Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Modified Deep Neural Network Architecture Performance for Speech Recognition

Recently, Deep Neural Networks (DNN) has been widely used for pattern recognition and classification applications because of its high accuracy. Here in this paper, we propose four different Deep Neural Network (DNN) architectures and comparison is made between these four proposed DNN architectures in terms of accuracy and training time. The proposed DNN models are evaluated for speech recognition application using TIDIGITS corpus. Mel-Frequency Cepstral Coefficients (MFCC) technique is used to extract feature vectors of speech data. It is observed that modified triangular architecture gave the highest accuracy of 99.31 % as compared to other architectures while the triangular architecture gave the least training time of 49.72 sec. Furthermore, results of proposed DNN architecture is compared with the existing Hidden Markov Model based speech recognition and the proposed DNN provide an increased accuracy of 2.33%.
Modeling of Discrete Interaction Processes in Traveling-Wave Tube with Resonator Slow-Wave Structures of Microwave and EHF Ranges

In this paper, a model describing the discrete interaction of a traveling-wave tube (TWT) with resonator slow-wave structures (SWSs) is considered. The problems of development of TWT are analyzed, and the approaches to finding solutions for these problems are considered. When simulating a TWT section, a chain of sextapoles with combined field and current terminals is used. The developed model served as a basis for the development of the software complex VEGA which will be used later for the design of TWT in microwave and EHF bands.
A Trustworthy Content Caching and Bandwidth Allocation Scheme With Edge Computing for Smart Campus

Edge computing enabled mobile social networks can improve mobile users’ quality of experience (QoE) when they exchange and share contents with each other. However, as some edge nodes are trustless or even malicious, mobile users are attacked by these untrustful nodes. Meanwhile, due to the limited caching and bandwidth capacities of edge nodes, the competition among mobile users also brings a new challenge to allocate the resource efficiently. To tackle the above problems, this paper presents a trustworthy edge caching and bandwidth allocation scheme for mobile users. First, to guarantee the reliability of each edge node, a trust evaluation mechanism is designed to assess the security of each edge node by the direct trust and indirect trust. With the trust evaluation, each mobile user selects satisfied edge nodes as candidates to cache contents. Then, the reverse auction game is employed to find the optimal edge node for each mobile user. The Bayesian equilibrium obtained via game analysis is used to determine the optimal strategy of each edge node. Finally, the simulation results show that the proposed scheme can not only improve the QoE of mobile users, but also prevent the attacks of malicious edge nodes.
Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks

In this work, we propose a graph-adaptive pruning (GAP) method for efficient inference of convolutional neural networks (CNNs). In this method, the network is viewed as a computational graph, in which the vertices denote the computation nodes and edges represent the information flow. Through topology analysis, GAP is capable of adapting to different network structures, especially the widely used cross connections and multi-path data flow in recent novel convolutional models. The models can be adaptively pruned at vertex-level as well as edge-level without any post-processing, thus GAP can directly get practical model compression and inference speed-up. Moreover, it does not need any customized computation library or hardware support. Finetuning is conducted after pruning to restore the model performance. In the finetuning step, we adopt a self-taught knowledge distillation (KD) strategy by utilizing information from the original model, through which, the performance of the optimized model can be sufficiently improved, without introduction of any other teacher model. Experimental results show the proposed GAP can achieve promising result to make inference more efficient, e.g., for ResNeXt-29 on CIFAR10, it can get 13X model compression and 4.3X practical speed-up with marginal loss of accuracy.
A Multi-Platform Framework for Artificial Intelligence Engines in Automotive Systems

It is becoming increasingly evident that data science and artificial intelligence are key technologies in the future of automotive industry. Artificial Intelligence (AI) successes are significant, with some limitations in terms of algorithms portability and code industrialization for mass production and deployment. This is because there are many domains where several software development process standards need to be applied.The approach presented in this paper tries to overcome this issue by creating a unique platform with multiple artificial intelligence engines. By using state-of-the-art AI libraries, together with target-independent software, tackles the challenge of driving context recognition in embedded systems, and uses it as a validation method.
Modality-Specific Working Memory Systems Verified by Clinical Working Memory Tests

Objective This study was to identify whether working memory (WM) can be clearly subdivided according to auditory and visual modality. To do this, we administered the most recent and universal clinical WM measures in a mixed psychiatric sample. Methods A total of 115 patients were diagnosed on the basis of DSM-IV diagnostic criteria and with MINI-Plus 5.0, a structured diagnostic interview. WM subtests of Korean version of Wechsler Adult Intelligence Scale-IV and Korean version of Wechsler Memory Scale-IV were administered to assess WM. Confirmatory factor analysis (CFA) was used to observe whether WM measures fit better to a one-factor or two-factor model. Results CFA results demonstrated that a two factor model fits the data better than one-factor model as expected. Conclusion Our study supports a modality model of WM, or the existence of modality-specific WM systems, and thus poses a clinical significance of assessing both auditory and visual WM tests.
Intraoperative Thermography of the Electrical Stimulation Mapping: A Safety Control Study

