Miracle-dz/UniFAR-FTUs huggingface dataset:
ILAPF: Incremental Learning Assisted Particle Filtering

This paper is concerned with dynamic system state estimation based on a series of noisy measurement with the presence of outliers. An incremental learning assisted particle filtering (ILAPF) method is presented, which can learn the value range of outliers incrementally during the process of particle filtering. The learned range of outliers is then used to improve subsequent filtering of the future state. Convergence of the outlier range estimation procedure is indicated by extensive empirical simulations using a set of differing outlier distribution models. The validity of the ILAPF algorithm is evaluated by illustrative simulations, and the result shows that ILAPF is more accurate and faster than a recently published state-of-the-art robust particle filter. It also shows that the incremental learning property of the ILAPF algorithm provides an efficient way to implement transfer learning among related state filtering tasks.
Human-Like Emotion Recognition: Multi-Label Learning from Noisy Labeled Audio-Visual Expressive Speech

To capture variation in categorical emotion recognition by human perceivers, we propose a multi-label learning and evaluation method that can employ the distribution of emotion labels generated by every human annotator. In contrast to the traditional accuracy-based performance measure for categorical emotion labels, our proposed learning and inference algorithms use cross entropy to directly compare human and machine emotion label distributions. Our audiovisual emotion recognition experiments demonstrate that emotion recognition can benefit from using a multi-label representation that fully uses both clear and ambiguous emotion data. Further, the results demonstrate that this emotion recognition system can (i) learn the distribution of human annotators directly; (ii) capture the humanlike label noise in emotion perception; and (iii) identify infrequent or uncommon emotional expression (such as frustration) from inconsistently labeled emotion data, which were often ignored in previous emotion recognition systems.
Robust Detection of Epileptic Seizures Using Deep Neural Networks

Robust detection of epileptic seizures in the presence of inevitable artifacts in Electroencephalogram (EEG) signals is addressed. The EEG dataset considered contains 300 signals recorded from 15 volunteers. Current seizure detection systems achieve good performance when the EEG data is entirely free of noise. However, their performance drastically decays with authentic EEG data polluted by real artifacts. We introduce a robust seizure detection method that can address clean and noisy data. The proposed method uses Long Short-Term Memory (LSTM) neural networks to extract the representative EEG features pertinent to seizures. Experimental results show that the proposed method beats existing methods by achieving 100% classification accuracy. Our method is also shown to be robust against the common EEG artifacts (e.g., muscle activities and eye-blinking) and white noise.
Procedural Generation of Game Maps With Human-in-the-Loop Algorithms

A key challenge in procedural content generation is to automatically evaluate whether the generated content has good quality. In this paper, we describe an approach that uses nonexpert workers to evaluate small portions of levels generated by an off-the-shelf generation system for the game of Infinite Mario Bros. Several such evaluated portions are then combined to form full levels of the game using a mathematical progression arc model. The composition of the small portions into full levels is done by accounting for the human-annotated information. We evaluated the approach using computational metrics as well as surveying human subjects playing the levels. The results show that the human computation approach is able to generate levels that are perceived by people to have better visual aesthetics and to be more enjoyable to play than existing approaches. Another contribution of our paper is a dataset of the small annotated levels that can be used in future research for learning models for evaluating machine-generated content.
Memory Access Pattern Profiling for Streaming Applications Based on MATLAB Models

In order to cope with the problems caused by the Data Deluge, for application-specific architectures it is important to design the system in such a way that the processing and memory subsystems are geared to each other. Especially for systems that need to fulfill high data throughput demands, there is a high risk, that otherwise there will be high degradation of the overall system performance caused by memory accesses. However especially for application-specific designs, it is possible to create a tailored memory subsystem by profiling the applications memory access patterns beforehand and then incorporating the information during the system design. For this purpose we present a data access pattern profiling framework, which can provide detailed information about an applications memory access patterns based on a MATLAB model of the application.
Smoothing Model Predictions Using Adversarial Training Procedures for Speech Based Emotion Recognition

