Miracle-dz/UniFAR-FTUs huggingface dataset:
PLEADES: Population Level Observation of Smartphone Sensed Symptoms for In-the-wild Data using Clustering

Smartphones are increasingly being used for health monitoring. Training of machine learning health models require studies in which smartphone sensor data is gathered passively on subjects’ phones. Subjects live their lives ’In-the-wild” and periodically annotate data with ground truth health labels. While computational approaches such as machine learning produce accurate results, they lack explanations about the complex factors behind the manifestation of health-related symptoms. Additionally, population-level insights are desirable for scalability. We propose Population Level Exploration and Analysis of smartphone DEtected Symptoms (PLEADES), a framework to present smartphone sensed data in linked panes using intuitive data visualizations. PLEADES utilizes clustering and dimension reduction techniques for discovery of groupings of similar days based on smartphone sensor values, across users for population level analyses. PLEADES allows analysts to apply different clustering and projection algorithms to a given dataset and then overlays human-provided contextual and symptom information gathered during data collection studies, which empower the analyst in interpreting findings. Such overlays enable analysts to contextualize the symptoms that manifest in smartphone sensor data. We visualize two real world smartphone-sensed datasets using PLEADES and validate it in an evaluation study with data visualization and human context recognition experts.
Grocery Recognition in the Wild: A New Mining Strategy for Metric Learning

Recognizing grocery products at scale is an open issue for computer-vision systems due to their subtle visual differences. Typically the problem is addressed as a classification problem, e.g., by learning a CNN, for which all classes that are to be distinguished need to be known at training time. We instead observe that the products within stores change over time. Sometimes new products are put on shelves, or existing appearances of products are changed. In this work, we demonstrate the use of deep metric learning for grocery recognition, whereby classes during inference are unknown while training. We also propose a new triplet mining strategy that uses all known classes during training while preserving the ability to perform cross-folded validation. We demonstrate the applicability of the proposed mining strategy using different, publicly available real-world grocery datasets. The proposed approach preserves the ability to distinguish previously unseen groceries while increasing the precision by up to 5 percent.
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data

In recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable information about an individual's functional ability and lifestyle is needed. In this study, we used the popular WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we established a statistically significant difference (p<0.05) between the data generated from the sensors embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches don't capture data in the same way due to the location where they are worn. We deployed several neural network architectures to classify 15 different hand and non-hand-oriented activities. These models include Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch accelerometer data. Also, we saw that the classification precision obtained with the convolutional input classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities. Additionally, the CNN model for the watch accelerometer was better able to classify non-hand oriented activities when compared to hand-oriented activities.
Generic User-guided Interaction Paradigm for Precise Post-slice-wise Processing of Tomographic Deep Learning Segmentations Utilizing Graph Cut and Graph Segmentation

State of the art deep learning (DL) manifested in image processing as an accurate segmentation method. Nevertheless, its black-box nature hardly allows user interference. In this paper, we present a generic Graph cut (GC) and Graph segmentation (GS) approach for user-guided interactive post-processing of segmentations resulting from DL. The GC fitness function incorporates both, the original image characteristics and DL segmentation results, combining them with weights optimized by evolution strategy optimization. To allow for accurate user-guided processing, the foreand background seeds of the Graph cut are automatically selected from the DL segmentations, but implementing effective features for expert input for adaptions of position and topology. The seamless integration of DL with GC/GS leads to marginal trade-off in quality, namely Jaccard (JI) 1.3% for automated GC and JI 0.46% for GS only. Yet, in specific areas where a welltrained DL model may potentially fail, precise adaptions at a low demand for user-interaction become feasible and thus even outperforming the original DL results. The potential of GC/GS is shown running on groundtruth seeds thereby outperforming DL by 0.44% JI for the GC and even by 1.16% JI for the GS. Iterative sliceby-slice progression of the post-processed and improved results keeps the demand for user-interaction low.
A Snapshot-based Approach for Self-supervised Feature Learning and Weakly-supervised Classification on Point Cloud Data

Manually annotating complex scene point cloud datasets is both costly and error-prone. To reduce the reliance on labeled data, we propose a snapshot-based self-supervised method to enable direct feature learning on the unlabeled point cloud of a complex 3D scene. A snapshot is defined as a collection of points sampled from the point cloud scene. It could be a real view of a local 3D scan directly captured from the real scene, or a virtual view of such from a large 3D point cloud dataset. First the snapshots go through a self-supervised pipeline including both part contrasting and snapshot clustering for feature learning. Then a weakly-supervised approach is implemented by training a standard SVM classifier on the learned features with a small fraction of labeled data. We evaluate the weakly-supervised approach for point cloud classification by using varying numbers of labeled data and study the minimal numbers of labeled data for a successful classification. Experiments are conducted on three public point cloud datasets, and the results have shown that our method is capable of learning effective features from the complex scene data without any labels.
Using EEG and Gamified Neurofeedback Environments to Improve eSports Performance: Project Neuroprotrainer

