Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing acoustic scene classifiers using Metamorphic Relations

Context: Artificial Intelligence (AI) applications appear as one of the main demands for the software industry nowadays; within this context, speech recognition and acoustic scene detection and classification achieve near-human performance. However, performing systematic testing on these applications is challenging and very costly if we follow traditional testing methodologies. In this scenario, Metamorphic Testing presents an efficient approach to ensuring the quality of machine learning-based systems. Objective: analyze techniques and applications of metamorphic testing and propose metamorphic relations to perform verification and validation of acoustic scene classifiers. Method: the use of Design Science Research to provide iterative and incremental research development, through which the results achieved in the first cycle of research are presented. Results: in the first design cycle, the use of two metamorphic relations focused on attributes and samples permutation were adopted to verify and validate 6 learning algorithms in an acoustic scene classification system, wherein one of the applied relations, on the random forest algorithm, presented a violation, leading to prediction errors and 2.34% drop in its accuracy in one of the tests performed. In the second design cycle, three new relations based on acoustic variations were proposed to validate the audio attributes, where in all of them the ZCR attribute was more effective to deal with the proposed variations. Conclusion: At the end of the two cycles, our approach has revealed verification flaws and has also proven effective for validation purposes of the systems under test, allowing developers of acoustic scene classification systems to apply them to their learning components, audio extraction processes, and to the test and training dataset
Using Robust Networks to Inform Lightweight Models in Semi-Supervised Learning for Object Detection

A common trade-off among object detection algorithms is accuracy-for-speed (or vice versa). To meet our application’s real-time requirement, we use a Single Shot MultiBox Detector (SSD) model. This architecture meets our latency requirements; however, a large amount of training data is required to achieve an acceptable accuracy level. While unusable for our end application, more robust network architectures, such as Regions with CNN features (R-CNN), provide an important advantage over SSD models—they can be more reliably trained on small datasets. By fine-tuning R-CNN models on a small number of hand-labeled examples, we create new, larger training datasets by running inference on the remaining unlabeled data. We show that these new, inferenced labels are beneficial to the training of lightweight models. These inferenced datasets are imperfect, and we explore various methods of dealing with the errors, including hand-labeling mislabeled data, discarding poor examples, and simply ignoring errors. Further, we explore the total cost, measured in human and computer time, required to execute this workflow compared to a hand-labeling baseline.
Analytical Model and Simulation Based Analysis of a Doped Si DG TFET Exhibiting Lower Subthreshold Slope

A doped double gate uni-junction silicon-based novel Tunnel Field Effect Transistor (TFET) structure is proposed that exhibits lower subthreshold slope (10 mV/decade) and better ON to OFF current ratio (Ion/Ioff is in order of 107 at low supply voltage) compared to conventional double gate TFET structure. A simulation-based analysis of the proposed structure has been carried out with the help of 2D TCAD Sentaurus device simulator at different channel length values. A mathematical model is also developed to verify the result with simulation. Our proposed TFET structure is composed of one common p region which acts as both source and channel. Therefore the configuration is basically a p-n double gate TFET having a n+ pocket close to source side and another n+ pocket close to drain side. These pockets are created separately by using work function engineering technique on metal gate having different lengths and thicknesses and also by using high k and low k dielectric as oxide. The source side n+ pocket enhances tunneling probability thus increasing Ion and Ioff is controlled by another n+ pocket near drain side. Significantly lower subthreshold slope as observed from calibrated simulation of our proposed configuration makes this device more suitable for digital logic application.
Assessment of Objectively Measured Physical Activity as an Independent Estimator of Functional Status in Clinical Trials

