Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Human Response in Feature Binding Experiment Using EEG Data

The ability to predict future human responses via analysis of electroencephalogram (EEG) signals is an active area of research from the viewpoint of understanding human brain and behavior, brain computer interface (BCI) applications, and neuro-rehabilitation of patients suffering with neurological symptoms and disorders. In this work, we predict human responses via analysis of EEG signals of healthy young adults collected during an experiment of visual feature binding. The subjects were asked to detect changes in color-shape binding of four objects shown in two successive screens with a gap of 1500 ms. The behavioral experiment comprised 96 trials as EEG data was collected simultaneously from a 21-electrode machine. The EEG data was pre-processed and artifacts were removed using independent component analysis (ICA). Feature reduction was carried out using principal component analysis (PCA) and linear discriminant analysis (LDA). A number of machine learning classifiers were trained on the EEG data of 15 subjects to predict the response of the subject in the color-shape binding experiment. Results are promising and show that EEG signal analysis can help in building relevant tools for the neuro-rehabilitation of subjects suffering with impairments in visual feature binding or tools for BCI applications.
Investigation of W-band High Power TWT Amplifier with Broadband Output Window

In this paper, the planar three-beam is adopted to investigate the potential of high output power and broad bandwidth for W-band staggered double-vane slow wave structure (SDV-SWS) travelling wave tube (TWT). A simple input/output coupler is proposed for the three-beam SDV-SWS, which consists of non-taper structures for the TWT. The high frequency simulations are used to design SWS and broadband diamond output window. With the center frequency of 95 GHz, the high transmission frequency range of 80 GHz to 114 GHz can be achieved for the super bandwidth. The 3D PIC simulation for the W-band SDV-TWT combined with the broadband diamond output window are designed and optimized for its output characteristics with the sensitively and stability thoroughly. With the beam voltage of 22 kV and beam current of 3 × 0.15 A, the output power can be reached above 1000 W within the frequency range of 89GHz and 111 GHz. The highest output power is obtained at 108GHz of 1141 W, with the gain of 43.5 dB, the efficiency of 11.5%, and 3 dB bandwidth of 28 GHz. Thus, the physical model designed in this paper can be used as the engineering prototype to develop the high power and broadband SDV-TWT in W-band in future.
Mobile Robot Object Recognition in The Internet of Things based on Fog Computing

Mobile robot object recognition has attracted significant attention in the internet of things recently, in which there are many challenging tasks, such as the objects, the communication networks and the computer system. It still needs a large of computation, communication and storage capability for the whole system. In this paper, we propose a scheme of mobile robot object recognition in IOT and use edge nodes to process the data from robot vision instead of cloud computing. Besides, we adopt YOLOv3 as the main algorithm in the edge nodes to process the video data. The video from the camera on the robot is transmitted to the fog node by a Wi-Fi router. The advantages of using edge nodes for computation local robot clusters are reliability, real-time capability and flexibility compared with the cloud. In our experiment, we efficiently train the computer model using COCO database deployed to GPU. The robot using the proposed model could recognize the objects in real-time. We achieve mAP of 31.0% and response time of 52ms that illustrates the state-of-art performance of the proposed framework.
Evaluation of the Lombard effect model on synthesizing Lombard speech in varying noise level environments with limited data

Lombard speech is intelligible speech produced by humans in noises. In this study, we focus on mimicking Lombard speech from natural neutral speech under backgrounds with varying noise levels to increase its intelligibility in these noises. Other approaches map corresponding speech features from the neutral speech to Lombard speech, which can only apply for an individual noise level, and cannot reveal feature tendencies. Instead, we implement a Lombard effect model to continuously estimate feature values with varying noise levels. The techniques, which are based on coarticulation, a source-filter model with MRTD and spectral-GMM, are used to easily modify features of the neutral speech to obtain their tendencies. Finally, these features are synthesized by STRAIGHT vocoder to obtain Lombard speech. The mimicking quality is evaluated in subjective listening experiments on similarity, naturalness, and intelligibility. The evaluation results show that the proposed method could convert neutral speech into Lombard speech in varying noise levels, which obtains comparable results with the state-of-the-art method.
Robotic Grasping Using Semantic Segmentation and Primitive Geometric Model Based 3D Pose Estimation

Due to the rapid development of hardware and software technologies, the machine automation has greatly improved in the past few decades. Robotic manipulators have been used in many factories for fully automated production. Compared with traditional sensor-based robot control, visual servo control has several advantages: high flexibility and precision, and robustness to calibration errors. In this paper, we present an eye-in-hand robotic arm for object recognition and grasping applications. The 2D images and depth information are acquired with an RGB-D camera for instance segmentation using convolutional neural network. The object pose estimation is achieved by ICP using primitive geometric models. Experiments carried out with different categories of objects in the real scene environment have demonstrated the feasibility of the proposed technique.
Implementation and Performance Assessment of Fast Active Power Injection Method for Type 4 Wind Turbine based on Real-time Simulation

