Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Synthetic Speech Workload with Oculo-motor Indices: Preliminary Observations for Japanese Speech

Pupillometry has recently been introduced as a method to evaluate cognitive workload of synthetic speech. Prior research conducted on English speech indicates that in noisy listening conditions, pupil dilation is significantly higher for synthetic speech as compared to natural speech. In a lab-based listening experiment, we evaluated participants’ (n=16) pupil responses to Japanese speech (natural vs. synthetic) at three different signal-to-noise levels (-1dB, -3dB and -5dB). Our research expands on previous work by evaluating pupillary responses both in terms of temporal changes in pupil size and degree of pupil oscillations. We observe statistically significant differences in pupil sizes at the recall stage between each type of speech. For pupil oscillations, we register statistically significant differences in frequency power spectrum densities (PSDs). Our investigation proposes an expansion of the current synthetic speech evaluation methods that are based on pupillary responses and outlines possible avenues for future research that arise from the findings of this work.
Graph Design: The Data-ink Ratio and Expert Users

Graphical depictions of data are common but there is little empirical work that has examined how graph design principles are instantiated by graph makers. The data-ink ratio is one popular measure of graphical information content, where the “ink” related to data is divided by the total amount of “ink” in the graph. Expert interviews were conducted to examine graph use, creation, and opinions about the data-ink ratio concept. Interviewees had a variety of opinions and preferences with regard to graph design, many of which were dependent upon the specific circumstances of presentation. Most interviewees did not believe that high data-ink graph designs were superior. The results suggest that arguments regarding the data-ink ratio deal with the subjective issue of graph aesthetics.
Movement Entropy in a Gardening Design Task as a Diagnostic Marker for Mental Disorders: Results of a Pilot Study

Movement, actions, and intentions are important psychological skills in human behavior. Studies have shown correlations between movement activity and a variety of mental disorders. In this context, planning and designing of gardens and outdoor spaces as an intentional activity might play an important role as a marker for mental health. Thus, in this study, 16 subjects (8 female) aged between 19 and 60 were asked to do a gardening task in an experimentally constructed environment while their movement activity was recorded with a camera from a fixed viewpoint. Movement heatmaps and entropy then was calculated and correlated with mental state measured via the Global Severity Index (GSI) of the Brief Symptom Inventory (BSI-18) questionnaire. After finding an optimal grid size of the heatmaps, we were able to find a moderate negative correlation of r = -0.463 between these quantities in an overall of both genders, explaining 21.4 % of variance. After considering the gender of the test group, a noticeable gender effect could be revealed. We found a significant interaction effect of entropy with gender meaning that a lower movement entropy in a gardening task correlates with a higher mental distress for men, but lower for women. Multivariate regression found that this model explained 77.44 % of variance (R = 0.88). Despite of these promising results, further investigations in this area should overcome some limitations in this pilot study in the field of position tracking and movement feature extraction.
CONSEQUENCE OF OXYTOCIN INJECTIONS ON MINERALS CONCENTRATION IN SAHIWAL COW MILK

The major objective of this study was to find out the consequence of exogenous administration of oxytocin to Sahiwal cow, local breed of Pakistan (to enhance milk let down) on mineral composition. Milk samples were collected from two groups of eight animals under controlled atmosphere and feeding input. The analysis of fodder and feed samples were also carried out to support this study. Means of macro and micro minerals of fodder and feed samples were determined. One group of Sahiwal cows was subjected to intramuscular injection of oxytocin (20 IU). The milk obtained from oxytocin injected animals possessed significantly higher sodium and chloride with increment in ash content. Lactose content decreased by 0.39% and ash content increased by 0.08% in milk treated with oxytocin injections. Oxytocin administration increased the level of Na (19.74%), Cl (9.39%) and Cu (146.89%) while K content decreased (10.06%).
Challenges in Developing Software for the Swedish Healthcare Sector

Context: High-quality software is essential to the progressing digitalisation of the Swedish healthcare sector. Developing software with the desired high quality is far from trivial due to the sophisticated requirements of the domain. Problem: Studies on healthcare digitalisation challenges in Sweden and other countries, however, largely focus on the perceptions of healthcare professionals and patients and less on opinions of IT professionals. Method: In this exploratory study, we conducted semi-structured interviews with nine IT professionals about observed challenges in developing software for the Swedish healthcare sector. A qualitative analysis was performed to identify common themes. Results: We identified the prevalent challenges to be related to data integrity, privacy and security, rules and regulations, engineering usability, and software testing. Conclusion: The results suggest that further research is required regarding agile methods, efficient requirement engineering, and testing in eHealth as well as in privacy and usability engineering.
Deep Learning Type Convolution Neural Network Architecture for Multiclass Classification of Alzheimer's Disease