Functional status of patients is an important concept in clinical trials. It subsumes functional capacity, which is traditionally estimated by exercise tests, and functional performance, which is often estimated by questionnaires. Objectively measured physical activity by means of wearables devices containing accelerometers (PA) have recently been proposed as a novel and advantageous way to estimate physical status including capacity and performance. There is nonetheless insufficient evidence of the association between PA and traditional ways to estimate functional status. In the ACTIVATE clinical trial, cycle ergometry tests were performed multiple times in all 267 patients, PA was measured for a week prior to each cycle ergometry test, and questionnaires were answered daily during the same week. Pearson’s correlation tests and clustering analysis revealed that PA, physical activity experience as assessed by questionnaires, and exercise endurance time as measured by the cycle ergometry test, are largely independent. Therefore, all three approaches together might achieve a complete assessment of the functional status of patients in clinical trials, as they each independently correlate with health-related quality of life and important clinical outcomes such as hospitalizations but are weakly associated among themselves.
Effect of (Mis)Matched Compression Speed on Speech Recognition in Bimodal Listeners

Automatic gain control (AGC) compresses the wide dynamic range of sounds to the narrow dynamic range of hearing-impaired listeners. Setting AGC parameters (time constants and knee points) is an important part of the fitting of hearing devices. These parameters do not only influence overall loudness elicited by the hearing devices but can also affect the recognition of speech in noise. We investigated whether matching knee points and time constants of the AGC between the cochlear implant and the hearing aid of bimodal listeners would improve speech recognition in noise. We recruited 18 bimodal listeners and provided them all with the same cochlear-implant processor and hearing aid. We compared the matched AGCs with the default device settings with mismatched AGCs. As a baseline, we also included a condition with the mismatched AGCs of the participants’ own devices. We tested speech recognition in quiet and in noise presented from different directions. The time constants affected outcomes in the monaural testing condition with the cochlear implant alone. There were no specific binaural performance differences between the two AGC settings. Therefore, the performance was mostly dependent on the monaural cochlear implant alone condition.
Scutellaria hsiehii (Lamiaceae), a New Species from Taiwan

A new species, Scutellaria hsiehii (Lamiaceae), was collected from central part of the island. Scutellaria hsiehii is close to S. playfairii Kudo, but can be distinguished by its slender procumbent stems, long petiole, long ovate leaves with 3-5 serration along the margin and nutlet coat with short grapping-hook tubercles. A taxonomic description, line drawing, pollen morphology in SEM observation, distribution information, and color photographs of the floral characters are provided.
The Alzheimer's disease‐associated C99 fragment of APP regulates cellular cholesterol trafficking

The link between cholesterol homeostasis and cleavage of the amyloid precursor protein (APP), and how this relationship relates to Alzheimer's disease (AD) pathogenesis, is still unknown. Cellular cholesterol levels are regulated through crosstalk between the plasma membrane (PM), where most cellular cholesterol resides, and the endoplasmic reticulum (ER), where the protein machinery that regulates cholesterol levels resides. The intracellular transport of cholesterol from the PM to the ER is believed to be activated by a lipid‐sensing peptide(s) in the ER that can cluster PM‐derived cholesterol into transient detergent‐resistant membrane domains (DRMs) within the ER, also called the ER regulatory pool of cholesterol. When formed, these cholesterol‐rich domains in the ER maintain cellular homeostasis by inducing cholesterol esterification as a mechanism of detoxification while attenuating its de novo synthesis. In this manuscript, we propose that the 99‐aa C‐terminal fragment of APP (C99), when delivered to the ER for cleavage by γ‐secretase, acts as a lipid‐sensing peptide that forms regulatory DRMs in the ER, called mitochondria‐associated ER membranes (MAM). Our data in cellular AD models indicates that increased levels of uncleaved C99 in the ER, an early phenotype of the disease, upregulates the formation of these transient DRMs by inducing the internalization of extracellular cholesterol and its trafficking from the PM to the ER. These results suggest a novel role for C99 as a mediator of cholesterol disturbances in AD, potentially explaining early hallmarks of the disease.
Cichorium intybus L. extract ameliorates testicular oxidative stress induced by lead acetate in male rats