Training discriminative classifiers involves learning a conditional distribution $p(y_{i}\vert\pmb{x}_{i})$, given a set of feature vectors $\pmb{x}_{i}$ and the corresponding labels $y_{i}, i=1\ldots N$. For a classifier to be generalizable and not overfit to training data, the resulting conditional distribution $p(y_{i}\vert\pmb{x}_{i})$ is desired to be smoothly varying over the inputs $\pmb{x}_{i}$. Adversarial training procedures enforce this smoothness using manifold regularization techniques. Manifold regularization makes the model's output distribution more robust to local perturbation added to a datapoint $\pmb{x}_{i}$. In this paper, we experiment with the application of adversarial training procedures to increase the accuracy of a deep neural network based emotion recognition system using speech cues. Specifically, we investigate two training procedures: (i) adversarial training where we determine the adversarial direction based on the given labels for the training data and, (ii) virtual adversarial training where we determine the adversarial direction based only on the output distribution of the training data. We demonstrate the efficacy of adversarial training procedures by performing a k-fold cross validation experiment on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) and a cross-corpus performance analysis on three separate corpora. Results show improvement over a purely supervised approach, as well as better generalization capability to cross-corpus settings.
Mrt-Based Joint Unicast and Multigroup Multicast Transmission in Massive Mimo Systems

We study joint unicast and multigroup multicast transmission in single-cell massive multiple-input-multiple-output (MIMO) systems, under maximum ratio transmission. For the unicast transmission, the objective is to maximize the weighted sum spectral efficiency (SE) of the unicast user terminals (UTs) and for the multicast transmission the objective is to maximize the minimum SE of the multicast UTs. These two problems are coupled to each other in a conflicting manner, due to their shared power resource and interference. To address this, we formulate a multiobjective optimization problem (MOOP). We derive the Pareto boundary of the MOOP analytically and determine the values of the system parameters to achieve any desired Pareto optimal point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource.
Finger Code Matching Algorithm Using Circular Tessellation

Due to the rising concern of data security and privacy, fingerprint recognition has been used for identity verification. This paper presents a minutiae-based finger code matching algorithm for identity verification. It describes the effect of using different circular neighborhood minutiae tessellation sizes to generate a finger code for identity verification using a fingerprint. The analyses of how the matching accuracy is affected using different sizes of circular neighborhood minutiae tessellations are described. A minutiae-based matching algorithm using multiple reference minutiae to generate a finger code is presented. The proposed finger code matching algorithm offers the advantage of improved matching speed due to the usage of binary codes and it can also be implemented on embedded platforms such as smart cards. The finger code matching algorithm obtained the highest matching accuracy of 7.03% Equal Error Rate (EER) using database DB1-b of Fingerprint Verification Competition 2002 (FVC2002).
DNN Based Embeddings for Language Recognition

In this work, we present a language identification (LID) system based on embeddings. In our case, an embedding is a fixed-length vector (similar to i-vector) that represents the whole utterance, but unlike i-vector it is designed to contain mostly information relevant to the target task (LID). In order to obtain these embeddings, we train a deep neural network (DNN) with sequence summarization layer to classify languages. In particular, we trained a DNN based on bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) layers, whose frame-by-frame outputs are summarized into mean and standard deviation statistics. After this pooling layer, we add two fully connected layers whose outputs correspond to embeddings. Finally, we add a softmax output layer and train the whole network with multi-class cross-entropy objective to discriminate between languages. We report our results on NIST LRE 2015 and we compare the performance of embeddings and corresponding i-vectors both modeled by Gaussian Linear Classifier (GLC). Using only embeddings resulted in comparable performance to i-vectors and by performing score-level fusion we achieved 7.3% relative improvement over the baseline.
Functional and structural properties of red color-related pigment-binding protein from the shell of Litopenaeus vannamei.