Alzheimer’s disease (AD) is one of the common medical issues that the world is facing today. This disease has a high prevalence of memory loss and cognitive decline primarily in the elderly. At present, there is no specific treatment for this disease, but it is thought that identification of it at an early stage can help to manage it in a better way. Several studies used machine learning (ML) approaches for AD diagnosis and classification. In this study, we considered the Open Access Series of Imaging Studies-3 (OASIS-3) dataset with 2,168 Magnetic Resonance Imaging (MRI) images of patients with very mild to different stages of cognitive decline. We applied deep learning-based convolution neural networks (CNN) which are well-known approaches for diagnosis-based studies. The model training was done by 70% of images and applied 10-fold cross-validation to validate the model. The developed architecture model has successfully classified the different stages of dementia images and achieved 83.3% accuracy which is higher than other traditional classification techniques like support vectors and logistic regression.
3.1n − o(n) circuit lower bounds for explicit functions

Proving circuit lower bounds has been an important but extremely hard problem for decades. Although it can be shown that almost every function f:F2n→F2 requires circuit of size Ω(2n/n) by a simple counting argument, it remains unknown whether there is an explicit function (for example, a function in NP) not computable by circuits of size 10n. In fact, a 3n−o(n) explicit lower bound by Blum (TCS, 1984) was unbeaten for over 30 years until a recent breakthrough by Find, Golovnev, Hirsch, and Kulikov (FOCS, 2016), which proved a (3+1/86)n−o(n) lower bound for affine dispersers, a class of functions known to be constructible in P. To obtain this improvement, Find, Golovnev, Hirsch, and Kulikov (FOCS, 2016) generalized the classical gate elimination method by keeping track of a bottleneck structure called troubled gates. In this paper, we prove a stronger lower bound 3.1n − o(n) for affine dispersers. To get this result, we strengthen the gate elimination approach for the (3+1/86)n−o(n) lower bound, by a more sophisticated case analysis that significantly decreases the number of troubled gates introduced during the elimination procedure. Intuitively, our improvement relies on three observations: adjacent troubled gates can be eliminated together; the gates eliminated are usually connected; and the hardest cases during gate elimination have nice local properties to prevent the introduction of new troubled gates.
Analysis of Recent Re-Identification Architectures for Tracking-by-Detection Paradigm in Multi-Object Tracking

: Person re-identiﬁcation is a vital module of the tracking-by-detection framework for online multi-object tracking. Despite recent advances in multi-object tracking and person re-identiﬁcation, inadequate attention was given to integrating these technologies to provide a robust multi-object tracker. In this work, we combine modern state-of-the-art re-identiﬁcation models and modeling techniques on the basic tracking-by-detection framework and benchmark them on heavily occluded scenes to understand their effect. We hypothesize that temporal modeling for re-identiﬁcation is crucial for training robust re-identiﬁcation models for they are conditioned on sequences containing occlusions. Along with traditional image-based re-identiﬁcation methods, we analyze temporal modeling methods used in video-based re-identiﬁcation tasks. We also train re-identiﬁcation models with different embedding methods, including triplet loss, and analyze their effect. We benchmark the re-identiﬁcation models on the challenging MOT20 dataset containing crowded scenes with various occlusions. We provide a thorough assessment and investigation of the usage of modern re-identiﬁcation modeling methods and prove that these methods are, in fact, effective for multi-object tracking. Compared to baseline methods, results show that these models can provide robust re-identiﬁcation proved by improvements in the number of identity switching, MOTA, IDF1, and other metrics.
Image Synthesisation and Data Augmentation for Safe Object Detection in Aircraft Auto-landing System

The feasibility of deploying object detection to interpret the environment is questioned in several missioncritical applications leading to raised concerns about the ability of object detectors in providing reliable and safe predictions of the operational environment, regardless of weather and light conditions. The lack of a comprehensive dataset, which causes class imbalance and detection difficulties of hard examples, is one of the main reasons of accuracy loss in attitude safe object detection. Data augmentation, as an implicit regularisation technique, has been shown to significantly improve object detection by increasing both the diversity and the size of the training dataset. Despite the success of data augmentation in various computer vision tasks, applying data augmentation techniques to improve safety has not been sufficiently addressed in the literature. In this paper, we leverage a set of data augmentation techniques to improve the safety of object detection. The aircraft in-flight image data is used to evaluate the feasibility of our proposed solution in realworld safety-required scenarios. To achieve our goal, we first generate a training dataset by synthesising the images collected from in-flight recordings. Next, we augment the generated dataset to cover real weather and lighting changes. Introduction of artificially produced distortions is also known as corruptions and has since recently been an approach to enrich the dataset. The introduction of corruptions, as augmentations of weather and luminance in combination with the introduction of artificial artefacts, is done as an approach to achieve a comprehensive representation of an aircraft’s operational environment. Finally, we evaluate the impact of data augmentation on the studied dataset. Faster R-CNN with ResNet-50-FPN was used as an object detector for the experiments. An AP@[IoU=. 5:. 95] score of 50.327% was achieved with the initial setup, while exposure to altered weather and lighting conditions yielded an 18.1% decrease. The introduction of the conditions into the training set led to a 15.6% increase in comparison to the score achieved from exposure to the conditions.
Domain Adaptation for Traffic Density Estimation