A standard procedure for continuous intraoperative monitoring of the integrity of the corticospinal tracts by eliciting muscle responses is the electric stimulation mapping (ESM). However, standard ESM protocols are ineffective in 20% of young children. We have developed a novel, highly efficient paradigm consisting of short-time burst (30 ms) of high frequency (500 Hz) and high peak current (≤100 mA), which may cause local tissue overheating. The presented safety control study was therefore designed. The infrared thermography camera captured to-be-resected cortex of 13 patients in vivo during ESM. Thermograms were image processed to reveal discrete ESM thermal effect of currents from 10 to 100 mA. Peak 100 mA currents induced a maximal increase in temperature of 3.1 °C, 1.23±0.72 °C in average. The warming correlated with stimulating electrode resistance ( ${p}< \textsf {0.001}$ ). The measurement uncertainty was estimated ± 1.01 ºC for the most skeptical conditions. The histopathological evaluation of stimulated tissue (performed in all cases) did not show any destructive changes. Our study demonstrates the ability of the thermographic camera to measure the discrete thermal effect of the ESM. The results provide evidence for the safety of the proposed protocol for full range currents with minimal risk of brain tissue damage.
ANÁLISE DE SÉRIES TEMPORAIS APLICADA A PRODUÇÃO PUXADA

Resumo: O objetivo desse estudo é verificar o impacto do uso de análise de séries temporais em um cenário de produção puxada. Para tanto, o estudo foi realizado em uma indústria do ramo alimentício,fabricante de biscoitos, por meio da comparação de modelos de suavização exponencial, ARIMA, suas combinações aritméticas e combinações por pesos. A análise abrange o período de 216 semanas e a aplicação de série temporal, para um total de nove famílias diferentes de produtos. Após a aplicação dos modelos e suas comparações por indicadores de acuracidade, verificou-se que o modelo de suavização exponencial apresentou o melhor resultado. O modelo foi utilizado para incrementar a acurácia de quantidades de estoques de produtos acabados, possibilitando que os dados de previsão sejam usados como um período firme, caracterizando um sistema de produção puxada. Na prática, verificou-se uma economia na ordem de R$ 181.355,50 no trimestre. Nesse sentido, pode-se observar como métodos de previsão de demanda podem ser empregados para organizar o sistema de produção.
A robust 3-D motion estimation with stereo cameras on a robot manipulator

Accurately estimating the 3-D pose (position and orientation) of a moving object with reference to the world frame (or robot base frame) based on a sequence of stereo images taken by cameras mounted on the end-effector of a robot manipulator is considered. Emphasis is given to the 3-D pose estimation in the presence of measurement noise in 2-D images and camera position errors due to the random noise involved in the joint angles of a robot manipulator. To this end, a set of discrete Kalman filter equations is derived, based on the following: (1) the orientation error of the object frame due to the measurement noise in 2-D images is modeled with reference to the camera frame; (2) an extended Jacobian matrix is formulated by combining the result of (1) with the orientation error of the end-effector frame; and (3) the rotational motion of an object, which is nonlinear in nature, is linearized based on quaternions. Motion parameters are computed from the estimated quaternions based on the iterated least-squares method. Simulation results show a significant reduction in estimation errors. <<ETX>>
Codonopsis pilosula Polysaccharide Attenuates Tau Hyperphosphorylation and Cognitive Impairments in hTau Infected Mice

Codonopsis pilosula polysaccharide (CPPs), a natural products with potentially lower toxicity and better bioavailability has been used in traditional Chinese medicine for 1000s of years and a neuroprotective polysaccharide mitigates tau pathology in Alzheimer’s disease (AD) mouse model. However, whether CPPs can relieve AD pathology and cognitive defects remains poorly understood. Here we reported that CPPs remarkably increased the cell viability and PP2A activity, decreased tau phosphorylation in HEK 293/tau cells. Next, we employed an adeno-associated virus serotype 2 (AAV2)-induced expression of human full length tau (hTau) in C57/BL6 mice to mimic AD tau pathology. One month intragastric administration of CPPs significantly increased PP2A activity and reduced tau phosphorylation at Ser199, Ser202/Thr205 (AT8) and Thr231 in hippocampus of AAV2-hTau infected mice. Furthermore, behavioral tests revealed that CPPs rescued hTau overexpression induced cognitive defects while CPPs significantly increased the fEPSP slope and synaptic proteins including synaptotagmin and synaptophysin. Together, our data suggest that CPPs might prevent AD-like tau hyperphosphorylation via activation of PP2A and attenuates AD-like cognitive impairments through restoring the synaptic plasticity and synaptogenesis. In conclusion, our findings suggest that CPPs might be a potential candidate compound for the treatment of tau related diseases.
Translation attenuation by minocycline enhances longevity and proteostasis in old post-stress-responsive organisms