Objective Oxidative stress has been suggested as a possible mechanism for the adverse effects of heavy metal toxicity on male reproduction. Cichorium intybus L. is used in Iranian folk medicine as a hepatoprotective agent as well as for its supposed fertility-enhancing properties. The present study was performed to investigate whether the ethanolic extract of C. intybus leaves could protect male rats against lead-induced testicular oxidative stress. Methods In this experimental study, adult Wistar rats were treated with 0.1% lead acetate in drinking water alone or with 50, 100, or 200 mg/ kg body weight of C. intybus extract via gavage once daily for 70 days. The weight of their reproductive organs, levels of serum hormones, histometric parameters of the seminiferous tubules, epidydimal sperm quality, and oxidative stress status were evaluated. Results The testis weight, seminiferous tubule diameter, epididymal sperm count, serum testosterone level, and testicular levels of superoxide dismutase and glutathione peroxidase were significantly reduced (p<0.05) in the lead-treated rats. Moreover, significantly (p<0.05) higher levels of malondialdehyde were observed in the lead-exposed group compared to the control. However, the co-administration of C. intybus ethanolic extract in lead-treated rats was associated with a significant improvement in reproductive parameters. Conclusion We conclude that C. intybus leaf extract has the potential to prevent lead-induced testicular toxicity and to suppress the adverse effects of lead on male reproductive health.
Next Basket Recommendation Model Based on Attribute-Aware Multi-Level Attention

Next basket recommendation is a challenging problem, mainly due to the relationships among the items in a basket almost not being considered in current research. In this article, we address next basket recommendation with a novel deep learning architecture. In particular, we consider both the short-term user interests and the long-term user preferences, and we design a new attention that considers the relationships among the items in a basket. We extensively evaluated the proposed model on two benchmark data sets, the Ta-Feng and JingDong datasets. The experimental results show that the proposed model outperforms several state-of-the-art next basket recommendation models. In the experiments of the modules’ effects, we also verify the effectiveness of each module. The model significantly improves the NDCG by 25.5 percentage points and 5 percentage points when compared with the uncompleted network model on the JingDong and Ta-Feng datasets, respectively; while in terms of the F1, the performance is improved by 14.6 and 23.4 percentage points, respectively.
Resource Allocation in Low Density Spreading Uplink NOMA via Asymptotic Analysis

Low density spreading non-orthogonal multiple- access (LDS-NOMA) is considered where K single-antenna user equipments (UEs) communicate with a base station (BS) over F fading sub-carriers. Each UE k spreads its data symbol over dk < F sub-carriers. Given dk, ∀k as design parameters, we characterize the resource allocation solutions that closely maximize the ergodic mutual information (EMI) in a scenario where the BS assigns resources solely based on the UEs’ pathlosses. Conducting analysis in asymptotic limit where F, K, and dk, ∀k converge to +∞ at the same rate, we present EMI in terms of a deterministic equivalent plus a residual term. The deterministic equivalent is given in terms of pathloss values and LDS-codes, and the small residual term scales as $\mathcal{O}\left( {\frac{1}{{{d^2}}}} \right)$ where d = min{dk, ∀k}. We formulate an optimization problem to get the set ${\bar {\mathcal{C}}^ * }$ of all spreading codes, irrespective of sparsity constraints, which maximize the deterministic equivalent of EMI. The spreading codes in ${\bar {\mathcal{C}}^ * }$ with desired sparsity are obtained via a simple and efficient algorithmic solution. In the finite regime, the residual term is shown to be a small incremental gain for the sparse solutions in ${\bar {\mathcal{C}}^ * }$, which is dictated mainly by dk, ∀k values. Accordingly, we show that the solutions in ${\bar {\mathcal{C}}^ * }$ with desired sparsity yield close to optimum values of EMI in the finite regime. Numerical simulation validates the attainable spectral efficiency enhancement as compared to regular, and random spreading.
Association between homocysteine levels and arterial stiffness in women with systemic sclerosis.