Convolutional Neural Networks have produced state-of-the-art results for a multitude of computer vision tasks under supervised learning. However, the crux of these methods is the need for a massive amount of labeled data to guarantee that they generalize well to diverse testing scenarios. In many real-world applications, there is indeed a large domain shift between the distributions of the train (source) and test (target) domains, leading to a significant drop in performance at inference time. Unsupervised Domain Adaptation (UDA) is a class of techniques that aims to mitigate this drawback without the need for labeled data in the target domain. This makes it particularly useful for the tasks in which acquiring new labeled data is very expensive, such as for semantic and instance segmentation. In this work, we propose an end-to-end CNN-based UDA algorithm for traffic density estimation and counting, based on adversarial learning in the output space. The density estimation is one of those tasks requiring per-pixel annotated labels and, therefore, needs a lot of human effort. We conduct experiments considering different types of domain shifts, and we make publicly available two new datasets for the vehicle counting task that were also used for our tests. One of them, the Grand Traffic Auto dataset, is a synthetic collection of images, obtained using the graphical engine of the Grand Theft Auto video game, automatically annotated with precise per-pixel labels. Experiments show a significant improvement using our UDA algorithm compared to the model’s performance without domain adaptation. The code, the models and the datasets are freely available at https://ciampluca.github. io/unsupervised counting.
Physicochemical characteristics of Ivo River, Ebonyi state, Nigeri

A study was carried out on the physiochemical characteristics of Ivo River in Ivo Local Government Area of Ebonyi State, Nigeria to determine the present level of pollution of the river water due to incidental industrial, agricultural and human activities. The results showed that pH, temperature, conductivity, total dissolved solids, dissolved oxygen, potassium, calcium, silica, sulphate and chloride which had value ranges of 7.2 – 7.4, 27 – 28.2 0C, 40.4-150μs/cm, 22 – 39 ppm, 2.4 – 9.0 ppm, 0.07 – 0.17 ppm, 0.1 – 4.0 ppm, 1.007- 1.48 ppm, 4.55 – 65.88 ppm, and 31.62 – 47.13 ppm respectively, were below the maximum permissible standards. The range of value of turbidity (0.8 – 60.2 NTU), total suspended solids (6.1 – 19.14 ppm) and sodium (82 – 89 ppm) were above the standards of WHO but below that of Federal Environmental Protection Agency (FEPA) and Department of Petroleum Resources (DPR) standards. Good refuse and sewage disposal practices, proper management and control of industrial wastes emanating from rock blasting and lead-zinc mining should be encouraged in the area. International Journal of Natural and Applied Sciences Vol. 1(2) 2005: 145-149
Smartphone-based Approach for Automatic Focus Assessment in NIR Fundus Images Targeted at Handheld Devices

Mobile fundus imaging devices can play an important role in the decentralization of eye diseases screening methods, increasing the accessibility of telemedicine solutions in this area. Since image focusing is crucial to obtain an optimal retinal image, this work presents a smartphone-based approach for automatic focus assessment of NIR retinal images, acquired by a prototype of a handheld fundus camera device called EyeFundusScope (EFS) A009. A DCT-based focus metric is proposed and compared against a group of Gradient-based, Statistical-based, and Laplacian-based functions in the same experimental setup. The paper also presents the EFS image acquisition logic and the protocol for creating the necessary NIR dataset with the optic disc region around the centre of the image. The results were obtained within 853 images acquired from 8 volunteers. The developed method combined with other features, and a SVM classifier in a Machine Learning approach which attained an AUC of 0.80, has shown to be a viable solution to integrate into the EFS mobile application.
Bacterial Growth and Siderophore Production in Bacteria: An Analytical Model

We have analyzed the impact of quorum sensing and resource dependency on the production of critically crucial for bacteria fitness compounds (siderophores). We have built two siderophore production models (quorum sensing and resource dependency) and linked them with Monod’s growth model. As a result, siderophore accumulation is explicitly expressed through bacterial concentration N, which allows direct experimental verification. A nutrient-dependent model predicts three siderophore accumulation phases, which accompany bacterial growth: slow accumulation for [N0, Nth], fast accumulation for [Nth, K/2], and slow or no accumulation for [K/2, K). Here N0 is the initial bacterial concentration, K is the carrying capacity. A quorum-sensing model predicts two regimes of siderophore accumulation: relatively slow accumulation for [N0, Ncr] and much faster non-linear accumulation for [Ncr, K). Ncr and Nth are model parameters. Ncr has an “absolute” value. It is dependent on bacterial strain only. Nth has a “relative” value. In addition to the bacterial strain, it also depends on inoculums concentration and the initial nutrient concentration. Such as models predict entirely different behavior, experimental data may help differentiate between these mechanisms.
Real-time Monocular 6DoF Tracking of Textureless Objects using Photometrically-enhanced Edges

