An Evaluation of the Efficacy of a Perceptually Controlled Immersive Environment for Learning Acupuncture

This paper presents a basic but functional Perceptual User Interface (PUI) controlled immersive environment (IE) on an electronic learning platform (e-Learning) in order to deliver educational material relating to the NADA (National Acupuncture Detoxification Association) protocol for acupuncture. The purpose of this study is set out a proposed process for evaluating the learning efficacy of the PUI IE e-Learning application when compared with a typical Graphical User Interface (GUI) e-Learning IE application. Both are to be compared to a more traditional learning method. This paper evaluates user interface (UI) sentiment of the systems in advance of this proposed evaluation

Auto clustering for unsupervised learning of atomic gesture components using minimum description length

We present an approach to automatically segment and label a continuous observation sequence of hand gestures for a complete unsupervised model acquisition. The method is based on the assumption that gestures can be viewed as repetitive sequences of atomic components, similar to phonemes in speech, governed by a high level structure controlling the temporal sequence. We show that the generating process for the atomic components can be described in gesture space by a mixture of Gaussian, with each mixture component tied to one atomic behaviour. Mixture components are determined using a standard EM approach while the determination of the number of components is based on an information criteria, the Minimum Description Length

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets

Comparative performance between human and automated face recognition systems, using CCTV imagery, different compression levels and scene parameters

In this investigation we identify relationships between human and automated face recognition systems with respect to compression. Further, we identify the most influential scene parameters on the performance of each recognition system. The work includes testing of the systems with compressed Closed-Circuit Television (CCTV) footage, consisting of quantified scene (footage) parameters. Parameters describe the content of scenes concerning camera to subject distance, facial angle, scene brightness, and spatio-temporal busyness. These parameters have been previously shown to affect the human visibility of useful facial information, but not much work has been carried out to assess the influence they have on automated recognition systems. In this investigation, the methodology previously employed in the human investigation is adopted, to assess performance of three different automated systems: Principal Component Analysis, Linear Discriminant Analysis, and Kernel Fisher Analysis. Results show that the automated systems are more tolerant to compression than humans. In automated systems, mixed brightness scenes were the most affected and low brightness scenes were the least affected by compression. In contrast for humans, low brightness scenes were the most affected and medium brightness scenes the least affected. Findings have the potential to broaden the methods used for testing imaging systems for security applications

A Comprehensive Study on Pain Assessment from Multimodal Sensor Data

Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction

A case study in identifying acceptable bitrates for human face recognition tasks

Face recognition from images or video footage requires a certain level of recorded image quality. This paper derives acceptable bitrates (relating to levels of compression and consequently quality) of footage with human faces, using an industry implementation of the standard H.264/MPEG-4 AVC and the Closed-Circuit Television (CCTV) recording systems on London buses. The London buses application is utilized as a case study for setting up a methodology and implementing suitable data analysis for face recognition from recorded footage, which has been degraded by compression. The majority of CCTV recorders on buses use a proprietary format based on the H.264/MPEG-4 AVC video coding standard, exploiting both spatial and temporal redundancy. Low bitrates are favored in the CCTV industry for saving storage and transmission bandwidth, but they compromise the image usefulness of the recorded imagery. In this context, usefulness is determined by the presence of enough facial information remaining in the compressed image to allow a specialist to recognize a person. The investigation includes four steps: (1) Development of a video dataset representative of typical CCTV bus scenarios. (2) Selection and grouping of video scenes based on local (facial) and global (entire scene) content properties. (3) Psychophysical investigations to identify the key scenes, which are most affected by compression, using an industry implementation of H.264/MPEG-4 AVC. (4) Testing of CCTV recording systems on buses with the key scenes and further psychophysical investigations. The results showed a dependency upon scene content properties. Very dark scenes and scenes with high levels of spatial–temporal busyness were the most challenging to compress, requiring higher bitrates to maintain useful information

Lipoprotein-Associated Phospholipase A2: A Novel Contributor in Sjögren’s Syndrome-Related Lymphoma?

BackgroundB-cell non-Hodgkin’s lymphoma (B-NHL) is one of the major complications of primary Sjögren’s syndrome (SS). Chronic inflammation and macrophages in SS minor salivary glands have been previously suggested as significant predictors for lymphoma development among SS patients. Lipoprotein-associated phospholipase A2 (Lp-PLA2)—a product mainly of tissue macrophages—is found in the circulation associated with lipoproteins and has been previously involved in cardiovascular, autoimmune, and malignant diseases, including lymphoma.ObjectiveThe purpose of the current study was to investigate the contributory role of Lp-PLA2 in B-NHL development in the setting of primary SS.MethodsLp-PLA2 activity in serum samples collected from 50 primary SS patients with no lymphoma (SS-nL), 9 primary SS patients with lymphoma (SS-L), and 42 healthy controls (HC) was determined by detection of [3H]PAF degradation products by liquid scintillation counter. Moreover, additional sera from 50 SS-nL, 28 SS-L, and 32 HC were tested for Lp-PLA2 activity using a commercially available ELISA kit. Lp-PLA2 mRNA, and protein expression in minor salivary gland (MSG) tissue samples derived from SS-nL, SS-L patients, and sicca controls (SC) were analyzed by real-time PCR, Western blot, and immunohistochemistry.ResultsSerum Lp-PLA2 activity was significantly increased in SS-L compared to both SS-nL and HC by two independent methods implemented [mean ± SD (nmol/min/ml): 62.0 ± 13.4 vs 47.6 ± 14.4 vs 50.7 ± 16.6, p-values: 0.003 and 0.04, respectively, and 19.4 ± 4.5 vs 15.2 ± 3.3 vs 14.5 ± 3.0, p-values: <0.0001, in both comparisons]. ROC analysis revealed that the serum Lp-PLA2 activity measured either by radioimmunoassay or ELISA has the potential to distinguish between SS-L and SS-nL patients (area under the curve [AUC]: 0.8022, CI [95%]: 0.64–0.96, p-value: 0.004 for radioimmunoassay, and AUC: 0.7696, CI [95%]: 0.66–0.88, p-value: <0.0001, for ELISA). Lp-PLA2 expression in MSG tissues was also increased in SS-L compared to SS-nL and SC at both mRNA and protein level. ROC analysis revealed that both MSG mRNA and protein Lp-PLA2 have the potential to distinguish between SS-nL and SS-L patients (area under the curve [AUC] values of 0.8490, CI [95%]: 0.71–0.99, p-value: 0.0019 and 0.9444, CI [95%]: 0.79–1.00, p- value: 0.0389 respectively). No significant difference in either serum Lp-PLA2 activity or MSG tissue expression was observed between SS-nL and HC.ConclusionsLp-PLA2 serum activity and MSG tissue mRNA/protein expression could be a new biomarker and possibly a novel therapeutic target for B-cell lymphoproliferation in the setting of SS

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201