This paper deals with the implementation of a Fast Active Power Injection (FAPI) controller in a Type-4 Wind Turbine. Two different FAPI controllers, droop-based and a modified derivative-based controller are proposed and investigated under real-time simulation platform. The implementation is done in a Real-Time Digital Simulator (RTDS) by using the functionalities of RSCAD software. The IEEE 9 bus system is taken as a case study to quantitatively check the suitability of the implemented controller. The response of the wind turbine observed in EMT simulations is compared against the response obtained via numerical simulations with a generic wind turbine model built-in DIg SILENT PowerFactory software. The details of the model implemented in RSCAD provides better insight on capturing the impacts of controller parameters. Obtained results clearly demonstrate how the proposed controller can effectively improve the dynamic frequency performance of the power system.
Mixture of CNN Experts from Multiple Acoustic Feature Domain for Music Genre Classification

In the field of music information retrieval (MIR), audio spectrogram can carry a great deal of information about the music content so as to be a robust visual representation for music signal. Recently, many research literatures show that convolutional neural network (CNN) has ability to capture indicative acoustic patterns from spectrogram input, and make remarkable performance on MIR-related tasks such as music genre classification (MGC). In this paper, we continue the line of research to explore different types of spectrograms, to emphasize different characteristics of music genre for the MGC task. To jointly leverage all of these features, in this paper, a mixture of experts (MoE) system is proposed. More formally, a set of MGC models can be derived by using the various spectrogram-based statistics. Then we treat each model as an individual expert. Accordingly, a neural mixture model is introduced to collect and compile the predictions from the expert models, and then to output a final decision for a given music to be predicted. In a nutshell, our major contributions in this paper are at least twofold. On one hand, we comprehensively examine several spectrogram-based features for the MGC task. On the other hand, a neural-based MoE system, which can dynamically decide the weighting factor for each expert system, is proposed to enhance the performance of the MGC task11The source code is available at https://github.com/superlyy/apsipa_2019. Experimental results demonstrate that the proposed framework not only can achieve success results than individual expert models, but has ability to provide a comparable classification accuracy to the SOTA systems.
Alleviate Cross-chunk Permutation through Chunk-level Speaker Embedding for Blind Speech Separation

Speaker-independent speech separation (SI-SS) refers to recovering speech of unknown speakers from multispeaker mixtures. The well-known deep clustering (DC) based SI-SS methods cast the speech separation problem into a clustering problem in an embedding space, where time-frequency (T-F) features are encoded as high-dimensional vectors (T-F embeddings). In training stage, the T-F embeddings from the same speaker are trained to be close to each other, otherwise far away. In prediction stage, the T-F embeddings are partitioned into clusters by K-Means, where each cluster corresponds to an unknown speaker from the mixture. To reduce the latency, the T-F embeddings are usually extracted on short speech chunks rather than utterances, which unfortunately leads to a cross-chunk permutation (CCP) problem. In this study, we focus on solving this CCP problem by using the speaker labels as the auxiliary supervision information to train a deep model to map the T-F embeddings of one cluster to one chunk-level speaker embedding (CL-SE). Therefore, in prediction stage, the generated CL-SEs are used to calculate the similarity between each cluster over consecutive chunks. As a result, the speech chunks with the more similar CL-SEs are concatenated to yield the complete utterances. The evaluation is conducted on the well-known WSJ0-2mix and the signal-to-distortion ratio (SDR) is adopted for performance evaluation. Noted that we obtain 41% SDR gain over DC baseline and up to 32% over other speaker-aware methods in open conditions.
Super Twisting Algorithm Based Sliding Mode Control Method for Single-Phase Dynamic Voltage Restorers

This paper presents a super twisting algorithm (STA) based sliding mode control (SMC) method for single-phase dynamic voltage restorers (DVRs). The proposed control method eliminates chattering and achieves constant switching frequency. The STA is usually considered as high-order sliding mode (HOSM). The main advantage of HOSM is that its acts on the high-order derivatives of the sliding surface while preserving the inherent advantages of first-order sliding modes and reducing the chattering phenomenon while increasing the accuracy. Hence, the consequence of applying STA in DVR control is that a continuous control input is obtained which can be compared with the triangular carrier signal to produce the pulse width modulation (PWM) signals. The theoretical considerations and analytical results are verified through computer simulations. It is shown that the load voltage is maintained at desired level under voltage sags and voltage swells in the grid.
TM-ZC: A Deep Learning-Based Predictor for the Z-Coordinate of Residues in α-Helical Transmembrane Proteins