: Human performance has permanently been an objective for society and, speciﬁcally, for researchers. Traditional sports, board games, video games. A wide diversity of domains have recall the attention on the reasons why top players perform better than the rest. eSports can be deﬁned as competitive multiplayer video games. Nowadays, eSports have a huge impact on society with billions of people playing or consuming related content. Being a relatively new universe, there is a wide gap when talking about applying scientiﬁc principles to performance analysis and improvement on eSports. This paper tries to establish a new research topic, introducing Virtual Reality and neuroscience as main frameworks to pursue a double objective: evaluate psycho-cognitive characteristics of eSports players aiming to proﬁle them and, additionally, using that proﬁle to create custom psycho-cognitive training plans. Neuroscience and EEG data from players have the ability of explaining the complex decision making procedures that involve an individual action while playing. Neurofeedback training (NFT) is a neuro-behavioral technique that will allow using real-time EEG data to drive a gamiﬁed environment aiming to adapt their brain activity to the optimum performance mode. This project aims, for the ﬁrst time, to use neurofeedback within a gamiﬁed training environment in order to improve individual performance on eSports.
Online Decision Support Tool that Explains Temporal Prediction of Activities of Daily Living (ADL)

: This paper presents an online decision support tool that can be used to assess and predict functional abilities in terms of nine Activities of Daily Living (ADLs) up to one year ahead. The tool is based on previously developed Computational Barthel Index (CBIT) and has been rebuilt using Gradient Boost (GB) models with average Area under ROC (AUC) of 0.79 (0.77-0.80), accuracy of 0.74 (0.70-0.79), recall of 0.78 (0.58-0.93), and precision of 0.75 (0.67-0.82) when evaluating ADLs for new patients. When re-evaluating patients, the models achieved AUC 0.95 (0.94-0.96), accuracy of 0.91 (0.90-0.92), recall of 0.91 (0.86-0.95), and precision of 0.92 (0.88-0.94). The decision support tool has been equipped with a prediction explanation module that calculates and visualizes influence of patient characteristics on the predicted values. The explanation approach focuses on patient characteristics present in the data, rather than all attributes used to construct models. The tool has been implemented in Python programming language using Flask Web framework and is accessible through a website or an Application Programming Interface (API).
Translational cultural adaptation and psychometric study of the Persian version of pediatric inventory for parents

BACKGROUND: Cancer, as a life-threatening disease in children, poses several challenges for parents. It is necessary to have a tool that can comprehensively examine the stressful events for parents of children with cancer. The aim this present study was done with the aim of study the Persian version of pediatric inventory for parents (PIP). MATERIALS AND METHODS: The study was a conducted based on methodological research design. Four hundred and fifteen parents of children with cancer referring to Alia Asghar Children's Hospital and Children's Medical Centre in Tehran answered the Persian version of PIP questionnaire in 2019. a confirmatory factor analysis was carried out using LISREL (software version 8.8) to test the construct validity of PIP. The two tools of parental stress scale and state-trait anxiety inventory (STAI-Y) were used for concurrent validity purposes. RESULTS: The results showed that, the overall score of the questionnaire was higher than the average and related to emotional distress. The internal correlation coefficient (Cronbach's alpha) in both parts of the PIP was between 0.808 and 0.957 and acceptable. Concurrent validity analysis indicated positive and significant correlation of this tool in the difficulty section of the scale with both Parental Stress Scale and STAI-Y. The results of confirmatory factor analysis indicated that the factor loads of all items except three items in the frequency section were more than 0.3 and were appropriate. CONCLUSION: The Persian version of PIP can be available to health and family experts as a valid and reliable tool to assess stressful events of parents of children with cancer.
Knowledge, attitude, and practice study on animal bite, rabies, and its prevention in an urban community