BACKGROUND A novel red color-related pigment-binding protein named LvPBP75 isolated from the shell of Litopenaeus vannamei has recently been identified as hemocyanin. However, information on the functional and structural properties of LvPBP75 is insufficient. This study aimed to elucidate the thermal properties and pigment-binding ability of LvPBP75. RESULTS LvPBP75 showed significant red color change after heat treatment with high concentrations of NaCl (>0.1 mol L-1 ), acidic (<5) or alkaline (>9) pH values and alcohols. LvPBP75 mRNA expression analysis revealed that expression level was highest in hepatopancreas and weakest in muscle. Reconstruction and structural analysis revealed that astaxanthin could bind to hemocyanin derived from the shell of L. vannamei but not to hemocyanins derived from the hepatopancreas or hemolymph of other invertebrates. Three-dimensional models of hemocyanin monomer displayed significant structural differences between native LvPBP75 and hemocyanin derived from shrimp hepatopancreas. CONCLUSION The results suggest a novel function of hemocyanin as binding with pigment and its involvement in L. vannamei shell color change. The pigment-binding ability of hemocyanins has species and tissue specificity, and their unique structural features play an important role in binding ability. © 2018 Society of Chemical Industry.
3D Object Structure Recovery via Semi-supervised Learning on Videos

This paper addresses the problem of joint 3D object structure and camera pose estimation from a single RGB image. Existing approaches typically rely on both images with 2D keypoint annotations and 3D synthetic data to learn a deep network model due to difﬁculty in obtaining 3D annotations. However, the domain gap between the synthetic and image data usually leads to a 3D object interpretation model sensitive to the viewing angle, occlusion and background clutter in real images. In this work, we propose a semi-supervised learning strategy to build a robust 3D object interpreter, which exploits rich object videos for better generalization under large pose variations and noisy 2D key-point estimation. The core design of our learning algorithm is a new loss function that enforces the temporal consistency constraint in the 3D predictions on videos. The exper-iment evaluation on the IKEA, PASCAL3D+ and our object video dataset shows that our approach achieves the state-of-the-art performance in structure and pose estimation.
Cloud Computing Adoption Challenges in the Banking Industry

Cloud computing is the most important part of today's information technology world that provides solutions to almost all the sectors for improving their business. This innovation helps organizations to enhance or redesign the services at a very low expense. This study investigates the integrated factors that impact in adopting Cloud computing technologies in banking industries in order to adopt innovation benefit. In this paper, the quantitative analysis were performed based on the questioners and answers given by 2358 banking industry related persons like bank managers, customers, higher management. The questionnaire is based to identify their opinion for the adoption of cloud computing for services in banking industry. Benefits and challenges of cloud adoption in banking industry are also identified. We have proposed a cloud adoption framework Cost, Organizational, Technological, Environmental and Decision-maker (COTED) factors. The findings of this research have revealed that all the identified factors have a significantly effect on the adoption of cloud computing technologies for banking industry. With the findings, cloud computing service providers will have a better understanding of what factors are most influencing for cloud computing adoption with relevant insight on current promotions.
MIMO Based Decoupling Strategy for Grid Connected Power Converters Controlled in the Synchronous Reference Frame

Power converters are frequently connected to the grid through a LCL filter, controlling its power transfer through a current control loop in the synchronous reference frame. In this reference frame, cross coupling terms appear between the current and voltages of the passive components, which, without a proper decoupling strategy, penalize the converter transient response and the current control adjustment. In this work, an intuitive decoupling strategy is presented to improve the dynamic behavior, based on Multiple-Input-Multiple-Output systems theory. The approach developed is particularly interesting in extremely weak grids, allowing an easier adjustment of the main controller.
Influenza C Virus in Cattle with Respiratory Disease, United States, 2016–2018

