Designing for Affective Augmentation: Assistive, Harmful, or Unfamiliar?

In what capacity are affective augmentations helpful to humans, and what risks (if any) do they pose? In this position paper, we outline three works on affective augmentation systems, where our studies suggest these systems have the ability to influence our cognitive, affective, and (social) bodily perceptions in perhaps unusual ways. We provide considerations on whether these systems, outside clinical settings, are assistive, harmful, or as of now largely unfamiliar to users.Comment: Presented at the Assistive Augmentation Workshop 2023 held at the Augmented Humans 2023 conferenc

ROBUST AND PARALLEL SEGMENTATION MODEL (RPSM) FOR EARLY DETECTION OF SKIN CANCER DISEASE USING HETEROGENEOUS DISTRIBUTIONS

Melanoma is the most common dangerous type of skin cancer; however, it is preventable if it is diagnosed early. Diagnosis of Melanoma would be improved if an accurate skin image segmentation model is available. Many computer vision methods have been investigated, yet the problem of finding a consistent and robust model that extracts the best threshold value, persists. This paper suggests a novel image segmentation approach using a multilevel cross entropy thresholding algorithm based on heterogeneous distributions. The proposed strategy searches the problem space by segmenting the image into several levels, and applying for each level one of the three benchmark distributions, including Gaussian, Lognormal or Gamma, which are combined to estimate the best thresholds that optimally extract the segmented regions. The classical technique of Minimum Cross Entropy Thresholding (MCET) is considered as the objective function for the applied method. Furthermore, a parallel processing algorithm is suggested to minimize the computational time of the proposed segmentation model in order to boost its performance. The efficiency of the proposed RPSM model is evaluated based on two datasets for skin cancer images: The International Skin Imaging Collaboration (ISIC) and Planet Hunters 2 (PH2). In conclusion, the proposed RPSM model shows a significant reduced processing time and reveals better accuracy and stable results, compared to other segmentation models. Design/methodology – The proposed model estimates two optimum threshold values that lead to extract optimally three segmented regions by combining the three benchmark statistical distributions: Gamma, Gaussian and lognormal. Outcomes – Based on the experimental results, the suggested segmentation methodology using MCET, could be nominated as a robust, precise and extremely reliable model with high efficiency. Novelty/utility –A novel multilevel segmentation model is developed using MCET technique and based on a combination of three statistical distributions: Gamma, Gaussian, and Lognormal. Moreover, this model is boosted by a parallelized method to reduce the processing time of the segmentation. Therefore, the suggested model should be considered as a precious mechanism in skin cancer disease detection

Monte-Carlo Galerkin Approximation of Fractional Stochastic Integro-Differential Equation

A stochastic differential equation, SDE, describes the dynamics of a stochastic process defined on a space-time continuum. This paper reformulates the fractional stochastic integro-differential equation as a SDE. Existence and uniqueness of the solution to this equation is discussed. A numerical method for solving SDEs based on the Monte-Carlo Galerkin method is presented

Designing for Affective Augmentation: Assistive, harmful, or unfamiliar?

In what capacity are affective augmentations helpful to humans, and what risks (if any) do they pose? In this position paper, we outline three works on affective augmentation systems, where our studies suggest these systems have the ability to influence our cognitive, affective, and (social) bodily perceptions in perhaps unusual ways. We provide considerations on whether these systems, outside clinical settings, are assistive, harmful, or as of now largely unfamiliar to users

DeepSleep: A ballistocardiographic deep learning approach for classifying sleep stages

Current techniques for tracking sleep are either obtrusive (Polysomnography) or low in accuracy (wearables). In this early work, we model a sleep classification system using an unobtrusive Ballistocardiographic (BCG)-based heart sensor signal collected from a commercially available pressure-sensitive sensor sheet. We present DeepSleep, a hybrid deep neural network architecture comprising of CNN and LSTM layers. We further employed a 2-phase training strategy to build a pre-trained model and to tackle the limited dataset size. Our model results in a classification accuracy of 74%, 82%, 77% and 63% using Dozee BCG, MIT-BIH’s ECG, Dozee’s ECG and Fitbit’s PPG datasets, respectively. Furthermore, our model shows a positive correlation (r = 0.43) with the SATED perceived sleep quality scores. We show that BCG signals are effective for long-term sleep monitoring, but currently not suitable for medical diagnostic purposes

AN IMPROVEMENT OF CROSS ENTROPY THRESHOLDING FOR SKIN CANCER

Image processing procedures in medical diagnosis are used to improve diagnosis accuracy. An example of this is skin cancer detection using the thresholding approach. Thus, research studies involved in identification of inherited mutations predisposing family members to malignant melanoma have been performed in the Cancer Genetics field. Melanoma is one of the deadliest cancers, but could be cured when diagnosed early. A fundamental step in image processing is segmentation that includes thresholding, among others. Thresholding is based on finding the optimal thresholds value that partitions the image into multiple classes to be able to distinguish the objects from the background. The algorithm developed in this work is based on Minimum Cross Entropy Thresholding (MCET) method, using statistical distributions. We improved the previous work of Pal by using separately different statistical distributions (Gaussian, Lognormal and Gamma) instead of Poisson distribution. We applied our improved methods on bimodal skin cancer images and obtained promising experimental results. The resulting segmented skin cancer images, using Gamma distribution yielded better estimation of the optimal threshold than does the same MCET method with Lognormal, Gaussian and Poisson distribution

Clinical, biochemical and inflammatory predictors of mortality in patients with spontaneous bacterial peritonitis

Background: Spontaneous bacterial peritonitis (SBP) is a serious complication of liver cirrhosis. It contributes to high morbidity and mortality in this population. In-hospital mortality of SBP ranges between 20% and 40%, suggesting that further refinements are essential in managing SBP. Early recognition of high-risk patients would enable us to reduce the short-term mortality.Objective: The current study aimed to evaluate the value of clinical, biochemical and inflammatory markers in the prediction of 1-month and 3-month cumulative mortality in patients with SBP.Patients and methods: Two hundred patients with a confirmed diagnosis of SBP were enrolled. They were admitted and received the proper treatment at the National Liver Institute Hospital-Menoufia University, Egypt. Patients were prospectively followed up for mortality over a period of three months. Predictors of mortality were assessed and analyzed.Results: Mortality rates were 20% and 41% at 1 month and 3 month respectively. Our findings showed that low blood pressure, abdominal pain, fever, higher Child-Pugh score, MELD score, serum bilirubin, INR, serum creatinine, C-reactive protein to albumin (CRP/Albumin) ratio, neutrophil–lymphocyte ratio (NLR), massive splenomegaly and large ascites have been demonstrated as risk factors associated with short-term mortality.Conclusion: SBP carries a high risk of mortality among cirrhotic patients. Clinical parameters (low blood pressure, abdominal pain, fever, massive splenomegaly and large ascites), prognostic scores (Child-Pugh and MELD) and inflammatory markers (CRP, CRP/albumin ratio, and NLR) seem to be accurate and reliable tools that could independently predict short-term mortality in patients with SBP