Miracle-dz/UniFAR-FTUs huggingface dataset:
Osteoporosis effect on posterior mandible in preimplanted area for postmenopausal females using cone-beam computed tomography

Background: Hormonal changes in women affect bone mineral density, especially during and postmenopausal years, which leads to susceptibility to osteoporosis which interfere with implantation. Aims: This study aims to use cone-beam computed tomography (CBCT) viewer program to predict patients with osteoporosis which may decrease the success rate of implantation and to evaluate osteoporosis effect on the posterior mandible. Settings and Design: This cross-sectional study consists of 60 Iraqi females who were divided into three groups: Group 1 consisted of 20 nonosteoporotic females aged 20–30 years as a control group, Group 2 consisted of 20 nonosteoporotic females aged 50 years and above, and Group 3 consisted of 20 osteoporotic females aged 50 years and above. Materials and Methods: The posterior mandibular first molar area was examined for alveolar bone height and radiographic density (RD) using CBCT. Statistical Analysis Used: Statistical analysis was computer assisted using the Statistical Package for the Social Sciences (SPSS version 21). Interclass correlation coefficient was used for calibration. Normally distributed variables were assessed using one-way ANOVA, and Dunnett test with control and not normally distributed were assessed by Kruskal–Wallis test, and multiple Mann–Whitney U-test with Bonferroni adjustment. Results: RD showed a significant difference between Group 2 and Group 3 (P = 0.000, MD = 158.554). On the other hand, alveolar bone height showed a significant difference between Group 1 and Group 2 (P = 0.039). Conclusions: RD in the posterior mandible first molar area is significantly affected in osteoporotic patients and it can be used as a predictor for the presence of osteoporosis using CBCT.
Using Mobile Technology to Provide Personalized Reminiscence for People Living With Dementia and Their Carers: Appraisal of Outcomes From a Quasi-Experimental Study

Background Dementia is an international research priority. Reminiscence is an intervention that prompts memories and has been widely used as a therapeutic approach for people living with dementia. We developed a novel iPad app to support home-based personalized reminiscence. It is crucial that technology-enabled reminiscence interventions are appraised. Objective We sought to measure the effect of technology-enabled reminiscence on mutuality (defined as the level of “closeness” between an adult living with dementia and their carer), quality of carer and patient relationship, and subjective well-being. Methods A 19-week personalized reminiscence intervention facilitated by a program of training and a bespoke iPad app was delivered to people living with dementia and their family carers at their own homes. Participants (N=60) were recruited in dyads from a cognitive rehabilitation team affiliated with a large UK health care organization. Each dyad comprised a person living with early to moderate dementia and his or her family carer. Outcome measurement data were collected at baseline, midpoint, and intervention closure. Results Participants living with dementia attained statistically significant increases in mutuality, quality of carer and patient relationship, and subjective well-being (P<.001 for all 3) from baseline to endpoint. Carers attained nonsignificant increases in mutuality and quality of carer and patient relationship and a nonsignificant decrease in subjective well-being. Conclusions Our results indicate that individual-specific reminiscence supported by an iPad app may be efficient in the context of early to moderate dementia. A robust randomized controlled trial of technology-enabled personalized reminiscence is warranted.
Swim Stroke Analytic: Front Crawl Pulling Pose Classification

In this work, we automatically distinguish the efficient high elbow pose from dropping one in pulling phase of front crawl stroke in front view amateurly recorded videos. This task is challenging due to the aquatic environment and missing depth information. We predict the pull's efficiency through multiclass svm and random forest classifiers given arms key positions and angles as the feature set. We evaluate our approach over a labeled dataset of video frames taken from 25 members of masters' swim club at Ryerson University with different levels of expertise and physiological characteristics. Our results show the effectiveness of our approach with random forest classifier, yielding 67% accuracy.
A Study of Subjective Video Quality at Various Spatial Resolutions

