Reviewerly/exHarmony huggingface dataset:
Validation of QyScore’s® fully automated quantitative image segmentation tools against expert manual gold‐standards

Abstract Background Quantitative imaging provides valuable information for early detection, disease progression and treatment response monitoring. Manual segmentation is the current gold‐standard, but is prohibitively labour‐intensive for large scale use, such as in clinical routine, and suffers from individual variability. Thus, there is a substantial unmet need for validation of automatic segmentation techniques that perform as accurately as this labour‐intensive manual gold‐standard. Method The validation cohort consisted of 50 individuals with multiple diagnoses, wide age range (18‐86 yrs), balanced for sex and acquired on multiple scanners for improved generalizability of quantification results (Table 1). Three expert neuroradiologists each manually segmented the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles on 3DT1 images using itk‐SNAP (http://www.itksnap.org/) software. A consensus expert segmentation was then derived using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm (Wakefield et al., (2004)) for each region and compared with the fully automated segmentations produced using QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device. Performance was investigated using the Dice Similarity Coefficient (DSC) and concordance assessed with plotted linear regression. Result Mean, standard deviation and confidence intervals of the DSC demonstrated a high degree of agreement between the consensus manual gold‐standard and QyScore®’s automated segmentations (Table 2). Importantly, this agreement remained consistent following stratification by field strength, demonstrating generalizability to most clinical imaging centres. For 1.5T scanners the mean DSC was 0.81, 0.89, 0.79, 0.86, 0.94, 0.93 and 0.91 for the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles respectively. For the 3T scanners, this was equivalent or marginally better at 0.84, 0.89, 0.82, 0.86, 0.94, 0.94 , and 0.93 respectively. In addition, strong concordance was demonstrated between the volumes, expressed as a percentage of intracranial volume (%ICV), obtained by the automated medical device QyScore® and the consensus of the manual gold‐standard segmentation (Figure 1: Table 2). Conclusion QyScore® produces fast reliable automated segmentations with comparable accuracy to expert neuroradiologists. These findings support the implementation of QyScore® in clinical trials and in clinical routine to provide quantitative image analysis in support of diagnosis and monitoring of disease progression and treatment response.
Voice Quality Monitoring in Dementia Households

Abstract Background Voice disorders are common in People Living with Dementia (PLwD) and a decrease in voice quality can be an indicator of cognitive deterioration. Previous computational works have successfully employed voice to detect or predict dementia. Voice quality can be assessed by a speech therapist, or computationally when subjects periodically repeat the same sentences. There is, however, to our knowledge, no research on monitoring voice to track dementia progress from unconstrained data collected in the wild. Method In this work, we employ a dataset containing audio recordings of 14 households (a carer, a PLwD and visitors) interacting with Alexa. We identify several voice features, such as speech rate and OpenSmile features that differ between healthy users and PLwD. We create a voice footprint for each PLwD using a Gaussian distribution and identify audio recordings that significantly differ from this baseline over time by combining Expectation‐Minimisation and Mahalanolis distance outliers detection. Result The voice features selected are significantly different between our two user groups (p‐value &lt; 0.0001). Training a classifier on those features, allows us to identify healthy subjects and PLwD with over 90% accuracy. We are also able to quality how much the voice differs from its baseline over time. Conclusion While we can computationally identify voice variations that are out the ordinary, in the future, we will compare our results to a clinical assessment of voice quality. We will also integrate a semantics analysis to identify grammar errors or the use of wrong words. This will allow us to design an objective index measure for monitoring of speech quality.
Speaker Identification in Dementia Households

Abstract Background Speaker identification on datasets collected in the wild faces several challenges, including the scarcity of labelled data, noisy data and data where several people are talking. Previous works have focused on speaker identification using large amounts of labelled data or semi‐supervised approaches where a small amount of data is used a training set, but in this case, the datasets used only contain clean data and often the same sentences repeated several times by each participant. Method In this work, we propose a new approach to achieve fuzzy semi‐supervised clustering on Alexa data collected in households of people living with dementia (PLwD). In this context we wish to identify the carer and the PLwD, but also visitors. We extract x‐vectors from the audio data, apply PCA and use those as input features for our algorithm. The clusters are initialised with a small amount of data from each identified speaker (carer, PLwD). New data is added iteratively and membership scores are computed for each cluster. Membership is computed according to the Euclidian distance between the data point and each cluster. Centroids are updated every 30 iterations, only considering the points with high membership. Membership is also updated with the new centroids and considering the Mahalanolis distance. Result Our method performs comparatively to unsupervised learning and supervised learning in a two‐class problem (carer and PLwD), with 100% accuracy and 96% recall in identifying the PLwD with a training set of 5 files. When considering visitors as an unknown class, unsupervised learning fails. Our method performs similarly to supervised learning in identifying the carer and PLwD (97% accuracy and 80% recall), but is better at identifying visitors as unknown speakers than supervised learning (around 40% accuracy, and 4% respectively). Conclusion In this work, we propose a new method to perform speaker identification on data collected in the wild. We endeavour to address the shortcomings of supervised learning which tends to overfit and unsupervised learning which does not perform well with unknown classes.
THE USE OF VISUAL SCRIPTING UNREAL ENGINE 5 IN THE DEVELOPMENT OF AN ANDROID-BASED FPS GAME LEFT BEHIND

