Miracle-dz/UniFAR-FTUs huggingface dataset:
Immersive Haptic Authoring: Creating Haptic Experiences On-Demand within VR through Direct Manipulation

This paper presents immersive haptic authoring , a novel way to author haptic experiences within VR. Designing haptics to create rich VR experiences is becoming increasingly common, yet designers lack the tools to iteratively design, experience, and test such experiences easily. Designing haptics using desktop tools is a slow, distracting and unintuitive process. To address this, we introduce immersive haptic authoring , a new approach that allows the haptic designer to map, program, and modify haptic experiences within VR through direct and spatial manipulation, which can support rapid iteration and exploration of new haptic experiences. In this paper, we develop a system to demonstrate the concept and present insights from a qualitative evaluation session with experts. The result suggest that our approach has many benefits including better design exploration, time efficiency, and provides immersive design interactions, which together provide us insights for the future of VR haptics authoring tools.
PROJE T PILO TE D’A PPLICATION D’UNE BAT TERIE DE BIOE S S A IS À L’ÉCHELLE N ATION A LE

Une batterie de bioessais est utilisée pour évaluer la qualité des sédiments de treize cours d’eau. L’objectif est d’évaluer la faisabilité et la pertinence du recours à des tests écotoxicologiques dans la surveillance de la qualité des sédiments. Les avantages et la complémentarité de ces outils aux approches chimiques, ainsi que la sensibilité relative des bioessais sont discutés dans le but de recommander une batterie de tests pertinente pour de futurs projets.
CONAN: Diagnosing Batch Failures for Cloud Systems

Failure diagnosis is critical to the maintenance of large-scale cloud systems, which has attracted tremendous attention from academia and industry over the last decade. In this paper, we focus on diagnosing batch failures, which occur to a batch of instances of the same subject (e.g., API requests, VMs, nodes, etc.), resulting in degraded service availability and performance. Manual investigation over a large volume of high-dimensional telemetry data (e.g., logs, traces, and metrics) is labor-intensive and time-consuming, like finding a needle in a haystack. Meanwhile, existing proposed approaches are usually tailored for specific scenarios, which hinders their applications in diverse scenarios. According to our experience with Azure and Microsoft 365 – two world-leading cloud systems, when batch failures happen, the procedure of finding the root cause can be abstracted as looking for contrast patterns by comparing two groups of instances, such as failed vs. succeeded, slow vs. normal, or during vs. before an anomaly. We thus propose CONAN, an efficient and flexible framework that can automatically extract contrast patterns from contextual data. CONAN has been successfully integrated into multiple diagnostic tools for various products, which proves its usefulness in diagnosing real-world batch failures.
Question Span Extraction from Chats of Instant Messaging Platforms

Instant messaging platform users often create random messages that are irrelevant to the community members to respond. We want to identify when a community member posts a relevant question so that we can alert the community experts (who can answer). To address this issue, we develop a novel data-set of 2925 span labels corresponding to the relevant questions in messages. We formulate two downstream natural language processing tasks separately - first, text classification to predict whether a message contains a relevant question or not, and second span extraction to output only relevant snippets of text. We adapt pre-trained models-BERT, SpanBERT, and RoBERTa for the classification task. We fine-tune, validate, and evaluate classifiers with 2400, 225, and 300 examples respectively. BERT(base) model achieves the best results with 85% accuracy, 96% precision, 80% recall, and 88% F1 score. We also introduce two customised metrics ( Relevancy and Non-Relevancy Accuracy) that are more appropriate for our downstream application to evaluate classifiers. We introduce a new downstream task called ’extract relevant question span’ to fine-tune T5 variants to extract text snippets corresponding to the relevant question in a given message. We also utilize pre-trained BART for span extraction task. Every span extraction model is fine-tuned and evaluated with 1200 and 300 examples respectively. Both types of models perform similarly with best BLEU-1 score of 0.83. BART-large and T5-base outperforms other models with 0.92 METEOR score.
GNN: A Survey on Architectures and Optimizations

Graph Neural Networks have achieved unprecedented success in modeling graph-structured problems. However, they are computationally expensive to train and use in production. Practitioners are forced to compromise on the size of their training graph and the depth & power of their neural network. Consequently, a large number of approximations and optimization techniques have been developed over the last several years to overcome these limitations. This survey is an attempt to systematically classify these techniques, helping practitioners evaluate holistically and apply them in a context-sensitive manner. We compare and contrast key ideas from nine recent representative papers that describe various optimized GNN architectures, neighborhood sampling techniques, and computational models for efficient training and deployment. In particular, this survey is organized as follows:
Outlier Detection in Linear Regression