In this paper we present the BVI-SR video database, which contains 24 unique video sequences at a range of spatial resolutions up to UHD-1 (3840p). These sequences were used as the basis for a large-scale subjective experiment exploring the relationship between visual quality and spatial resolution when using three distinct spatial adaptation filters (including a CNN-based super-resolution method). The results demonstrate that while spatial resolution has a significant impact on mean opinion scores (MOS), no significant reduction in visual quality between UHD-1 and HD resolutions for the super-resolution method is reported. A selection of image quality metrics were benchmarked on the subjective evaluations, and analysis indicates that VIF offers the best performance.
Image Fusion of X-Ray and Electron Tomograms

With electron tomography, we can reconstruct a three-dimensional (3D) volume of a specimen from a series of its two-dimensional (2D) projection images on the nanoscale. In a scanning transmission electron microscope (STEM), element-specific maps and mass-contrast projections can be simultaneously acquired from the X-ray spectrometer and electron detector. The X-ray tomogram has high chemical specificity but low signal-to-noise ratio (SNR), while the electron tomogram has poor compositional information but high SNR. In this paper, we adopt and modify a regression-based image fusion algorithm to combine these two complementary modalities, so that the fused version would maintain both high chemical specificity and high SNR. We demonstrate that our method improves reconstruction quality on an experimental dataset of a core-shell nanoparticle. Specifically, it delivers tomograms with sharper edges and smoother fore- and background, and hence can enable easier and more accurate 3D characterization of such nanostructures.
Training and Prediction Data Discrepancies: Challenges of Text Classification with Noisy, Historical Data

Industry datasets used for text classification are rarely created for that purpose. In most cases, the data and target predictions are a by-product of accumulated historical data, typically fraught with noise, present in both the text-based document, as well as in the targeted labels. In this work, we address the question of how well performance metrics computed on noisy, historical data reflect the performance on the intended future machine learning model input. The results demonstrate the utility of dirty training datasets used to build prediction models for cleaner (and different) prediction inputs.
Output-Gate Projected Gated Recurrent Unit for Speech Recognition

In this paper, we describe the work on accelerating decoding speed while improving the decoding accuracy. Firstly, we pro-pose an architecture which we call Projected Gated Recurrent Unit (PGRU) for automatic speech recognition (ASR) tasks, and show that the PGRU could outperform the standard GRU consistently. Secondly, in order to improve the PGRU’s generalization, especially for large-scale ASR task, the Output-gate PGRU (OPGRU) is proposed. Finally, time delay neural network (TDNN) and normalization skills are found to be beneﬁ-cial to the proposed projected-based GRU. The ﬁnally proposed unidirectional TDNN-OPGRU acoustic model achieves 3.3% / 4.5% relative reduction in word error rate (WER) compared with bidirectional projected LSTM (BLSTMP) on Eval2000 / RT03 test sets. Meanwhile, TDNN-OPGRU acoustic model speeds up the decoding speed by around 2.6 times compared with BLSTMP.
An Evaluation Metric for Object Detection Algorithms in Autonomous Navigation Systems and its Application to a Real-Time Alerting System

An autonomous navigation system relies on a number of sensors including radar, LIDAR and a visible light camera for its operation. We focus our attention on the visible light camera in this work. Object detection is the key first step to processing the video input from the camera. Specifically, we address the problem of assessing the performance of object detection algorithms in hazardous driving conditions that an autonomous navigation system is expected to encounter in a realistic scenario. To this end, we propose a novel metric for quantifying the degradation in performance of an object detection algorithm under different weather conditions. Additionally’ we introduce a real-time method to detect extreme variations in performance of the algorithm that can be used to issue an alert. We evaluate the performance of our metric and alerting system and demonstrate its utility using the YOLOv2 object detection algorithm trained on the KITTI and virtual KITTI dataset.
Modulation of specific cell cycle phases in human embryonic stem cells by lncRNA RNA decoys