OBJECTIVES The purpose of this study was to evaluate homocysteine (Hcy) serum levels in women with systemic sclerosis (SSc) compared with healthy controls and to examine possible associations between Hcy and markers of arterial stiffness. METHODS A cross-sectional study was performed at a single hospital between November 2017 and May 2019: 62 women with SSc and 62 age- and sex-matched healthy controls were enrolled. Pulse wave velocity (PWV) was measured non-invasively along the carotid-femoral arterial segment. Serum Hcy was analysed using immunonephelo-metric method. RESULTS There was a significant difference in Hcy serum levels between SSc female patients and healthy controls (11.9±3.3 vs. 10.3±2.3 μmol/ml, p=0.002). Serum levels of Hcy were positively correlated with PWV (r=0.28, p<0.05), brain natriuretic peptide (BNP) (r=0.36, p<0.05) and disease duration (r=0.38, p<0.05), within the SSc group. In addition, in the linear regression model, higher Hcy concentrations were associated with higher PWV [β=0.74 95% CI (0.085, 1.394); p=0.027], BNP [β=0.04 95% CI (0.014, 0.072); p=0.004] and disease duration [β=0.18 95% CI (0.070, 0.300); p=0.002]. In multiple linear regression model adjusting for covariants, Hcy remained positively related to the PWV [β=0.033 95% CI (0.003, 0.062); p=0.031]. CONCLUSIONS Our findings revealed a positive correlation between Hcy serum levels and PWV, which indicates that high levels of Hcy may predispose to the development of vascular stiffness in patients with SSc.
Estimating wind dispersal potential in Ailanthus altissima: The need to consider the three-dimensional structure of samaras

Abstract Plant dispersal is a very important ecological phenomenon, as it can enable species to move away from the parent plant. This contributes to shaping communities, determining patterns of distribution, landscape configuration, plant invasions and evolutionary processes. Measuring dispersal distance directly is difficult and thus, diaspore morphology can be used to make estimates. Previous research on the topic often resorts to analysing the diaspore’s morphology as if it was a bi-dimensional structure; when in many cases, diaspores have three-dimensional qualities. In this study, we show how estimates of wind dispersal potential of Ailanthus altissima can be considerably improved using morphological variables that succeed in describing the three-dimensional nature of samaras. We suggest that this reasoning could be extensively applied to research involving not only other species, but also multi-specific scenarios with a wide range of diaspore morphologies.
A ‘Complete Blind’ No-Reference Stereoscopic Image Quality Assessment Algorithm

We propose a complete blind no-reference (NR) image quality assessment algorithm for assessing the perceptual quality of natural stereoscopic (S3D) images. Towards this end, we have generated an intermediate image from the left and right views, and hypothesize that the perceived quality of the S3D view close to that cyclopean image. We perform multi-steerable decomposition on cyclopean images and we compute the naturalness image quality evaluator (NIQE) score [1] and entropy score from each subband. Finally, the primitive quality scores of steerable subbands are pooled to obtain the overall perceptual quality score of an S3D image. The proposed algorithm is evaluated on the LIVE Phase I [2] and LIVE Phase II [3] stereoscopic image datasets and demonstrates its robust performance on both the datasets and across distortions. The proposed algorithm, which is a ‘complete blind’ model (neither requires pristine S3D images nor requires training on human opinion scores), is called the Multi-Orient NIQE based 3D image quality evaluator (MO-NIQE).
A wearable vision-based system for detecting hand-object interactions in individuals with cervical spinal cord injury: First results in the home environment

Cervical spinal cord injury (cSCI) causes the paralysis of upper and lower limbs and trunk, significantly reducing quality of life and community participation of the affected individuals. The functional use of the upper limbs is the top recovery priority of people with cSCI and wearable vision-based systems have recently been proposed to extract objective outcome measures that reflect hand function in a natural context. However, previous studies were conducted in a controlled environment and may not be indicative of the actual hand use of people with cSCI living in the community. Thus, we propose a deep learning algorithm for automatically detecting hand-object interactions in egocentric videos recorded by participants with cSCI during their daily activities at home. The proposed approach is able to detect hand-object interactions with good accuracy (F1-score up to 0.82), demonstrating the feasibility of this system in uncontrolled situations (e.g., unscripted activities and variable illumination). This result paves the way for the development of an automated tool for measuring hand function in people with cSCI living in the community.
Multiphase Computed Tomographic Angiography with Bone Subtraction Using 3D Multichannel Convolution Neural Networks*