Introduction: Rabies is a neglected zoonotic tropical disease that usually affects the poorest communities. Rabies is 100% fatal and at the same time 100% preventable. A huge proportion of death due to rabies occurs in Asia and Africa, and India is reported to have the highest incidence of rabies. Aims: To assess the knowledge, attitude, and practice related to animal bites, rabies, and its prevention and utilization of health services for this purpose in the study population. Methods: A community-based cross-sectional study done in an urban area among the age group 20 and above of both gender to assess and statistically highlight the knowledge, attitude, and practice related to animal bites, rabies, and its prevention and utilization of health services for this purpose in the study population. The sample size was estimated to be 350 and a simple random sampling technique was used for the selection of samples. Results: About 76% had heard about rabies. Among them only 63.5% knew it is a fatal disease, only 37.6% knew animals other than dogs can also cause rabies, only 37.3% of the study participants knew about appropriate first aid. Only 37.5% of the animal bite victims washed their wound with soap and running water and 35% had a full course of vaccination. Education had been an important factor that created a significant difference in the knowledge level of the participants. Conclusions: Improved community awareness, forestalling animal rabies, and better access to affordable and potent human rabies vaccines are essential for the elimination of human rabies.
Parental perception on screen time and psychological distress among young children

Objective: We aimed to assess the parent-reported screen time of children, identify the perceived risk factors for increased screen time and its relationship to psychological distress in children. Materials and Method: A cross sectional study was conducted at a teaching hospital in Karachi, Pakistan. A total of 230 employees from medical and non-medical departments were included. Participants were employees with child/children ages 4-12 year who consented to participate in the study, we included 135 fathers and 91 mothers. The questionnaire included (i) demographic data (ii) Media history exam form and (iii) parent reported strength and difficult questionnaire (SDQ). Result: The average daily screen time reported was 2.5 (1.5-5) hour for boys and 2 (1-4) hour for girls. Preschoolers had greater screen time as compared to school-aged children (Median (IQR): 3 (1.5-5.6) vs 2 (1-4), P = 0.46). The children owned devices with approximately equal distribution of preschoolers and school-aged children (19 (27.1%) and 48 (30%), P = 0.661 respectively). Emotional score was found higher in school-aged group in comparison to preschoolers (p = 0.036). Moreover, mother screen time and number of devices owned by a child were found to be positively associated with child's screen time. Conclusion: We conclude that as we are embracing the digital age providing a tech free zone to children is virtually impossible. Children screen time related activities in our part of the world exceeds the limitation. Parental awareness and co-viewing screen with their children are essential to avoid media related behavior problems.
Association of handgrip strength and endurance with body composition in head and neck cancer patients

Introduction: Assessment of skeletal muscle function (SMF) is of clinical relevance in the prediction of treatment outcome and to decide on optimal management of head & neck cancer (HNC) patients. Handgrip strength (HGS) & handgrip endurance (HGE) are considered as surrogate marker for whole-body skeletal muscle function. Further, SMF depends substantially on the body composition (BC). Hence in this study, we compared BC, HGS and HGE between HNC patients and healthy controls and also analysed the association of HGS, HGE with body composition in HNC patients. Methods: A cross-sectional study, conducted in 44 subjects in the age between 18 to 60 years. Twenty-two were histologically proven HNC patients prior to cancer-specific treatment and twenty-two age and gender-matched healthy volunteers. The parameters recorded were Height, weight, waist circumference, hip circumference, HGS, HGE and BC. Hand-held dynamometer was used to measure HGS and HGE measured using a stopwatch. BC was estimated by whole-body bioelectrical Impedance analysis method using Bodystat Quad scan 4000 device. Result: Comparison of data between HNC patient & healthy control was done by Student's t test. HGS, HGE, lean body mass (LBM), fat-free mass index (FFMI), Phase angle (PA), body cell mass (BCM) and body cell mass index (BCMI) were found to be reduced significantly in HNC patients when compared to healthy subjects. Further, Pearson correlation analysis revealed a significant positive correlation of HGS & HGE with LBM, FFMI, PA, BCM & BCMI, whereas body fat mass index showed a negative correlation with HGS & HGE in HNC patients. Conclusion: Our findings revealed, a significant reduction in HGS, HGE in patient with HNC which denotes decreased skeletal muscle function and it is linearly associated with low muscle mass, body cell mass and phase angle.
Recent Progress in the Development of Anode Materials for Solid Oxide Fuel Cells