Recent studies have shown that long noncoding RNAs (lncRNAs) are crucial regulators of human embryonic stem cells (hESCs). However, modes of actions of lncRNAs in hESCs are not well illustrated. Here, we predicted a regulatory network in hESCs in which lncRNAs interact with TFs and thereby control the expressions of downstream targets of TFs. The predicted network is comprised of 2289 3‐motif subgraphs which are characterized by 3 nodes: (i) a lncRNA which is predicted to interact with (ii) a TF and (iii) a gene which is a target of TF and coexpressing with lncRNA. We performed functional annotation of the network by identifying hub nodes followed by pathway enrichment study, which unveiled an active G1‐S cell cycle phase transition‐specific subnetwork that encompasses 2 lncRNAs, MALAT1 and DANCR. Our analysis revealed that MALAT1 and DANCR might be playing key roles in G1‐S phase transition by acting as RNA decoy via interacting with crucial stemness maintaining TFs. We predicted that MALAT1 possibly compete with DNMT1 and CDCA7 genes to bind to E2F1 thereby interrupting repression of DNMT1 and activation of CDCA7 by E2F1 in hESCs, whereas DANCR possibly competes with IPO7 gene to bind to MYC thereby interrupting MYC‐mediated activation of IPO7 in hESCs. Both of these are conjectured to contribute to rapid G1‐S phase transition aiding in stemness maintenance of hESCs. This study presents a crucial TF target cross talks mediated by lncRNAs in hESCs regulating its properties which needs further investigation.
TDNN-based Multilingual Speech Recognition System for Low Resource Indian Languages

India is a diverse and multilingual country. It has vast linguistic variations, spoken across its billion plus population. Lack of resources in terms of transcribed speech data, phonetic pronunciation dictionary or lexicon, and text collection has hindered the development and improvement of the ASR systems for In-dic languages. With the Interspeech 2018 Special Session: Low Resource Speech Recognition Challenge for Indian Languages, efforts have been made to solve this issue to an extent. In this paper, we explore the fact that the shared phonetic properties of the languages are essential for improved ASR performance. We build a multilingual Time Delay Neural Network (TDNN) system that uses combined acoustic modeling and language-speciﬁc information to decode the input test sequences. Using this approach, for Tamil, Telugu and Gujarati language we obtain a Word Error Rate (WER) of 16.07%, 17.14%, 17.69%, respectively, which was the second best system at the challenge.
Accelerating Similarity-Based Discriminant Analysis Using Class-Specific Prototypes

Reducing the dimensionality of data is among the most common preprocessing steps for a number of machine learning tasks. A linear similarity-based discriminant analysis (S-LDA) algorithm was recently proposed, significantly improving the discriminative ability of the learned low-dimensional representation over classical techniques, such as LDA. S-LDA builds upon the notion of similarity and allows for exploiting higher-order statistics, while being notably robust to outliers. However, optimizing S-LDA‘s objective requires the calculation of the pairwise similarities between all the training data. This quickly becomes a significant burden and limits the ability of the method to scale to larger datasets. In this paper we propose using prototypes, that act as similarity anchors, to reduce the number of required similarity calculations. We experimentally demonstrate that the proposed technique is capable of reducing the optimization time by two orders of magnitude without harming the discriminative ability of the learned representations.
A Priori SNR Estimation Based on a Recurrent Neural Network for Robust Speech Enhancement

Speech enhancement under highly non-stationary noise conditions remains a challenging problem. Classical methods typically attempt to identify a frequency-domain optimal gain function that suppresses noise in noisy speech. These algorithms typically produce artifacts such as “musical noise” that are detrimental to machine and human understanding, largely due to inaccurate estimation of noise power spectra. The optimal gain function is commonly referred to as the ideal ratio mask (IRM) in neural-network-based systems, and the goal becomes estimation of the IRM from the short-time Fourier transform amplitude of degraded speech. While these data-driven techniques are able to enhance speech quality with reduced artifacts, they are frequently not robust to types of noise that they had not been exposed to in the training process. In this paper, we propose a novel recurrent neural network (RNN) that bridges the gap between classical and neural-network-based methods. By reformulating the classical decision-directed approach, the a priori and a posteriori SNRs become latent variables in the RNN, from which the frequency-dependent estimated likelihood of speech presence is used to update recursively the latent variables. The proposed method provides substantial enhancement of speech quality and objective accuracy in machine interpretation of speech.
Physiology and Pathophysiology of Steroid Biosynthesis, Transport and Metabolism in the Human Placenta