We propose a novel real-time edge-based 6DoF tracking approach for 3D rigid objects requiring just a monocular RGB camera and a CAD model with material information. The technique is aimed at low-texture or textureless, pigmented objects. It works even when under strong illumination, motion, and occlusion challenges. We show how preprocessing the model’s texture can improve tracking and apply region-based ideas like localized segmentation to improve the edge-based pipeline. This way, our technique is able to find model edges even under fast motion and in front of high-gradient backgrounds. Our implementation runs on desktop and mobile. It only requires one CPU thread per object tracked simultaneously and requires no GPU. It showcases a drastically reduced memory footprint when compared to the state of the art. To show how our technique contributes to the state of the art, we perform comparisons using two publicly available benchmarks.
A Lightweight Real-time Stereo Depth Estimation Network with Dynamic Upsampling Modules

Deep learning based stereo matching networks achieve great success in the depth estimation from stereo image pairs. However, current state-of-the-art methods usually are computationally intensive, which prevents them from being applied in real-time scenarios or on mobile platforms with limited computational resources. In order to tackle this shortcoming, we propose a lightweight real-time stereo matching network for disparity estimation. Our network adopts the efficient hierarchical Coarse-To-Fine (CTF) matching scheme, which starts matching from the low-resolution feature maps, and then upsamples and refines the previous disparity stage by stage until the full resolution. We can take the result of any stage as output to trade off accuracy and runtime. We propose an efficient hourglass-shaped feature extractor based on the latest MobileNet V3 to extract multiresolution feature maps from stereo image pairs. We also propose to replace the traditional upsampling method in the CTF matching scheme with the learning-based dynamic upsampling modules to avoid blurring effects caused by conventional upsampling methods. Our model can process 1242×375 resolution images with 3568 FPS on a GeForce GTX 1660 GPU, and outperforms all competitive baselines with comparable runtime on the KITTI 2012/2015 datasets.
Simple Matrix Factorization Collaborative Filtering for Drug Repositioning on Cell Lines

The discovery of new biological interactions, such as interactions between drugs and cell lines, can improve the way drugs are developed. Recently, there has been important interest for predicting interactions between drugs and targets using recommender systems; and more specifically, using recommender systems to predict drug activity on cellular lines. In this work, we present a simple and straightforward approach for the discovery of interactions between drugs and cellular lines using collaborative filtering. We represent cellular lines by their drug affinity profile, and correspondingly, represent drugs by their cell line affinity profile in a single interaction matrix. Using simple matrix factorization, we predicted previously unknown values, minimizing the regularized squared error. We build a comprehensive dataset with information from the ChEMBL database. Our dataset comprises 300,000+ molecules, 1,200+ cellular lines, and 3,000,000+ reported activities. We have been able to successfully predict drug activity, and evaluate the performance of our model via utility, achieving an Area Under ROC Curve (AUROC) of near 0.9.
Exploring the consequences of patient commitment to cardiac rehabilitation program: A qualitative study

BACKGROUND: Commitment to implementing cardiac rehabilitation (CR) plays an important role in managing the problems caused by heart diseases. Commitment to the treatment plan implementation is accompanied by numerous positive consequences. This study was carried out to explain the consequences of commitment to CR. MATERIALS AND METHODS: Data were collected through semi-structured individual interviews with 26 participants (13 CR specialists and 13 patients), using purposeful sampling. Interview questions focused on the factors influencing the formation of commitment to the CR and its consequences. Conventional content analysis with Graneheim and Lundman's approach was used to analyze the data. To obtain data trustworthiness, Lincoln and Guba's criteria were used. RESULTS: The findings were classified in three categories: purposeful of a purposeful behavior structure (sense of controlling and managing the condition, sense of responsibility, sense of security), formation of active performance structure (interaction between the patient-family-treatment team, stabilization of behavior and prevention of intermittent behavior, no cessation of the treatment plan and adherence to it, directing behaviors and adaptation to conditions) and achieving dynamic and effective care (active follow-up of the treatment plan, sense of satisfaction, reduced readmission, reduced costs, improved quality of life, reduced anxiety and concerns about treatment failure, increased self-care ability). CONCLUSION: The results show that the patient's commitment to CR is accompanied by optimal consequences. The results of this study can help design the training plan for the CR staff and develop the clinical practice guidelines to provide educational-care approaches to patients to reinforce their commitment.