenient diagnoses.
SmartFunction: An Immersive Vr System To Assess Attention Using Embodied Cognition

In traditional neuropsychological tests, executive functions (EFs) are typically evaluated using paper and pencil or computer-based sit-down tasks. However, a new assessment framework, the Automated Test of Embodied Cognition (ATEC), has been developed to measure EFs and embodied cognition through physical tasks. This paper proposes integrating the ATEC system with virtual reality (VR) to evaluate and diagnose attention-deficit disorders using embodied cognition (EC) principles. The VR system will utilize Meta Quest 2 VR headsets and controllers with motion sensors to accurately capture users’ physical movements. The collected motion data will be transmitted to a remote server for evaluation through machine learning algorithms. By incorporating VR technology, the proposed solution provides an effective and affordable tool for assessing executive functions and attention-deficit disorders in real-life scenarios. The paper also presents the proposed solution’s system architecture, which involves using a virtual avatar and visual and auditory cues to guide users during physical tasks.
TONIC: A teamwork simulator and digital twin for organizational innovation challenges

Efficient collaboration is the cornerstone of most successful projects and organizations are increasingly seeking tools to optimize their team formation processes. In this paper, we introduce an agent-based simulator designed for team formation in open collaboration settings, specifically innovation hackathons. The tool models and simulates three distinct team formation approaches: bottom-up team formation, where users self-organize with minimal algorithm intervention, top-down team formation, and hybrid team formation. With a broad range of customizable parameters, our tool can serve as a digital twin for collaboration within organizations, permitting organizational decision-makers to design tailored scenarios according to their team formation requirements, utilize existing models or develop their own, and evaluate the effectiveness of their team building approaches prior to real-world implementation.
Developing Brain-Computer Interfaces with Everyone

Throughout its history, the field of brain-computer interfaces (BCIs) has offered people with severe motor disabilities the opportunity to engage with their environments using brain activity alone. Contemporary solutions, however, lack support for reliable evaluation by researchers, independent use by patients and their caregivers, or creative extension by students, artists, and software developers at home. This paper provides preliminary guidance on the integration of research engagement activities into BCI research to enable use at home. Alongside key principles for enabling Research Engagement Always And With Everyone, we present the initial specification for a standardized software ecosystem that could enable the rapid development of high-performance BCI applications on the Open Web. By integrating Open Web technologies alongside engagement activities in current research programs, we argue that participation in the development of a new generation of at-home BCI systems can be widened.
Improved Classification of Mental Workload Using One Dimensional Convolutional Neural Network and SMOTE Technique

Mental Workload (MWL) can be measured as the amount of mental strength necessary to complete a work. Mental workload measurement can help in the reduction of mental stress, tension, anxiety, strain, and worry, in daily life or job-specific tasks. This might have a significant impact on tasks like studies/learning and driving etc. In this paper, the classification of mental workload levels through deep learning (one-dimensional convolutional neural networks (1DCNN))has been done. STEW: Simultaneous Task EEG Workload Dataset is used in this study. Electroencephalogram (EEG) signals are captured during the mental task. Based on the subject's rating after completing the mental task, the workload is categorized as low mental workload or high mental workload. To balance and increase thedataset size Synthetic Minority Oversampling Technique (SMOTE) has been used. Different classification measures are used to evaluate the performance of the deep learning model used. Finally, the mental workload classification accuracy of 97.77 was achieved.
Learning channel attention for decoding of visual imagined text from multi-band EEG using metric learning