The popularity of Android mobile games in Indonesia is very high, but the majority of the games available are from overseas. Therefore, local game developers need to improve the quality of their products in order to compete with games that already exist in the Indonesian market. The purpose of the preparation of this research is to develop a zombie First Person Shooter (FPS) game entitled Left Behind which was developed using the Visual Scripting method and is expected to be an inspiration for developers in pouring their work by making quality games. This Left Behind FPS game was developed using Unreal Engine 5 as a game engine and later the game can be played on an Android smartphone. Unreal Engine 5 is an opensource game engine that was released in 2022 so that it can present high quality graphics. In testing the application, it is proven that the use of high graphics can be run on Android devices smoothly.
Neuropedagogy as a perspective direction of vocal educational work

Statement of the problem. The question of the quality of education in one way or another is raised by almost all recent studies in the field of education. Both domestic and foreign scientists insist on the importance of innovative changes in the educational process. Cognitive technologies of neuropedagogy can become one of the most effective innovative methods of learning, education and self-development. Neuropedagogy is based on the classical principles of pedagogy, psychology, neurology, cybernetics and reflects a person-oriented approach in the educational process. The goal of neuropedagogy is to optimally and creatively solve pedagogical tasks in practice, using knowledge about the individual features of the brain organization of higher mental functions. Neuropedagogy studies neuropedagogical systems, their properties and processes. Obiectives, methods, and novelty of the research. Neuropedagogy almost did not attract the attention of domestic authors, therefore the issues discussed in the article are of a topical nature and include elements of scientific novelty. The purpose of the article is to define and consider some of the aspects of neuropedagogy that can become the basis for innovative directions in vocal education. The main research methods were analysis and synthesis when studying the Ukrainian and foreign experience of implementing the principles of neuropedagogy in the educational process; method of systematization for determining the provisions of neuropedagogy essential for vocal education. Research results and conclusion. Vocal pedagogy is based on the general psychophysiological characteristics of a person, the peculiarities of his higher nervous activity. The system of vocal and performance skills is a complex of conditioned reflex actions, most of which have already been studied, but the search for new knowledge about the capabilities of the human body does not stop. The practical application of such knowledge is also useful in vocal pedagogy. Among the various methods and principles of teaching offered by neuropedagogy, as the most essential for working in the vocal class, we have identified the following: the reliance of perception on the unity of analysis-synthesis processes; taking into account the features of simultaneous perception (the effect of peripheral perception) and controlling the directions of attention; unity of conscious and unconscious processes; features of memory (emotionally colored, spatial-visual memorization); the importance of freedom in the educational process; application of the phenomenon of emotional and psychological resonance in the formation of vocal and performing skills; the development of the musician’s individual sphere as the embodiment of a personally oriented approach to learning. The application of the principles of neuropedagogy in the educational process can become an additional factor that will contribute to the individualization of education, the introduction of a personally oriented approach to the education of specialists in various creative fields in general and in vocal art in particular.
Psychological description of the factors of professional self-determination in medical students studying for a secondary vocational degree