Aging impairs the activation of stress signaling pathways (SSPs), preventing the induction of longevity mechanisms late in life. Here, we show that the antibiotic minocycline increases lifespan and reduces protein aggregation even in old, SSP-deficient Caenorhabditis elegans by targeting cytoplasmic ribosomes, preferentially attenuating translation of highly translated mRNAs. In contrast to most other longevity paradigms, minocycline inhibits rather than activates all major SSPs and extends lifespan in mutants deficient in the activation of SSPs, lysosomal or autophagic pathways. We propose that minocycline lowers the concentration of newly synthesized aggregation-prone proteins, resulting in a relative increase in protein-folding capacity without the necessity to induce protein-folding pathways. Our study suggests that in old individuals with incapacitated SSPs or autophagic pathways, pharmacological attenuation of cytoplasmic translation is a promising strategy to reduce protein aggregation. Altogether, it provides a geroprotecive mechanism for the many beneficial effects of tetracyclines in models of neurodegenerative disease. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
Adaptive sliding control of an experimental underwater vehicle

It is shown that adaptive extensions of sliding control are effective for precise control of underwater vehicles through experiments with an actual vehicle. Adaptive sliding control permits direct nonlinear control system design, including online parameter estimation. Experimental results are detailed which were obtained from a tethered underwater vehicle equipped with a precision broadband acoustic navigation system and three-degree-of-freedom attitude instrumentation controlled through a real-time network of transputers. Simulation and experimental trials show online determination of vehicle parameters such as mass and nonlinear drag. <<ETX>>
Dense xUnit Networks

Deep net architectures have constantly evolved over the past few years, leading to significant advancements in a wide array of computer vision tasks. However, besides high accuracy, many applications also require a low computational load and limited memory footprint. To date, efficiency has typically been achieved either by architectural choices at the macro level (e.g. using skip connections or pruning techniques) or modifications at the level of the individual layers (e.g. using depth-wise convolutions or channel shuffle operations). Interestingly, much less attention has been devoted to the role of the activation functions in constructing efficient nets. Recently, Kligvasser et al. showed that incorporating spatial connections within the activation functions, enables a significant boost in performance in image restoration tasks, at any given budget of parameters. However, the effectiveness of their xUnit module has only been tested on simple small models, which are not characteristic of those used in high-level vision tasks. In this paper, we adopt and improve the xUnit activation, show how it can be incorporated into the DenseNet architecture, and illustrate its high effectiveness for classification and image restoration tasks alike. While the DenseNet architecture is extremely efficient to begin with, our dense xUnit net (DxNet) can typically achieve the same performance with far fewer parameters. For example, on ImageNet, our DxNet outperforms a ReLU-based DenseNet having 30% more parameters and achieves state-of-the-art results for this budget of parameters. Furthermore, in denoising and super-resolution, DxNet significantly improves upon all existing lightweight solutions, including the xUnit-based nets of Kligvasser et al.
Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation

High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to achieve good classification performance. In this paper, multiple kernel learning-based low rank representation at superpixel level (Sp_MKL_LRR) is proposed to improve the classification accuracy for hyperspectral images. Superpixels are generated first from the hyperspectral image to reduce noise effect and form homogeneous regions. An optimal superpixel kernel parameter is then selected by the kernel matrix using a multiple kernel learning framework. Finally, a kernel low rank representation is applied to classify the hyperspectral image. The proposed method offers two advantages. (1) The global correlation constraint is exploited by the low rank representation, while the local neighborhood information is extracted as the superpixel kernel adaptively learns the high-dimensional manifold features of the samples in each class; (2) It can meet the challenges of multiscale feature learning and adaptive parameter determination in the conventional kernel methods. Experimental results on several hyperspectral image datasets demonstrate that the proposed method outperforms several state-of-the-art classifiers tested in terms of overall accuracy, average accuracy, and kappa statistic.
Imaging and Modelling of L3-L4 Spine: Simulation

