Miracle-dz/UniFAR-FTUs huggingface dataset:
Contact-less Vital Parameter Determination: An e-Health Solution for Elderly Care

Vital parameters are key figures for the basis functions of the human body. Without these basis body functions, such as the heart beat, life is impossible. Therefore, vital parameters are indicators for a person’s general medical condition. In recent years, the topic of vital parameter monitoring has been increasingly studied in the field of e-health. Especially the contact-less determination of vital parameters, such as heart rate, respiration rate, oxygen saturation and blood pressure, with consumer cameras brings a variety of advantages. In this work, we present methods to determine the mentioned vital parameters in a contact-less, optical way. Furthermore, we evaluated these methods for an utilisation in home environments with respect to elderly care. As a result, the remote determination of heart and respiration rate show reliable measurements, which makes the proposed methods ready for the application in home environments.
Early Recognition and Grading of Cataract Using a Combined Log Gabor/Discrete Wavelet Transform with ANN and SVM

— Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.
Validation of the fNIRS Pioneer™, a Portable, Durable, Rugged functional Near-Infrared Spectroscopy (fNIRS) Device

Assessing cognitive workload using functional near-infrared spectroscopy (fNIRS) in labs is well established. However, fNIRS sensors useful during normal activities in real-world environments are only recently emerging. We validated a small, portable fNIRS sensor (the fNIRS Pioneer TM) against a larger sensor with coverage of a larger cortical area, the NINScan developed at Massachusetts General Hospital. We used a gold-standard working memory task (n-back; (Kirchner, 1958)) and a more complex multi-attribute task battery (MATB) (Santiago-Espada et al., 2011). Twenty healthy adult (21.5 ± 3.3 years; 9 males) students at Brown University completed all three experimental visits. Fitting with previous research, on the n-back task, we found a significant effect of difficulty level on blood oxygenation (HbO2) in dorsolateral prefrontal cortex (dlPFC) HbO2 (p<.01), but not medial PFC HbO2 with the fNIRS Pioneer. For the NINScan, we observed increases in HbO2 from 3-back in two channels corresponding to the border between ventrolateral PFC (vlPFC) and dlPFC in both hemispheres (p<.05). When we aggregated MATB data across subtasks, and after accounting for time-on-task, we found a significant (p<.01) effect on HbO2 for the Pioneer and the NINScan. In all cases, the significant HbO2 findings were negative relationships, indicating less brain activation with better performance. While prior literature of functional brain imaging with MATB is not available, this finding is at least broadly consistent with the role of lateral PFC’s role in working memory. This indicates that both the fNIRS Pioneer and the NINScan sensor, when combined with appropriate data analytic techniques were useful for detecting changes in HbO2 that correlate with cognitive workload and behaviour, and that the fNIRS Pioneer is able to assess cognitive workload similarly to more larger, more expensive, and more established devices.
Histological and cytological heterogeneity of bone marrow in Philadelphia-positive chronic myelogenous leukaemia at diagnosis.

Summary. Cytological and histological features of the bone marrow in Philadelphia-positive chronic myelogenous leukaemia (CML) at diagnosis were analysed in a prospective study. Formerly described (Frisch et al, 1984) subtypes GRAN and GRAN/MEG were found, and myelofibrosis and incipient blast crisis could also be detected. A more or less continuous spectrum of number and degree of atypias in megakaryocytes was found. Biopsy and imprint were complementary in making differential diagnosis of both subtypes. Cytologically, type GRAN/MEG was characterized by a mixed proliferation of neutrophil, eosinophil and basophil series, besides proliferation of megakaryocytes. Type GRAN showed a predominantly neutrophilic proliferation. The importance of performing bone marrow analysis in evaluation of CML patients is discussed.
Cost-Effective Clinical Trial Design: Application of a Bayesian Sequential Stopping Rule to the ProFHER Pragmatic Trial

We investigate value-based clinical trial design by applying a Bayesian decisiontheoretic model of a sequential experiment to data from the ProFHER pragmatic trial. In the first applied analysis of its kind to use research cost data, we show that the model’s stopping policy would have stopped the trial early, saving about 5% of the research budget (approximately £73,000). A bootstrap analysis based on generating resampled paths from the trial data suggests that the trial’s expected sample size could have been reduced by approximately 40%, saving an expected 15% of the budget, with 93% of resampled paths making a decision consistent with the result of the trial itself. Results show how substantial benefits to trial cost stewardship may be achieved by accounting for research costs in defining the trial’s stopping policy and active monitoring of trial data as it accumulates.
Projection Free Online Learning over Smooth Sets