The electroencephalogram (EEG) signals represent proxies of the perceptual cognitive, and emotional processes of the user, which allows neuro-engineering researchers to decode the activities of the human mind non-invasively. Many studies resulting in visual imagined brain decoding have been proposed for the classification of EEG using traditional machine learning and deep learning algorithms. Although deep learning methods have achieved great success in many fields, their performance in EEG-based brain decoding analysis is still limited mainly due to the relatively small sizes of available datasets and low signal-to-noise ratio characteristics. Since some channels in the input feature maps contain more prominent information, we capitalize discriminative feature learning across the channels through the channel attention mechanism. This paper employs a low parametric channel attention module that adopts the idea of sparse cross-channel relationships and involves much fewer parameters but brings a performance gain to the baseline. Prior embedding is obtained using multi-block 1DCNN and then the feature vector is optimized using triplet loss for inducting better discriminator power. In order to show network generality, experiments have been conducted on an In-house visual imagery EEG dataset and as well a case study has been presented on a standard visual imagery EEG dataset. Our framework has achieved outperforming results on standard EEG datasets as well as showing gains in performance on in-house data (Dataset and code will be made publically available).
Assisting radiologists with transformer-based fracture detection for chest radiographs

Chest x-rays are a widely used diagnostic imaging technique in the medical field, but expert interpretation can be time-consuming and subjective, leading to potential diagnostic errors. To overcome these limitations, computer-aided diagnostic (CAD) systems using artificial intelligence (AI) have been developed, with transformer-based object detectors showing promising results. This study explores the rib fracture detection power of an out-of-the-box transformer detector and examines its adaptability to medical imaging. A private dataset of 30,000 chest radiographs was used to conduct experiments and compare the performance of transformer-based object detectors with a traditional CNN-based Faster-RCNN implementation. A retrospective analysis showed that transformer detectors had superior performance (i.e., 25.3mAP vs. 23.6mAP). Additionally, a reader study was conducted to evaluate the added diagnostic value of the AI system when assisting two radiologists in diagnosing chest x-rays. The results showed that the AI system provided valuable support improving the true positive rates from 73.6% to 83.4%. In conclusion, the study highlights the potential of transformer-based object detectors as a valuable tool in medical imaging, particularly in the diagnosis of chest x-ray rib fractures.
Emovo: A real-time anger detector on the smartphone using acoustic signal

Anger detection plays a pivotal role in mitigating potential mental health risks, presenting a significant yet challenging task. An anger detection app helps better track users’ emotional scenarios and helps to prevent potential mental illness. Such system on smartphones requires less computational resources, but many lite emotion recognition systems are based on facial expression as input, while facial data is difficult to obtain in the daily use of smartphones. We propose Emovo, a real-time anger detector on the smartphone using the acoustic voice signal. Emovo uses audio as input, which makes it easier to collect audio data through microphones in daily usage and easier to implement with fewer parameters. Our model on smartphones has a comparable accuracy of around 84.3% on the public dataset and has a number of parameters of around 0.2M. We explore the effects of variables such as gender and semantics through user research. Emovo stands as a compelling alternative for effective emotion recognition on smartphones.
How Could I Learn Rhythm Better? Investigating Three Learning Signals for Passive Haptic Learning in Different Context

Passive Haptic Learning (PHL) is the process of learning to perform a certain task without paying attention to it. In this paper, we invited 24 participants (evenly divided into 3 groups), and taught them a simple rhythm through the use of haptic, auditory and haptic & auditory signals. The subjects were then tested to reproduce the rhythms on a keyboard and ukulele. We inspected and analyzed the data from two aspects: duration and timing. We found that the difference in instruments and learning groups will be of significance when looking at the duration of notes and pauses. The participants provided both haptic and auditory signals during the learning phase achieved the best performance on duration of notes and pauses. Regarding the timing of the notes within the rhythm.
Towards a Haptic-based Virtual Cane to Assist Blind People in Obstacle Detection

Blind persons rely on the traditional white cane to explore and avoid obstacles while moving independently in indoor and outdoor environments. Although numerous electronic aids have been proposed in the literature and on the market, orienting and moving are still a challenge for people who cannot see. Usually, electronic support devices are used in combination with the stick as blind people consider it a fundamental tool for exploring and getting information about the surrounding. In this paper, a virtual version of the traditional white cane based on the use of a laser sensor, actuators and haptic feedback for obstacle detection is proposed to move towards a single aid for mobility tasks. Although the proposed electronic aid is the first prototype tested in an indoor environment, it is a first step towards a discreet and lightweight virtual cane.
AutoConstruct: Automated Neural Surrogate Model Building and Deployment for HPC Applications