Regression analysis is one of the most important branches of multivariate statistical techniques. It is widely used in almost every field of research and application in multifactor data, which helps to investigate and to fit an unknown model for quantifying relations among observed variables. Nowadays, it has drawn a large attention to perform the tasks with neural networks, support vector machines, evolutionary algorithms, et cetera. Till today, least squares (LS) is the most popular parameter estimation technique to the practitioners, mainly because of its computational simplicity and underlying optimal properties. It is well-known by now that the method of least squares is a non-resistant fitting process; even a single outlier can spoil the whole estimation procedure. Data contamination by outlier is a practical problem which certainly cannot be avoided. It is very important to be able to detect these outliers. The authors are concerned about the effect outliers have on parameter estimates and on inferences about models and their suitability. In this chapter the authors have made a short discussion of the most well known and efficient outlier detection techniques with numerical demonstrations in linear regression. The chapter will help the people who are interested in exploring and investigating an effective mathematical model. The goal is to make the monograph self-contained maintaining its general accessibility. DOI: 10.4018/978-1-60960-551-3.ch020
Inflammatory cytokines and ischemic stroke risk

Objective We studied circulating interleukin (IL)–6, IL-8, and IL-10 concentrations and incident ischemic stroke risk in a biracial cohort, and determined if these cytokines mediated the racial disparity in stroke incidence affecting the black population. Methods The Reasons for Geographic and Racial Differences in Stroke study enrolled 30,237 black and white men and women age ≥45 in 2003-2007. We measured baseline IL-6, IL-8, and IL-10 in a case–cohort study of 557 participants with incident stroke over 5.4 years and 951 participants in a cohort sample. Results IL-6, but not IL-8 or IL-10, was higher in cases compared to the cohort sample (mean 4.5 vs 3.7 ng/mL; p < 0.001). Only IL-6 was associated with stroke risk factors. Adjusting for age, sex, and race, the hazard ratio (HR; 95% confidence interval) for incident stroke for the highest vs lowest quartile of IL-6 was 2.4 (1.6–3.4). HRs for the highest vs lowest quartiles of IL-8 and IL-10 were 1.5 (1.0–2.1) and 1.4 (1.0–1.9), respectively. After additional adjustment for stroke risk factors, only higher IL-6 remained associated with stroke risk (HR 2.0; 1.2–3.1). Associations did not differ by race. Mediation analyses showed that IL-6 mediated the black–white disparity in stroke risk, but mediation was via IL-6 associations with stroke risk factors. Conclusions In this biracial population-based sample, IL-6 was strongly associated with risk of incident stroke and mediated the racial disparity in stroke via inflammatory effects of risk factors. Further study on the clinical utility of IL-6 measurement in stroke risk assessment would be helpful.
Deciphering the relationship between type 2 diabetes and fracture risk: the genetic and observational evidences

The "diabetic bone paradox" suggested that type 2 diabetes (T2D) patients would have higher bone mineral density (BMD) but higher fracture risk than individuals without T2D. In this study, we found that the genetically predicted T2D was associated with higher BMD and lower risk of fracture in both wGRS and two-sample MR analyses. We also identified ten genomic loci shared between T2D and fracture, with the top signal at SNP rs4580892 in the intron of gene RSPO3. And the higher expression in adipose subcutaneous and higher protein level in plasma of RSPO3 were associated with increased risk of T2D, but decreased risk of fracture. In the prospective study, T2D was observed to be associated with higher risk of fracture, but BMI mediated 30.2% of the protective effect. However, when stratified by the risk factors secondary to the disease, we observed that the effect of T2D on the risk of fracture decreased when the number of risk factors secondary to T2D decreased, and the association became non-significant if the T2D patients carried none of the risk factors. In conclusion, the genetically determined T2D might not be associated with higher risk of fracture. And the shared genetic architecture between T2D and fracture suggested a top signal around RSPO3 gene. The observed effect of T2D on fracture risk decreased if the risk factors secondary to T2D could be eliminated. Therefore, it is important to manage the complications of T2D to prevent the risk of fracture.
Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program

Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide (P < 4.6x10-11) significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.
Modern Transference of Domestic Cooking Skills

As the primary source of learning cooking skills; it is vital to understand what mothers think about the transference of cooking skills to their children. The current analysis aimed to highlight mothers’ perceptions of children’s involvement and cooking practices within the home setting. Sixteen focus group discussions were conducted on the island of Ireland (Republic of Ireland and Northern Ireland [UK]) with 141 mothers aged 20–39 years old. All focus groups were transcribed verbatim and an inductive thematic analysis using NVivo software was undertaken. Seven themes emerged from the dataset; (1) “How we learned to cook”; (2) “Who’s the boss”; (3) “Children in the way”; (4) “Keep kids out”; (5) “Involvement means eating”; (6) “Intentions versus reality”; and (7) “Kids’ ‘interest’ in cooking”. These themes illustrate a lack of cooking skill transference in relation to everyday meal preparation in modern times. The culture of children in the kitchen has vastly changed; and opportunities for children to learn basic skills are currently limited. Further research is required to confirm the findings that emerged from this analysis.
Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review