The projection operation is a crucial step in applying Online Gradient Descent (OGD) and its stochastic version SGD. Unfortunately, in some cases, projection is computationally demanding and inhibits us from applying OGD. In this work we focus on the special case where the constraint set is smooth and we have an access to gradient and value oracles of the constraint function. Under these assumptions we design a new approximate projection operation that necessitates only logarithmically many calls to these oracles. We further show that combining OGD with this new approximate projection, results in a projection free variant which recovers the standard rates of the fully projected version. This applies to both convex and strongly-convex online settings.
Cross Layer NOMA Interference Mitigation for Femtocell Users in 5G Environment

In this paper, we propose a joint channel allocation and power control algorithm by using cognitive radio non-orthogonal multiple access (CR-NOMA) for femtocell users (FUs). The aim is to maximize the sum rate of the FUs for guaranteed quality of service (QoS). With an aim to have guaranteed QoS for FUs, we use CR-NOMA at the femto base station (FBS). Then, an algorithm for pairing among strong and weak users is proposed by using the channel gain difference. Using pairing, the NOMA interference between them reduces which results in better channel utilization. Moreover, we differentiate the even/odd number of FUs in a femtocell to provide the QoS for weak users also. For this purpose, OMA is used to get a predefined data rate using a greedy channel allocation algorithm. The power of each FBS is controlled by using the successive convex approximation for low complexity (SCALE) protocol with Karush-Kuhn-Tucker (KKT) conditions. Numerical results demonstrate that the proposed scheme enhances the sum rate and provides guaranteed QoS for CR-NOMA based femtocell users in comparison to the existing conventional OMA based-femtocell techniques.
Understanding the pedagogical potential of Interactive Spherical Video-based Virtual Reality from the teachers’ perspective through the ACE framework

ABSTRACT This study explores the pedagogical potential of Interactive Spherical Video-Based Virtual Reality (ISVVR) in geography education from the teachers’ perspective. Based on a synthesized developmental model, namely ACE (Acceptance, Creation, and Expertise), we analyzed 187 sets of survey data collected in teacher professional training in which geography teachers were introduced to the production of ISVVR for learning and teaching purposes. The quantitative data analysis revealed that the participants were positive about the pedagogical use of ISVVR. Also, based on the analysis of the qualitative data gathered through the open-ended survey questions via a data-mining technique, we found that participants’ adoption concerns were already beyond the evaluative acceptance level, majorly revolving around issues on pedagogical technology integration. It is suggested that further efforts of teacher professional development are needed for designing and refining the pedagogical use of ISVVR in practice.
Machine Learning Programs Predict Saguaro Cactus Death

Results: Saguaro cacti (Carnegiea gigantea) show extensive bark coverage and cacti with extensive bark coverage die prematurely. Over the 23-year period of study, bark coverage on all surfaces was relatively constant. Decision trees are able to predict cactus death up to 96%. Three machine learning programs used similar surface coverages to make similar predictions of future bark coverage and cactus death accurately (approximately 92%), for cacti that had overall bark coverage less than 80% on south-facing surfaces. Higher prediction accuracies were obtained for cacti with were low bark percentages. While bark coverage rates and cactus death were less accurate for cacti with higher bark percentages because cacti can remain with high bark percentages with many years prior to death. Cacti with more than 80% coverage on south-facing surfaces were accurately predicted (p<0.05) to be alive and dead of the 23-year period with a tracking method.
A combined deep-learning approach to fully automatic left ventricle segmentation in cardiac magnetic resonance imaging