Z-coordinate is an important structural feature of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-helical transmembrane proteins (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-TMPs), which is defined as the distance from a residue to the center of the biological membrane. Since the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-TMP structures from both experimental solved and computational predicted approaches still cannot cover the requirements in relevant research fields, z-coordinate prediction provides an opportunity to partly descript <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-TMP structures based on their sequences, further contributes to function annotation and drug target discovery. For the purpose of improving the prediction accuracy and providing a convenient tool, we proposed a deep learning-based predictor (TM-ZC) for the z-coordinate of residues in <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-TMPs. TM-ZC used the one-hot code and the PSSM as input features for a convolutional neural network (CNN) regression model. The experimental results demonstrated that TM-ZC was a powerful predictor, which is simple and fast, and achieved a considerable performance: the average error was 1.958, the percent of prediction error within 3Å was 77.461%, and the correlation coefficient (CC) was 0.922. We further discussed the usefulness of TM-ZC predicted z-coordinate and found its high consistency with topology structure and the enhancement of the surface accessibility prediction.
Winter Diet Variation and Overlap of Sympatric Red Deer and Sika Deer in Northeast China

ABSTRACT The red deer (Cervus elaphus) and sika deer (C. nippon) are two important and endangered herbivores in northeast China. It is unclear whether potential dietary competition exists between them when they are sympatric in winter. We used micro-histological analysis of faecal samples to determine diet composition and dietary overlap of the red deer and sika deer in northeast China. We found that red deer had a more diverse diet than sika deer, and consumed more shrubs throughout the winter. Euonymus spp. was the most stable and principal food item for both red and sika deers in winter, while the proportions of the three other major food species (Acer spp., Abies spp. and Pinus spp.) were significantly different between the deer species as well as between different winter periods. The proportions of coniferous browse of sika deer increased significantly from early to late winter (20.9 vs 45.4%), while for red deer they remained stable (18.3 vs 23.7%). Crude protein in the red and sika deer's diet was over 7.5% of dry matter in whole winter. Red and sika deer take different strategies to meet their requirements for crude protein. Red deer consumed in winter consistently more shrubs to obtain crude protein and to minimize the intake of tannins, whereas sika deer increased coniferous browse consumption in late winter to meet the crude protein requirement regardless of higher intake of tannin. Our results indicate a high degree of dietary overlap between red and sika deers (range 0.88–0.95), and suggest its further increase in late winter.
A New Digital Predistortion Algorithms Scheme of Feedback FIR Cross-Term Memory Polynomial Model for Short-Wave Power Amplifier

Due to the non-linear behavior, high fractional bandwidth, relatively poor circuit matching, large standing wave of output port, large fluctuation of power tube temperature of the short-wave power amplifier (PA), it is difficult to achieve the expected performance in HF (High Frequency) communication to maintain high efficiency and improve the linearity of HF power amplifier at the same time. However, there are no optimization measures for the above problems in the traditional digital pre-distortion (DPD) model, which is always recognized as an effective technique to improve system linearity and efficiency. In this paper, the new Feedback FIR Cross-term Memory Polynomial Model (MPM) to compensate the characteristics of the short-wave PA is presented. The experimental results show that, comparing with the traditional MPM DPD look-up table (LUT) method and the Generalized Memory Polynomial Model (GMPM) LUT method, this model can improve IM3 of Adjacent Channels Power Ratio (ACPR) about 10 dB.
Comprehensive Drowsiness Level Detection Model Combining Multimodal Information

This paper presents a drowsiness detection model that is capable of sensing the entire range of stages of drowsiness, from weak to strong. The key assumption underlying our approach is that the sitting posture-related index can indicate weak drowsiness that drivers themselves do not notice. We first determined the sensitivity of the posture index and conventional indices for the stages of drowsiness. Then, we designed a drowsiness detection model combining several indices sensitive to weak drowsiness and to strong drowsiness, to cover all drowsiness stages. Subsequently, the model was trained and evaluated on a dataset comprised of data collected from approximately 50 drivers in simulated driving experiments. The results indicated that posture information improved the accuracy of weak drowsiness detection, and our proposed model using the driver’s blink and posture information covered all stages of drowsiness (F1-score 53.6%, root mean square error 0.620). Future applications of this model include not only warning systems for dangerously drowsy drivers but also systems which can take action before their drivers become drowsy. Since measuring the information requires no restrictive equipment such as on-body electrodes, the model presented here based on blink and posture information can be used in several practical applications.
A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing

Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing tasks, traditionally performed by employees or contractors, to a large group of smart-phone users by means of an open call. With the increasing complexity of the crowdsourcing applications, requesters find it essential to harness the power of collaboration among the workers by forming teams of skilled workers satisfying their complex tasks' requirements. This type of MCS is called Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on the aspect of team skills maximization. Other team formation studies on social networks (SNs) have only focused on social relationship maximization. In this paper, we present a hybrid approach where requesters are able to hire a team that, not only has the required expertise, but also is socially connected and can accomplish tasks collaboratively. Because team formation in CMCS is proven to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge about their SN neighbors and asks a designated leader to recruit a suitable team. The proposed algorithm is inspired from the optimal stopping strategies and uses the odds-algorithm to compute its output. Experimental results show that, compared to the benchmark exponential optimal solution, the proposed approach reduces computation time and produces reasonable performance results.
Semi-supervised Subject Recognition through Pseudo Label Generation in Ubiquitous Sensor Data

The task of gait-based subject recognition (SR) in ubiquitous sensor environments has become popular due to its wide range of applications in biometric authentication and smart home products. In recent times, a significant amount of work has been done in SR using supervised learning algorithms on datasets having high modality. However, the process of annotation for SR is difficult due to the challenges like privacy and high manual cost, which results in a scarcity of labeled data samples. Also, for the datasets having less modality, the task of SR in a semi-supervised domain is sparsely explored and challenging. In this work, we analyze the effect of these two factors (sparse labels and low modality) which are critical for SR in ubiquitous data. We select two datasets of ubiquitous data that are relatively unexplored in the context of SR. The datasets, namely OPPOR-TUNITY and Smartphone used to perform SR using conventional supervised learning algorithms to benchmark the results. Then we perform extensive experimentation to analyze the effect of the aforementioned factors over the task of SR by studying the variations in classification accuracies. Next, we propose our semi-supervised framework for SR based on the concept of pseudo labels to counter the adverse effects of low modality and lack of labels. Experimental results show that our approach offers up to 77% and 98% accuracy on the Smartphone and OPPORTUNITY dataset respectively.
ACOGNA2: A novel algorithm for maximizing accuracy in global network alignment

Proteins do not independently work, they interact with each other. Their interactions are modelled by protein-protein interaction (PPI) networks. PPI networks alignment problem has great significance in the biomedical field and has attracted the interest of many researchers. This article introduces a new algorithm called ACOGNA2 to global aligning the PPI networks. Experiments demonstrated that the proposed algorithm outperform the state-of-the-art algorithms.
Infections With Extracellular Trypanosomes Require Control by Efficient Innate Immune Mechanisms and Can Result in the Destruction of the Mammalian Humoral Immune System

Salivarian trypanosomes are extracellular parasites that affect humans, livestock, and game animals around the world. Through co-evolution with the mammalian immune system, trypanosomes have developed defense mechanisms that allow them to thrive in blood, lymphoid vessels, and tissue environments such as the brain, the fat tissue, and testes. Trypanosomes have developed ways to circumvent antibody-mediated killing and block the activation of the lytic arm of the complement pathway. Hence, this makes the innate immune control of the infection a crucial part of the host-parasite interaction, determining infection susceptibility, and parasitemia control. Indeed, trypanosomes use a combination of several independent mechanisms to avoid clearance by the humoral immune system. First, perpetuated antigenic variation of the surface coat allows to escape antibody-mediated elimination. Secondly, when antibodies bind to the coat, they are efficiently transported toward the endocytosis pathway, where they are removed from the coat proteins. Finally, trypanosomes engage in the active destruction of the mammalian humoral immune response. This provides them with a rescue solution in case antigenic variation does not confer total immunological invisibility. Both antigenic variation and B cell destruction pose significant hurdles for the development of anti-trypanosome vaccine strategies. However, developing total immune escape capacity and unlimited growth capabilities within a mammalian host is not beneficial for any parasite, as it will result in the accelerated death of the host itself. Hence, trypanosomes have acquired a system of quorum sensing that results in density-dependent population growth arrest in order to prevent overpopulating the host. The same system could possibly sense the infection-associated host tissue damage resulting from inflammatory innate immune responses, in which case the quorum sensing serves to prevent excessive immunopathology and as such also promotes host survival. In order to put these concepts together, this review summarizes current knowledge on the interaction between trypanosomes and the mammalian innate immune system, the mechanisms involved in population growth regulation, antigenic variation and the immuno-destructive effect of trypanosomes on the humoral immune system. Vaccine trials and a discussion on the role of innate immune modulation in these trials are discussed at the end.