Multiphase computed tomographic angiography (CTA) have been demonstrated to be a reliable imaging tool for evaluating cerebral collateral circulation that can be used to select acute ischemic patients for recanalization therapy. We proposed using bone subtraction techniques to visualize multiphase CTA for clinicians to make fast and consistent decisions in the imaging triage of acute stroke patients. A total of 40 multiphase brain CTA datasets were collected and processed by two bone subtraction methods. The reference method used pre-contrast (phase 0) scans to create ground truth bone masks by thresholding. The tested method used only contrast enhanced (phases 1, 2, and 3) scans to extract bone masks with two versions (U-net and atrous) of 3D multichannel convolution neural networks (CNNs) in a supervised deep learning paradigm for semantic segmentation. Half (n = 20) of the datasets were used to train and half (n = 20) were used to test the conventional 3D U-net and a patch-based 3D multichannel atrous CNN. The tested U-net and atrous CNNs achieved a mean intersection over union (IoU) scores of 90.0% +/- 2.2 and 93.9% +/- 1.2 respectively.Clinical Relevance—This bone subtraction technique helps to visualize CTA volumetric datasets in the form of full brain angiogram-like images to assist the clinicians in the emergency department for evaluating acute ischemic stroke patients.
Decoding movement direction from cortical microelectrode recordings using an LSTM-based neural network

Brain-machine interfaces (BMIs) allow individuals to communicate with computers using neural signals, and Kalman Filter (KF) are prevailingly used to decode movement directions from these neural signals. In this paper, we implemented a multi-layer long short-term memory (LSTM)based artificial neural network (ANN) for decoding BMI neural signals. We collected motor cortical neural signals from a nonhuman primate (NHP), implanted with microelectrode array (MEA) while performing a directional joystick task. Next, we compared the LSTM model in decoding the joystick trajectories from the neural signals against the prevailing KF model. The results showed that the LSTM model yielded significantly improved decoding accuracy measured by mean correlation coefficient (0.84, p < 10-7) than the KF model (0.72). In addition, using a principal component analysis (PCA)-based dimensionality reduction technique yielded slightly deteriorated accuracies for both the LSTM (0.80) and KF (0.70) models, but greatly reduced the computational complexity. The results showed that the LSTM decoding model holds promise to improve decoding in BMIs for paralyzed humans.
Automated Bioreactor System for the Cultivation of Autologous Tissue-Engineered Vascular Grafts

In an aging society, diseases associated with irreversible damage of organs are frequent. An increasing percentage of patients requires bioartificial tissue or organ substitutes. Tissue engineering products depend on a well-defined process to ensure successful cultivation while meeting high regulatory demands. The goal of the presented work is the development of a bioreactor system for the cultivation of tissue-engineered vascular grafts (TEVGs) for autologous implantation and transition from a lab scale setup to standardized production. Key characteristics include (i) the automated reliable monitoring and control of a wide-range of parameters regarding implant conditioning, (ii) easy and sterile setup and operation, (iii) reasonable costs of disposables, and (iv) parallelization of automated cultivation processes. The presented prototype bioreactor system provides comprehensive physiologically conditioning, sensing, and imaging functionality to meet all requirements for the successful cultivation of vascular grafts on a productional scale.
Recurrent Neural Network for Contaminant Type Detector in Surface Electromyography Signals

A surface Electromyography (sEMG) contaminant type detector has been developed by using a Recurrent Neural Network (RNN) with Long Short-Term (LSMT) units in its hidden layer. This setup may reduce the contamination detection processing time since there is no need for feature extraction so that the classification occurs directly from the sEMG signal. The publicly available NINAPro (Non-Invasive Adaptive Prosthetics) database sEMG signals was used to train and test the network. Signals were contaminated with White Gaussian Noise, Movement Artifact, ECG and Power Line Interference. Two out of the 40 healthy subjects’ data were considered to train the network and the other 38 to test it. Twelve models were trained under a -20dB contamination, one for each channel. ANOVA results showed that the training channel could affect the classification accuracy if SNR = -20dB and 0dB. An overall accuracy of 97.72% has been achieved by one of the models.