Introduction. Professional self-determination of students is a precondition for their successful adaptation to professional activity. The article describes the psychological factors of students’ professional self-determination. Materials and Methods. The study involved 76 second-year students of the Faculty of Dentistry of Novosibirsk State Medical University (year of study 2021–2022) enrolled on a secondary vocational degree program. The research methods were selected according to the levels of professional self-determination proposed by E. A. Klimov. The gnostic level was studied by the following methods: M. Rokich’s Questionnaire of Value Orientations, D. A. Leontiev’s Life Orientations Test, R. V. Ovcharova’s Motives for Choosing a Profession Test, S. A. Pakulin’s Motivation of Pedagogical University Students, S. M. Ketko, V. V. Boyko’s Method of Diagnostics of Communicative Attitude, K. Izard’s Differential Emotions Scale, L. A. Regush’s Ability to Predict Questionnaire, and M. V. Chumakov’s Volitional Qualities of Personality Questionnaire. The practical level was studied by the following methods: Professional Self-Determination of Medical Students Questionnaire developed by the authors, A. A. Azbel’s Questionnaire for Studying the Statuses of Professional Identity, V. V. Boyko’s Questionnaire for Diagnosing Communicative Tolerance, and M. Kuhn and T. McPartland’s test “Who am I?” processed by I.S. Kletsina. Results. The study revealed the following factors of professional self-determination of students: psychological maturity, motivation, value orientations, life position, identity, communication style and cognitive competence. The analysis of the components of the levels of professional self-determination showed the contradictory nature of the relationship between the two levels. At the practical level, students are inclined to show mercy and have a well-formed professional identity. At the gnostic level, however, students who have a formed motivation for learning demonstrate a tendency to aggressive behavior, doubts about the choice of profession and lack of positive emotions. Further, there is a dominance of muscular characteristics. The data allowed us to formulate the assumption that students’ gender characteristics transformed as a result of students having to resolve the contradiction between the requirements for professional training and psychological capabilities to meet them. Conclusions. By the end of study, students go through the stage of professional formation, clarifying the value-semantic sphere of the profession and solving the contradiction between the requirements for professional training and the psychological capability to satisfy them. Further study is needed to explain why the muscular gender characteristic dominates in students’ communication style.
DualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition With Limited Annotations

Multi-label image recognition in the low-label regime is a task of great challenge and practical significance. Previous works have focused on learning the alignment between textual and visual spaces to compensate for limited image labels, yet may suffer from reduced accuracy due to the scarcity of high-quality multi-label annotations. In this research, we leverage the powerful alignment between textual and visual features pretrained with millions of auxiliary image-text pairs. We introduce an efficient and effective framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Evidence-guided Dual Context Optimization</i> ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DualCoOp++</monospace> ), which serves as a unified approach for addressing partial-label and zero-shot multi-label recognition. In <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DualCoOp++</monospace> we separately encode evidential, positive, and negative contexts for target classes as parametric components of the linguistic input (i.e., prompts). The evidential context aims to discover all the related visual content for the target class, and serves as guidance to aggregate positive and negative contexts from the spatial domain of the image, enabling better distinguishment between similar categories. Additionally, we introduce a Winner-Take-All module that promotes inter-class interaction during training, while avoiding the need for extra parameters and costs. As <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DualCoOp++</monospace> imposes minimal additional learnable overhead on the pretrained vision-language framework, it enables rapid adaptation to multi-label recognition tasks with limited annotations and even unseen classes. Experiments on standard multi-label recognition benchmarks across two challenging low-label settings demonstrate the superior performance of our approach compared to state-of-the-art methods.
Accelerating Huffman Encoding Using 512-bit SIMD Instructions

Based on 512-bit SIMD instructions, a Huffman encoding implementation, termed Huffman-SIMD, is proposed. The proposed implementation consists of four parts: establishing the Huffman coding table, data initialization, look-up table, and shifting and merging data. We establish the code table according to the characteristics of SIMD instructions. The code table is divided into eight sub-tables, with every two sub-tables in a group. It uses flag bits in each storage item to distinguish codewords from non-codewords, so the code table does not need to store the length of codewords in order to reduce the use of registers. After accelerating the table lookup operation using SIMD instruction, the valid size in each entry is different. Therefore, the shift merging algorithm is designed to operate on data and eliminate the spacing between data. This paper uses three datasets, Calgary, Silesia and Canterbury, to evaluate the implementation and compare it with the existing Huff0 library. The throughput is improved by 12.01% on average. Thus the implementation proposed in this paper improves the coding efficiency.
ESNet: An Efficient Framework for Superpixel Segmentation

Superpixel segmentation divides an original image into mid-level regions to reduce the number of computational primitives for subsequent tasks. The two-stage approaches work better but have high computational complexity among the existing deep superpixel algorithms. In contrast, the FCN style approaches cannot extract specific image features for the superpixel task. To combine the advantages of both types of methods, we propose a carefully designed framework termed Efficient Superpixel Network (ESNet) to explicitly enhance the capability of the network to describe clustering-friendly features and simultaneously preserve the simple network structure. Concretely, two points are concerned with ESNet. First, meaningful features need to be constructed for effective superpixel clustering; hence we propose the Pyramid-gradient Superpixel Generator(PSG) to decouple the ESNet into two joint parts, i.e., the feature extractor and the superpixel generator. Second, the superpixel generator is designed in an efficient manner, which performs multi-scale sampling of input images, and can work independently by replacing the introduced feature extractor with two initial convolutional layers. Extensive experiments show that our framework achieves state-of-the-art performances on multi-datasets and is 5.3× smaller on inference than the best existing one-stage FCN-based methods.
A Robust Heterogeneous Offloading Setup Using Adversarial Training