The steroid hormones progestagens, estrogens, androgens, and glucocorticoids as well as their precursor cholesterol are required for successful establishment and maintenance of pregnancy and proper development of the fetus. The human placenta forms at the interface of maternal and fetal circulation. It participates in biosynthesis and metabolism of steroids as well as their regulated exchange between maternal and fetal compartment. This review outlines the mechanisms of human placental handling of steroid compounds. Cholesterol is transported from mother to offspring involving lipoprotein receptors such as low-density lipoprotein receptor (LDLR) and scavenger receptor class B type I (SRB1) as well as ATP-binding cassette (ABC)-transporters, ABCA1 and ABCG1. Additionally, cholesterol is also a precursor for placental progesterone and estrogen synthesis. Hormone synthesis is predominantly performed by members of the cytochrome P-450 (CYP) enzyme family including CYP11A1 or CYP19A1 and hydroxysteroid dehydrogenases (HSDs) such as 3β-HSD and 17β-HSD. Placental estrogen synthesis requires delivery of sulfate-conjugated precursor molecules from fetal and maternal serum. Placental uptake of these precursors is mediated by members of the solute carrier (SLC) family including sodium-dependent organic anion transporter (SOAT), organic anion transporter 4 (OAT4), and organic anion transporting polypeptide 2B1 (OATP2B1). Maternal–fetal glucocorticoid transport has to be tightly regulated in order to ensure healthy fetal growth and development. For that purpose, the placenta expresses the enzymes 11β-HSD 1 and 2 as well as the transporter ABCB1. This article also summarizes the impact of diverse compounds and diseases on the expression level and activity of the involved transporters, receptors, and metabolizing enzymes and concludes that the regulatory mechanisms changing the physiological to a pathophysiological state are barely explored. The structure and the cellular composition of the human placental barrier are introduced. While steroid production, metabolism and transport in the placental syncytiotrophoblast have been explored for decades, few information is available for the role of placental-fetal endothelial cells in these processes. With regard to placental structure and function, significant differences exist between species. To further decipher physiologic pathways and their pathologic alterations in placental steroid handling, proper model systems are mandatory.
Detecting Double Jpeg Compression with Same Quantization Matrix Based on Dense Cnn Feature

Detection of double JPEG compression with same quantization matrix has been regarded as a challenging task in digital image forensics because there are very few modification cues in the tampered images especially when the compression quality factor is low. In order to solve this problem, a comprehensive feature representation based on the dense CNN framework is proposed, which is sensitive to the artifacts caused by double JPEG compression and is not related to the image content. With the appropriate network design and contributing to the characteristics of average pooling, dense connection and transition, the proposed network can differentiate double JPEG compression artifacts accurately. Experiment results on the two datasets have demonstrated that the proposed feature outperforms several state-of-the-art approaches investigated.
An Active Feature Transformation Method for Attitude Recognition of Video Bloggers