The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.
Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images

Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study’s training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew’s correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively.
Counting Finger and Wrist Movements Using Only a Wrist-Worn, Inertial Measurement Unit: Toward Practical Wearable Sensing for Hand-Related Healthcare Applications

The ability to count finger and wrist movements throughout the day with a nonobtrusive, wearable sensor could be useful for hand-related healthcare applications, including rehabilitation after a stroke, carpal tunnel syndrome, or hand surgery. Previous approaches have required the user to wear a ring with an embedded magnet or inertial measurement unit (IMU). Here, we demonstrate that it is possible to identify the occurrence of finger and wrist flexion/extension movements based on vibrations detected by a wrist-worn IMU. We developed an approach we call “Hand Activity Recognition through using a Convolutional neural network with Spectrograms” (HARCS) that trains a CNN based on the velocity/acceleration spectrograms that finger/wrist movements create. We validated HARCS with the wrist-worn IMU recordings obtained from twenty stroke survivors during their daily life, where the occurrence of finger/wrist movements was labeled using a previously validated algorithm called HAND using magnetic sensing. The daily number of finger/wrist movements identified by HARCS had a strong positive correlation to the daily number identified by HAND (R2 = 0.76, p < 0.001). HARCS was also 75% accurate when we labeled the finger/wrist movements performed by unimpaired participants using optical motion capture. Overall, the ringless sensing of finger/wrist movement occurrence is feasible, although real-world applications may require further accuracy improvements.
FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition

In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still room for improvement in the accuracy of EEG emotion recognition. In this study, we propose a novel EEG emotion recognition algorithm called FCAN–XGBoost, which is a fusion of two algorithms, FCAN and XGBoost. The FCAN module is a feature attention network (FANet) that we have proposed for the first time, which processes the differential entropy (DE) and power spectral density (PSD) features extracted from the four frequency bands of the EEG signal and performs feature fusion and deep feature extraction. Finally, the deep features are fed into the eXtreme Gradient Boosting (XGBoost) algorithm to classify the four emotions. We evaluated the proposed method on the DEAP and DREAMER datasets and achieved a four-category emotion recognition accuracy of 95.26% and 94.05%, respectively. Additionally, our proposed method reduces the computational cost of EEG emotion recognition by at least 75.45% for computation time and 67.51% for memory occupation. The performance of FCAN–XGBoost outperforms the state-of-the-art four-category model and reduces computational costs without losing classification performance compared with other models.
Machined Surface Quality Monitoring Using a Wireless Sensory Tool Holder in the Machining Process

The quality of a machined surface plays a critical role in assembly performance, especially for precise matching parts, and therefore it is necessary to develop a surface quality monitoring system in the machining process. In this paper, an indirect surface quality monitoring approach is proposed with a wireless sensory tool holder. First, experimentation is conducted to collect the machining process signals from the tool holder. Then, the time domain, frequency domain and time–frequency domain features are extracted, and the deep forest algorithm is adopted to identify the surface quality, which is evaluated through the surface average parameter. Finally, the results of the experiment and the comparisons with other approaches demonstrate the effectiveness of the proposed method, which could be applied to ensure the surface quality, improve the machining efficiency and reduce the rejection rate of the machining process.
An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients’ lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimized deep learning models for differentiating between MCI and normal control samples. In previous studies, the hippocampus region located in the brain is used extensively to diagnose MCI. The entorhinal cortex is a promising area for diagnosing MCI since severe atrophy is observed when diagnosing the disease before the shrinkage of the hippocampus. Due to the small size of the entorhinal cortex area relative to the hippocampus, limited research has been conducted on the entorhinal cortex brain region for predicting MCI. This study involves the construction of a dataset containing only the entorhinal cortex area to implement the classification system. To extract the features of the entorhinal cortex area, three different neural network architectures are optimized independently: VGG16, Inception-V3, and ResNet50. The best outcomes were achieved utilizing the convolution neural network classifier and the Inception-V3 architecture for feature extraction, with accuracy, sensitivity, specificity, and area under the curve scores of 70%, 90%, 54%, and 69%, respectively. Furthermore, the model has an acceptable balance between precision and recall, achieving an F1 score of 73%. The results of this study validate the effectiveness of our approach in predicting MCI and may contribute to diagnosing MCI through MRI.
Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies

Cardiovascular diseases kill 18 million people each year. Currently, a patient’s health is assessed only during clinical visits, which are often infrequent and provide little information on the person’s health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.