Deep Neural Networks (DNNs) are very resource-demanding at inference time. Hence, one needs to be able to offload the model execution on the cloud as a solution. The problem is that we should use the same model on both resource-constrained devices and cloud sides. On the other hand, adversarial robustness is one of the main issues in many real-world applications, such as autonomous driving, where one desires model stability under imperceptible but adversarial input perturbations. However, adversarial training (AT) requires access to the actual model architecture and weights during the training. In our setup, two different deep models (suitable for each side) are broken into several blocks. Then, we select a combination of blocks to perform the computation according to the constraints in the inference time, and each block is executed on its respective side. Moreover, we propose a novel modified AT method that can virtually train all the mentioned blocks collectively. Rigorous evaluations of our method on CIFAR-10 and CIFAR-100 show that the proposed AT is effective in making the models robust under various offloading scenarios. Furthermore, we show that the more blocks of the large network are present in the selected model, the higher the final accuracy. To the best of our knowledge, our method is the first one, in which a heterogeneous offloading scheme under adversarial robustness is investigated.
Designing An Encryption Function for Chaotic-based Model with Bifurcation Shifting in Security Communicate

A new design for a function(s) developed in the encryption stage on secret communications by shifting of the bifurcation for Quadratic map that is chaotic with one parameter. These functions approach the values for parameters on the real axis. Each new value for a chaotic map will add to the generated real values super increasing sequence toward negative infinity, and then modify the ciphertext. Bifurcation shifting computed through different functions shifting the bifurcation function such as triangular and exponential function. A modified ciphertext applied in the terms of a fuzzy chaotic based model. This encryption stage will be applied in communication models or as a part of the master-slave system. Then we provide the algorithms to recover the performance steps of the designed method. Finally, the process of numerical implementation is discussed to verify the effectiveness of the method with primary results.
Comparative Analysis of Algorithms Naïve Bayes and C45 for Student Satisfaction with Administrative Services

One of the reasons for increasing the number of people interested in schools is the service aspect. This is faced with the demands of the interests of various parties, such as leaders, shareholders (foundations), teachers and students at Siti Banun Vocational School. For this reason, good and effective management is needed so that the target of service recipients, namely the level of student satisfaction with administrative services, can be achieved. Data mining is an academic field focused on the systematic exploration and analysis of extensive datasets to discover valuable insights through the extraction of knowledge and detection of patterns. The best classification algorithm currently is the C45 algorithm and Naïve Bayes. The accuracy and AUC values will be compared. From 126 RPL Department Data from Class From the comparison of the amount of training data and test data, the best level of accuracy and AUC is by dividing test data: training data 10: 90. The accuracy and AUC values for the C45 algorithm are 85% and 88%. each. The accuracy and AUC values for the Naïve Bayes algorithm are 92% and 94% respectively.
Learning State Detection with Multimodal Information in Virtual Reality Learning

The learning state is a crucial factor that significantly impacts students' learning efficiency and overall learning outcomes. It also serves as a vital indicator for evaluating teachers' effectiveness in their instructional practices. To assess the learning state, various aspects of students' behavior, including their expressions, actions, and language, can effectively reveal valuable insights. Traditionally, studies have focused on investigating the learning state in conventional classroom and online education settings. However, limited research has been conducted in the context of virtual reality (VR) immersive learning environments. Considering the unique challenges posed by VR, such as the occlusion of the upper part of the face by VR devices and the distinct characteristics of VR interaction, we propose a novel method for detecting the learning state while wearing VR during the learning process. This method integrates multimodal information, including video captured by a camera, eye movement sensor data from the VR device, and electroencephalography (EEG) signals from an EEG headband. By comprehensively analyzing these inputs, we can determine the learning state effectively. An experimental evaluation was conducted to validate the effectiveness of the proposed multimodal information infusion learning state detection method in the context of VR immersive learning. The results demonstrate that this approach significantly enhances the accuracy of determining the learning state. Ultimately, this advancement has the potential to improve the overall learning quality and facilitate the widespread adoption of VR in educational settings.