In clinical practice, cardiac magnetic resonance imaging (CMR) is considered the gold-standard imaging modality for the evaluation of function and structure of the left ventricle (LV). However, the quantification of LV parameters in all frames, even when performed by experienced radiologists, is very time consuming mainly due to the inhomogeneity of cardiac structures within each image, the variability of the cardiac structures across subjects and the complicated global/regional temporal deformation of the myocardium during the cardiac cycle. In this work, we employed a combination of two convolutional neural networks (CNN) to develop a fully automatic LV segmentation method for Short Axis CMR datasets. The first CNN defines the region of interest (ROI) of the cardiac chambers based on You Only Look Once (YOLO) network. The output of YOLO net is used to filter the image and feed the second CNN, based on UNet network, which segments the myocardium and the blood pool. The method was validated in CMR exams of 59 individuals from an institutional clinical protocol. Segmentation results, evaluated by metrics Percentage of Good Contours, Dice Index and Average Perpendicular distance, were 98,59% ± 4,28%, 0,93 ± 0,06 and 0,72 mm ± 0,62 mm, respectively, for the LV epicardium, and 94,98% ± 14,04%, 0,86 ± 0,13 and 1,19 mm ± 1,29 mm, respectively, for the LV endocardium. The combination of two CNNs demonstrated good performance in terms of the evaluated metrics when compared to literature results.
LSTM neural network implementation using memristive crossbar circuits and its various topologies

Neural Network (NN) algorithms have existed for long time now. However, they started to reemerge only after computers had been invented, because computational resources are required to implement NN algorithms. In fact, computers themselves are not fast enough to train and run the NNs. It can take days to train some complex neural networks for certain applications. One of the complex NNs that became widely used is Long-Short Term Memory (LSTM) NN algorithm. As a broader approach to increase the computation speed and decrease power consumption of neural network algorithms, hardware realizations of the neural networks have emerged. Mainly FPGA and analog hardware are used for these purposes. On this occasion, it happens to be only FPGA implementations of LSTM exist. Using this lack, this thesis work mainly aims to show that LSTM neural network is realizable and functional in analog hardware. In fact, analog hardware using memristive crossbars can be a potential solution to the speed bottleneck experienced in software implementations of LSTM and other complex neural networks in general. This work mainly focuses on implementation of already trained LSTM neural networks in analog circuitry. Since training consists of both forward and backward pass computations through NNs, first, there should be focus on implementing the
Exploring the Impact of Individualized Research Consultations Using Pre and Posttesting in an Academic Library: A Mixed Methods Study

Abstract Objective – Academic librarians consistently offer individualized help to students and researchers. Few studies have empirically examined the impact of individualized research consultations (IRCs). For many librarians, IRCs are an integral part of their teaching repertoire. However, without any evidence of an IRC’s effectiveness or value, one might ask if it’s worth investing so much time and effort. Our study explored the impact of IRCs on students' search techniques and self-perceived confidence levels. We attempted to answer the following questions: 1) Do IRCs improve students’ information searching techniques, including the proper use of keywords and/or subject headings, the accurate use of Boolean operators, and the appropriate selection of specialized resources/databases? 2) Do IRCs influence students’ confidence level in performing effective search strategies? Methods – Our study used a mixed-methods approach. Our participants were students from the Faculties of Health Sciences and Medicine at the University of Ottawa, completing an undergraduate or graduate degree, and undertaking a research or thesis project. Participants were invited to complete two questionnaires, one before and one after meeting with a librarian. The questionnaires consisted of open-ended and multiple choice questions, which assessed students' search techniques, their self-perceived search techniques proficiency and their confidence level. A rubric was used to score students' open-ended questions, and self-reflective questions were coded and analyzed for content using the software QSR NVivo. Results – Twenty-nine completed pre and posttests were gathered from February to September 2016. After coding the answers using the rubric, two paired-samples t-tests were conducted. The first t-test shows that students’ ability to use appropriate keywords was approaching statistical significance. The second t-test showed a statistically significant increase in students’ ability to use appropriate search strings from the pretest to the posttest. We performed a last paired-samples t-test to measure students’ confidence level before and after the appointment, and a statistically significant increase in confidence level was found. Conclusion – Out of three paired t-tests performed, two showed a statistically significant difference from the pretest to the posttest, with one t-test approaching statistical significance. The analysis of our qualitative results also supports the statement that IRCs have a positive real impact on students’ search techniques and their confidence levels. Future research may explore specific techniques to improve search strategies across various disciplines, tips to improve confidence levels, and exploring the viewpoint of librarians.
Distributed Inexact Newton-type Pursuit for Non-convex Sparse Learning

In this paper, we present a sample distributed greedy pursuit method for non-convex sparse learning under cardinality constraint. Given the training samples uniformly randomly partitioned across multiple machines, the proposed method alternates between local inexact sparse minimization of a Newton-type approximation and centralized global results aggregation. Theoretical analysis shows that for a general class of convex functions with Lipschitze continues Hessian, the method converges linearly with contraction factor scaling inversely to the local data size; whilst the communication complexity required to reach desirable statistical accuracy scales logarithmically with respect to the number of machines for some popular statistical learning models. For nonconvex objective functions, up to a local estimation error, our method can be shown to converge to a local stationary sparse solution with sub-linear communication complexity. Numerical results demonstrate the efficiency and accuracy of our method when applied to large-scale sparse learning tasks including deep neural nets pruning
A Restore-Free Mode for MLC STT-RAM Caches