The field of research into solid oxide fuel cell (SOFC) anode materials has been rapidly moving forward. In the four years since the last in‐depth review significant advancements have been made in the reduction of the operating temperature and improvement of the performance of SOFCs. This progress report examines the developments in the field and looks to draw conclusions and inspiration from this research. A brief introduction is given to the field, followed by an overview of the principal previous materials. A detailed analysis of the developments of the last 4 years is given using a selection of the available literature, concentrating on metal‐fluorite cermets and perovskite‐based materials. This is followed by a consideration of alternate fuels for use in SOFCs and their associated problems and a short discussion on the effect of synthesis method on anode performance. The concluding remarks compile the significant developments in the field along with a consideration of the promise of future research. The recent progress in the development of anode materials for SOFCs based on oxygen ion conducting electrolytes is reviewed.
Extraction and Characterization of Phenolic Compounds from Rose Hip ( Rosa canina L.) Using Liquid Chromatography Coupled with Electrospray Ionization - Mass Spectrometry

Wild berry are a rich of natural compounds which provide them high antioxidant potential. The compounds which provide them these proprieties are known to be vitamins, flavonoids, anthocyanins and phenolic acids. The aim of this study was to extract and characterize bioactive compounds from rose hip ( Rosa canina L.) currently found in Romania. A qualitative high-performance liquid chromatography coupled with electrospray ionization mass spectrometric (ESI-MS) detection in positive ion mode has been used to identify phenolic compounds from rose hip crude extract. The chromatograms revealed the presence of a large number of compounds (19), identified and grouped as phenolic acids and flavones/ols, flavan-3-ols and also anthocyanins. Based on obtained results these berries can be highly recommended as part of our diet. Also this finding represents a contribution to the chemical characterization of phenolic profile of rose hip.
The relationship between physical performance and quality of life and the level of physical activity among the elderly

BACKGROUND: Loss of physical function during the process of aging might affect the quality of life. Physical function assessment tests predicts outcomes such as falls, institutionalization, and death. Studies assessing the association of physical function with quality of life and physical activity level of elderly population in India is scarce. Hence we aimed to assess the physical function of community dwelling older adults and to determine its association with physical activity levels and quality of life. MATERIALS AND METHODS: This was a cross-sectional analytical study. 89 community dwelling older adults between 60 and 80 years of age were recruited. Physical function was assessed by standing balance, walking speed, and grip strength. Quality of life was assessed by WHO QOL BREF questionnaire and physical activity level was assessed by International Physical activity Questionnaire. RESULTS: Standing balance was reduced in 24% and walking speed was decreased in 33% of the participants. Males had higher walking speed, and grip strength. Quality of life was better among males. Standing balance, walking speed and grip strength was significantly higher in moderate – heavy activity levels. There was positive correlation between physical function and quality of life. There was also positive correlation between physical activity level and quality of life. CONCLUSION: Physical function, quality of life and physical activity level were decreased. Males had better physical function and quality of life. Physically active individuals had better physical function and quality of life. Early detection of decreased physical function and increase in physical activity level could result in better quality of life among elderly.
Do orthodontists aim to decrease the duration of fixed appliance treatment?

OBJECTIVES: The aim of the present study was to investigate the trends in the use of fixed appliance and accelerated orthodontics to decrease the treatment duration. Further, this study aimed to assess the effect of demographic factors on the participant's choice of treatment modality. METHODS: This was a cross-sectional study in the form of questionnaire-based online survey especially designed and distributed to 265 orthodontists via email. The questions aimed to collect basic information about the participants and respondents' choices for decreasing the treatment duration. The data were analyzed according to gender, level of academic education and years of clinical experience using Chi-square test. RESULTS: A response rate of 85.2% was reported. Most of the orthodontists aimed to reduce the treatment duration by the biomechanical (66.8%) and surgical approaches (27.4%). The use of sliding mechanics (69.4%) and one-step retraction was more common (66.3%). MBT bracket prescription was more prevalent (51.7%), followed by Roth (41.1%). Conventional brackets were used more than self-ligating brackets, and aesthetic brackets were avoided by one third of the respondents. NiTi wires were the most commonly used wires during the alignment stage (44.2%). However, the effect of gender, years of clinical experience and specialty level of education showed some effects on the use of certain techniques and clinical practice of the respondents. CONCLUSIONS: Most of the orthodontists aimed to decrease orthodontic treatment duration by using biomechanical and surgical approaches. Gender and clinical experience to a certain extent affected the participants' choice during orthodontic treatment.
Cross section and optical theorem for defects on a corrugated surface

The definition for the surface scattering cross section of an object (defect, adatom, etc.) on a periodic surface in three dimensions is given. The analog of the Optical Theorem for gas phase scattering is derived. Calculations (made using semiclassical wave packets) are reported and compared to experiments for CO on Pt(111).