We identified influenza C virus (ICV) in samples from US cattle with bovine respiratory disease through real-time PCR testing and sequencing. Bovine ICV isolates had high nucleotide identities (≈98%) with each other and were closely related to human ICV strains (≈95%). Further research is needed to determine bovine ICV’s zoonotic potential.
QoE-optimized Cache System in 5G Environment for Computer Supported Cooperative Work in Design

Computer Supported Cooperation Work (CSCW) has been playing an increasingly important role in many areas of human social life. Cooperative design refers to the technique of product design based on CSCW and parallel engineering. With widespread use of CSCW, network bandwidth is becoming a bottleneck that affects user experience and service quality. Currently, global attention has been paid to the fifth-generation communication system (5G). In order to address the network bottleneck of the cooperative design system using the 5G network advantages, this paper focuses on optimizing QoE of the cooperative design system and proposes a distributed cache system for cooperative design in the 5G environment. The cache network is divided into different domains based on the characteristics of the 5G structure. Coupling between cache and cooperative design is implemented after taking the properties of the cooperative design system into account. Simulation results demonstrate the ability of the proposed system to considerably improve QoE of the cooperative design system and reduce bandwidth utilization.
A Passivity-Based Strategy for Coaching in Human-Robot Interaction

In order to make robot programming more easy and immediate, walk-through programming techniques can be exploited. However, a modification of a portion of the trajectory usually means to execute the path from the beginning. In this paper we propose a passivity-based framework to modify the trajectory online, manually driving the robot throughout the desired correction. The system follows the initial trajectory, encoded with Dynamical Movement Primitives, by setting high gains in the admittance control. When the human operator grabs the end-effector, the robot becomes compliant and the user can easily teach the desired correction, until he/she releases it at the end of the modification. Finally, the correction is optimally joined to the initial trajectory, restarting the path tracking. To avoid unsafe behaviors, the variation of the admittance parameters is performed exploiting energy tanks, in order to preserve the passivity of the interaction.
Molecular dynamics simulation of aluminum nitride deposition: temperature and N : Al ratio effects

Heteroepitaxial growth of aluminum nitride (AIN) has been explored by experiments, but the corresponding growth mechanism is still unrevealed. Here, we use molecular dynamics simulations to study effects of temperature and N : Al flux ratio on deposited AlN. When the temperature increases from 1000 K to 2000 K with an N : Al flux ratio of 2.0, the growth rate of the AlN film decreases. The crystallinity of the deposited AlN is distinctly improved as the temperature increases from 1000 K to 1800 K and it becomes saturated between 1800 K and 2000 K. The crystallinity of the deposited film at 1800 K increases with an increase in the N : Al flux ratio from 0.8 to 2.4, and this degraded a little at an N : Al flux ratio of 2.8. In addition, stoichiometry is closely related to crystallinity of deposited films. Film with good crystallinity is connected with a near 50% N fraction. Furthermore, the average mean biaxial stress and mean normal stress at 1800 K with N : Al flux ratios of 2.0, 2.4 and 2.8 are calculated, indicating that the deposited film with lowest stress has the best crystal quality and the defects appear where stresses occur.
Predicting Tongue Motion in Unlabeled Ultrasound Video Using 3D Convolutional Neural Networks

A 3-dimensional convolutional neural network is trained on unlabeled ultrasound video to predict an upcoming tongue image from previous ones. The network obtains results superior to those of simpler predictors and provides a starting point for exploiting the higher-level representation of the tongue learned by the system in a variety of applications in speech research. This work is believed to be the first application of convolutional neural networks to unlabeled ultrasound video for the purpose of predicting tongue movement.
Cardioprotective effects and underlying mechanism of Radix Salvia miltiorrhiza and Lignum Dalbergia odorifera in a pig chronic myocardial ischemia model