Video blogging is a form of unidirectional communication where a video blogger expresses his/her opinion about different issues. The success of a video blog is measured using metrics like the number of views and comments by online viewers. Researchers have highlighted the importance of non-verbal behaviours (e.g. attitudes) in the context of video blogging and showed that it correlates with the level of attention (number of views) gained by a video blog. Therefore, an automatic attitude recognition system can help potential video bloggers to train their attitudes. It can also be useful in developing video blogs summarization and searching tools. This study proposes a novel Active Feature Transformation (AFT) method for automatic recognition of attitudes (a form of non-verbal behaviour) in video blogs. The proposed method transforms the Mel-frequency Cepstral Coefﬁcient (MFCC) features for the classi-ﬁcation task. The Principal Component Analysis (PCA) transformation is also used for comparison. Our results show that AFT outperforms PCA in terms of accuracy and dimensionality reduction for attitude recognition using linear discrimination analysis, 1-nearest neighbour and decision tree classiﬁers.
General Utterance-Level Feature Extraction for Classifying Crying Sounds, Atypical & Self-Assessed Affect and Heart Beats

In the area of computational paralinguistics, there is a growing need for general techniques that can be applied in a variety of tasks, and which can be easily realized using standard and publicly available tools. In our contribution to the 2018 Interspeech Computational Paralinguistic Challenge (ComParE), we test four general ways of extracting features. Besides the standard ComParE feature set consisting of 6373 diverse attributes, we experiment with two variations of Bag-of-Audio-Words representations, and define a simple feature set inspired by Gaussian Mixture Models. Our results indicate that the UAR scores obtained via the different approaches vary among the tasks. In our view, this is mainly because most feature sets tested were local by nature, and they could not properly represent the utterances of the Atypical Affect and Self-Assessed Affect SubChallenges. On the Crying Sub-Challenge, however, a simple combination of all four feature sets proved to be effective.
Robust Room Layout Estimation from a Single Image with Geometric Hints

Estimation of room layout suffers from heavy occlusions and clutters in indoor scenes. In this paper, we propose a deep network that combines textures and geometric hints to predict the surface layout from a single image. Our method consists of three steps. First, depths and normals are extracted from the input RGB image. Secondly, a multi-channel FCN (MC-FCN) is presented to integrate these geometric hints for semantic surface segmentation. Thirdly, an optimization framework is adopted to refine the layout estimation. The results on two commonly used benchmark datasets demonstrate the robustness of our method on complex scenes.
Co-Segmentation of Non-Homogeneous Image Sets

In this paper, we formulate image co-segmentation as a classification problem in an unsupervised framework with the classes being the common foreground and the remaining regions in the image set. We first find a set of superpix-els across all images with high feature similarity such that the constituent superpixels in individual images are spatially compact and label them as seed for the common foreground. Those superpixels with high background probability are labeled as respective seeds for multiple background classes. Seed computation here is unsupervised and automated unlike some semi-supervised methods. Then, we compute discriminative features that separate the initially labeled classes using linear discriminant analysis. We use these features to perform spatially constrained label propagation and obtain labels for the unlabeled regions, and iterate this process till the seed regions grow to the common object. Experimental results demonstrate excellent robustness properties even while processing non-homogeneous image sets where the common object is present only in majority of the images.
VoiceGuard: Secure and Private Speech Processing

With the advent of smart-home devices providing voice-based interfaces, such as Amazon Alexa or Apple Siri, voice data is constantly transferred to cloud services for automated speech recognition or speaker veriﬁcation. While this development enables intriguing new applications, it also poses signiﬁcant risks: Voice data is highly sensitive since it contains biometric information of the speaker as well as the spoken words. This data may be abused if not protected properly, thus the security and privacy of billions of end-users is at stake. We tackle this challenge by proposing an architecture, dubbed VoiceGuard , that efﬁciently protects the speech processing task inside a trusted execution environment (TEE). Our solution preserves the privacy of users while at the same time it does not require the service provider to reveal model parameters. Our architecture can be extended to enable user-speciﬁc models, such as feature transformations (including fMLLR), i-vectors, or model transformations (e.g., custom output layers). It also generalizes to secure on-premise solutions, allowing vendors to securely ship their models to customers. We provide a proof-of-concept implementation and evaluate it on the Resource Management and WSJ speech recognition tasks isolated with Intel SGX, a widely available TEE implementation, demonstrating even real time processing capabilities.