The lumbar region is highly susceptible to disc degeneration due to it supporting body weight and loads from daily activities. Disc degeneration itself is a complex subject and understanding the biomechanics of the condition is integral in providing corrective treatments for those affected. Measurement of Range of Motion (RoM) and loads acting on lumbar spine are essential in studying the effects of disc degeneration on vertebra. This study comprises of Range of Motion measurement via an in vivo method and generation of a Finite Element Model of L3L4 lumbar spine to be used for analysis. CT Scan image is utilized with help of image processing techniques to obtain a surface geometry of vertebral components. The completed model is validated with literature. Stresses in individual components are also obtained and compared with literature to ensure values are within safe levels (167–215 MPa for bone and 46 MPa for annulus). Related analysis and comparisons for RoM and stresses are detailed.
Enhancing INS/UWB Integrated Position Estimation Using Federated EFIR Filtering

In order to obtain accurate position information, a federated extended finite impulse response (EFIR) filter which includes sub-filters and main filter is proposed for inertial navigation system (INS)/ultrawideband (UWB)-integrated human positioning in this paper. Here, the EFIR filter is employed as the sub-filter, which is able to fusion the UWB- and INS-measured distances between the reference nodes and the target human. The main filter is used to provide the optimal INS position error estimation by fusing the outputs of the sub-filters, which is able to correct the INS position. The test results show that the performance of the proposed method is better than the traditional federated extended Kalman filter in position accuracy.
New speech/music discrimination approach based on warping transformation and ANFIS

Abstract Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents an effective approach based on an Adaptive Network-Based Fuzzy Inference System (ANFIS) for the classification stage required in a speech/music discrimination system. A new simple feature, called Warped LPC-based Spectral Centroid (WLPC-SC), is also proposed. Comparison between WLPC-SC and the classical features proposed in the literature for audio classification is performed, aiming to assess the good discriminatory power of the proposed feature. The length of the vector for describing the proposed psychoacoustic-based feature is reduced to a few statistical values (mean, variance and skewness), which are then transformed to a new feature space by LDA, with the aim of increasing the classification accuracy percentage. The classification task is performed applying ANFIS to the features in the transformed space. To evaluate the performance of the ANFIS system for speech/music discrimination, comparison to other commonly used classifiers is reported. The classification results for different types of music and speech signals show the good discriminating power of the proposed approach.
Influence of Industrialization and Environmental Protection on Environmental Pollution: A Case Study of Taihu Lake, China

In order to quantitatively study the effect of environmental protection in China since the twenty-first century and the environmental pollution projected for the next ten years (under the model of extensive economic development), this paper establishes a Bayesian regulation back propagation neural network (BRBPNN) to analyze the typical pollutants (i.e., cadmium (Cd) and benzopyrene (BaP)) for Taihu Lake, a typical Chinese freshwater lake. For the periods 1950–2003 and 1950–2015, the neural network model estimated the BaP concentration for the database with Nash-Sutcliffe model efficiency (NS) = 0.99 and 0.99 and root-mean-square error (RMSE) = 3.1 and 9.3 for the total database and the Cd concentration for the database with NS = 0.93 and 0.98 and RMSE = 45.4 and 65.7 for the total database, respectively. In the model of extensive economic development, the concentration of pollutants in the sediments of Taihu reached the maximum value at the end of the twentieth century and early twenty-first century, and there was an inflection point. After the early twenty-first century, the concentration of pollutants was controlled under various environmental policies and measures. In 2015, the environmental protection ratio of Cd and BaP reached 52% and 89%, respectively. Without environmental protection measures, the concentrations of Cd and BaP obtained from the neural network model is projected to reach 2015.5 μg kg−1 and 407.8 ng g−1, respectively, in 2030. Based on the results of this study, the Chinese government will need to invest more money and energy to clean up the environment.
Strain-Tunable Quantum Integrated Photonics

Semiconductor quantum dots are crucial parts of the photonic quantum technology toolbox because they show excellent single-photon emission properties in addition to their potential as solid-state qubits. Recently, there has been an increasing effort to deterministically integrate single semiconductor quantum dots into complex photonic circuits. Despite rapid progress in the field, it remains challenging to manipulate the optical properties of waveguide-integrated quantum emitters in a deterministic, reversible, and nonintrusive manner. Here we demonstrate a new class of hybrid quantum photonic circuits combining III–V semiconductors, silicon nitride, and piezoelectric crystals. Using a combination of bottom-up, top-down, and nanomanipulation techniques, we realize strain tuning of a selected, waveguide-integrated, quantum emitter and a planar integrated optical resonator. Our findings are an important step toward realizing reconfigurable quantum-integrated photonics, with full control over the quantum sources and the photonic circuit.