Traditional Chinese medicines, including Radix Salvia miltiorrhiza (SM) and Lignum Dalbergia odorifera (DO) extracts, have historically been used to treat myocardial ischemia and other cardiovascular diseases. The volatile oil of DO (DOO) is one of the main components of DO. The aim of the present study was to assess the cardioprotective effects and possible underlying mechanisms of SM-DOO in pigs with ameroid constriction-induced chronic myocardial ischemia. An ameroid constrictor was placed around the left anterior descending coronary artery of pigs to induce chronic myocardial ischemia. At weeks 2, 6 and 8, myocardial injury markers and blood gas levels were detected. At week 8, coronary angiography, echocardiography and hemodynamics analysis were performed to evaluate myocardial function. Following sacrifice, myocardial tissue was collected and subjected to morphological, histopathological and apoptosis assays. Western blotting was used to detect the protein expression of Bcl-2 associated X (Bax), Bcl-2, Akt, phosphorylated (p)-Akt, glycogen synthase kinase (GSK)-3β and p-GSK-3β. It was revealed that SM-DOO treatment following chronic myocardial ischemia significantly downregulated the expression of myocardial injury markers, ameliorated myocardial oxygen consumption, increased collateralization, reduced regional cardiac dysfunction and limited the extent of myocardial damage. Furthermore, the results of an apoptosis assay revealed that the apoptosis rate was decreased, the expression of Bax decreased and Bcl-2 increased, and the ratio of Bcl-2/Bax was increased. Further experiments indicated that treatment with SM-DOO increased the phosphorylation of Akt and GSK-3β. These findings suggest that SM-DOO treatment ameliorates myocardial injury in a chronic myocardial ischemia model, and that the underlying mechanisms responsible may be associated with the activation of the Akt/GSK-3β signal pathway. Thus, experimental evidence that SM-DOO may be an effective drug for the prevention and treatment of chronic myocardial ischemia in clinical applications has been provided.
Impact of Virtual Environments on Motivation and Engagement During Exergames

Video games and sport are an essential part in the life of millions of people. With recent advances of immersive virtual reality devices such as the HTC Vive, Oculus Rift, or PlayStation VR, the use of virtual environments (VE) for exergames is becoming more and more popular. An exergame combines a physical activity with video game elements by tracking body movements or reactions of user, attempting to engage users in a more enjoyable system. In this paper, we present the results of a subjective experiment carried out with the aim to compare different kinds of virtual environments with each other. A rowing ergometer, connected either to a virtual reality system using a head-mounted display (HMD) or to a CAVE environment, was used as an exergame device. While for rowing experts, fitness and performance improvements are of major interest, we wanted to focus on the motivation and engagement of non-professionals. By means of a series of questionnaires and a follow-up interview, the Quality of Experience of participants using the system was assessed. Measurements include concepts such as flow, presence, video quality and well-being. Results show significant advantages of the HMD as well as of the CAVE compared to a system without a VE for the overall quality, system feedback, and flow. While the CAVE and HMD system mainly differed in their autotelic experience, the HMD was favored by the majority of participants due to a superior feeling of presence.
Multistage Pooling for Blind Quality Prediction of Asymmetric Multiply-Distorted Stereoscopic Images

Quality prediction for asymmetric multiply-distorted stereoscopic images (MDSIs) confronts more challenges than previous stereoscopic image quality assessment (SIQA) issues, whereas the existing no-reference SIQA methods have been limited to understand the asymmetric distortions and multiple distortions simultaneously for general-purpose blind quality prediction. In this paper, we propose a multistage pooling (MUSP) model for quality prediction of asymmetric MDSIs. In the training stage, we establish multimodal sparse representation framework for phase and amplitude components, respectively. In the testing stage, we use an MUSP strategy to simulate the pooling procedure undergoing multimodal quality pooling, feature pooling, binocular pooling, and phase-amplitude quality pooling in order. Experimental results on our new established database (NBU-MDSID Phase-II) demonstrate the effectiveness of our blind metric.