Scientific Machine Learning (SciML), aiming at using machine learning methods to solve scientific computing problems, has been used in a wide range of HPC applications to improve the applications' performance. However, domain scientists, despite their rich expertise in their respective fields, often lack adequate knowledge in machine learning and computer systems. This gap makes it difficult for them to determine which computations can be replaced by neural network (NN) surrogates. Moreover, NN surrogates in HPC applications typically have sparse input features, necessitating system-level optimization to prevent memory resource overuse on accelerators. Consequently, constructing and deploying NN surrogates for HPC applications remains a significant challenge, with no existing tools to assist domain scientists in systematically building high-quality ML surrogates. To address these issues, we introduce AutoConstruct, a comprehensive solution for SciML in distributed systems. AutoConstruct consists of two components, the model-maker and deployment-helper, which operate automatically to select the appropriate ML surrogate architecture and deploy the surrogate model training and inference on specific accelerators, respectively. We showcase AutoConstruct's adaptability and performance through a series of HPC numerical benchmarks, real-world scientific applications, and computing platforms.
Generative Transformer Chatbots for Mental Health Support: A Study on Depression and Anxiety

Mental health is a critical issue worldwide and effective treatments are available. However, incidence of social stigma prevents many from seeking the support they need. Given the rapid developments in the field of large-language models, this study explores the potential of chatbots to support people experiencing depression and anxiety. The focus of this research is on the engineering aspect of building chatbots, and through topology optimisation find an effective hyperparameter set that can predict tokens with 88.65% accuracy and with a performance of 96.49% and 97.88% regarding the correct token appearing in the top 5 and 10 predictions. Examples of how optimised chatbots can effectively answer questions surrounding mental health are provided, generalising information from verified online sources. The results of this study demonstrate the potential of chatbots to provide accessible and anonymous support to individuals who may otherwise be deterred by the stigma associated with seeking help for mental health issues. However, the limitations and challenges of using chatbots for mental health support must also be acknowledged, and future work is suggested to fully understand the potential and limitations of chatbots and to ensure that they are developed and deployed ethically and responsibly.
Reliability factor for accurate remote PPG systems

The significance of contactless vital data monitoring has gained increasing attention in recent years, with light playing a crucial role in its implementation. Visible light, capable of penetrating thin body regions, enables inferences to be drawn regarding changes in volume, blood color, and pulse rates based on alterations in light intensity. Additionally, vital signs can be ascertained from light reflected off the skin, which can be captured by a standard camera when utilizing ambient light. Remote photoplethysmography (rPPG), a non-contact method that functions effectively over greater distances, remains highly susceptible to lighting conditions, individual physical attributes, and motion artifacts. Consequently, rPPG signal quality ranges from highly accurate to extremely poor, resulting in an unstable, non-reproducible, and challenging-to-implement technology. In this conference paper, we provide a comprehensive overview of the latest rPPG advancements, introduce annotated video databases for quality validation, and propose a reliability factor to address the issue of fluctuating signal quality. We demonstrate the potential of our approach through the CareCam application, a workplace-oriented solution employing a standard webcam to acquire vital data in real-time.
A Systematic Review of Interaction Approaches based on Visually Evoked Potentials

BCIs are already present in the mass market. Visual evoked potentials (VEP) are based on the recognition of recurrent visual stimuli by specific BCI. A variety of approaches use this technology to implement a whole range of different application scenarios. This paper provides an overview of approaches that use VEP either as a stand-alone technology or in combination with other technologies. First, terms for different VEP technologies and their possible procedures are introduced. The papers are classified according to the technology used, the quality of the classification, the number of simultaneous stimuli, and different application areas. One focus of the paper is value input through VEP, especially for approaches that do not work in combination with other devices. It is shown that the value input has been addressed only very little so far.