Spin-transfer torque RAM (STT-RAM) caches are foreseen to replace traditional static RAM caches because of their nonvolatile nature and high density. Multilevel cell (MLC) STT-RAMs further enhance the storage density of single-level cell STT-RAMs. However, the two-step read/write process in MLC STT-RAMs adversely affects performance, energy consumption, and lifetime. Moreover, technology scaling makes the read operations disturb the stored data in MLC STT-RAMs, giving rise to an issue called read disturbance (RD). Restore operations, which are required to cope with RD, further add to the problems of using MLCs. In this brief, we propose a Restore-free mode for frequently reused cache lines in MLC STT-RAMs that leverages single-step read/write operations to the MLC without the need for restore operations. Our proposed scheme in single-core (quad-core) systems, achieves a 27.4% (23%) dynamic energy reduction, a 3.7% (7%) increase in performance, and an 81% (62.5%) lifetime improvement.
Generating image descriptions using capsule network

Abstract Using Capsule network to generate natural language descriptions of images with different orientation, shapes, sizes and regions is a novel technique to address image to text problem. In this paper we are going to present a model that generates image descriptions using convolutional neural network and capsule network. First, the model will be trained on MNIST dataset for testing the accuracy of identifying the images with different orientation using capsule network and after that we used Flickr8K [3] and Flickr30K [4, 5] datasets over CNN and bidirectional recurrent neural network to generate text descriptions. We have added an additional steps to change the orientation of these images by rotating them to certain angles which will help in creating a new set of training samples for images with different orientations. Second, we analysed the performance of both the system separately and combined, calculate the aggregated result while taking in account the complexity of image descriptor and pose content of images. We found that this idea outperform significantly on describing images with different orientations and scalable to adapt new images apart from the training samples without predefined set of guidelines.
Cross-Modality Bridging and Knowledge Transferring for Image Understanding

The understanding of web images has been a hot research topic in both artificial intelligence and multimedia content analysis domains. The web images are composed of various complex foregrounds and backgrounds, which makes the design of an accurate and robust learning algorithm a challenging task. To solve the above significant problem, first, we learn a cross-modality bridging dictionary for the deep and complete understanding of a vast quantity of web images. The proposed algorithm leverages the visual features into the semantic concept probability distribution, which can construct a global semantic description for images while preserving the local geometric structure. To discover and model the occurrence patterns between intra- and inter-categories, multi-task learning is introduced for formulating the objective formulation with Capped-$\ell _{1}$ penalty, which can obtain the optimal solution with a higher probability and outperform the traditional convex function-based methods. Second, we propose a knowledge-based concept transferring algorithm to discover the underlying relations of different categories. This distribution probability transferring among categories can bring the more robust global feature representation, and enable the image semantic representation to generalize better as the scenario becomes larger. Experimental comparisons and performance discussion with classical methods on the ImageNet, Caltech-256, SUN397, and Scene15 datasets show the effectiveness of our proposed method at three traditional image understanding tasks.
Single Conspiracy Number Analysis in Checkers

Game playing provides the medium for a variety of algorithms to formulate play decisions that surpass human expert. However, the reasons that distinguish the winning and losing positions remain actively researched which leads to the utilization of the search “indicator”. Conspiracy number search (CNS) and proof number search (PNS) had been popularly adopted as the search indicators in MIN/MAX and AND/OR tree, respectively. However, their limitations had encouraged the need for an alternative search indicator. The single conspiracy number (SCN) is a search indicator inspired by CNS and PNS which measure the difficulty of getting MIN/MAX value over a threshold point for a current root node. Recently, SCN had been successfully applied in Chinese chess to analyze both the progress pattern and long-term position. In this paper, analysis of the SCN within the game of checkers is conducted where different SCN threshold values and varying depth of the search tree were considered. Checkers was chosen due to its smaller search space and contain a rule that affects the shape of the search tree. The experimental results show that the SCN values stabilize as the depth of the search tree increases whether the player is in winning, drawing or losing position.