Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Whole Body Vibration Therapy on Spasticity and Disability of the Patients with Poststroke Hemiplegia

Objective To determine if whole body vibration therapy (WBV) effectively improves functional outcome in patients with poststroke hemiplegia. Materials and Methods In this single-blind RCT, WBV group (n = 10) had 40 hz frequency/4 mm amplitude vibration during 5 minutes/session, 3 days a week, for a duration of 4 weeks. The control group (n = 11) had no vibration therapy for the same duration while standing on the same platform. Patients in both of the groups did 15 minutes of stretching and active range of motion exercises before the intervention. Outcome measures were Modified Ashworth Scale (MAS), Functional Independence Measurement (FIM), and Timed 10-Meter Walk Test (10 mWT). Results Only 10 mWT improved at the 1st week (p = 0.002), 1st month (p < 0.001), and 3rd month (p < 0.001) in favor of the intervention group. There was positive correlation also between 10 mWT and ankle spasticity (p < 0.001, r = 0.931). Conclusion This study suggests that WBV therapy may be a complementary therapy in gait rehabilitation and functional outcome of the patients with calf muscle spasticity.
Squib: The Language Resource Switchboard

The CLARIN research infrastructure gives users access to an increasingly rich and diverse set of language-related resources and tools. Whereas there is ample support for searching resources using metadata-based search, or full-text search, or for aggregating resources into virtual collections, there is little support for users to help them process resources in one way or another. In spite of the large number of tools that process texts in many different languages, there is no single point of access where users can find tools to fit their needs and the resources they have. In this squib, we present the Language Resource Switchboard (LRS), which helps users to discover tools that can process their resources. For this, the LRS identifies all applicable tools for a given resource, lists the tasks the tools can achieve, and invokes the selected tool in such a way so that processing can start immediately with little or no prior tool parameterization.
Palovarotene reduces heterotopic ossification in juvenile FOP mice but exhibits pronounced skeletal toxicity

Fibrodysplasia ossificans progressiva (FOP) is a rare genetic disorder characterized by debilitating heterotopic ossification (HO). The retinoic acid receptor gamma agonist, palovarotene, and antibody-mediated activin A blockade have entered human clinical trials, but how these therapeutic modalities affect the behavior of pathogenic fibro/adipogenic progenitors (FAPs) is unclear. Using live-animal luminescence imaging, we show that transplanted pathogenic FAPs undergo rapid initial expansion, with peak number strongly correlating with HO severity. Palovarotene significantly reduced expansion of pathogenic FAPs, but was less effective than activin A inhibition, which restored wild-type population growth dynamics to FAPs. Palovarotene pretreatment did not reduce FAPs’ skeletogenic potential, indicating that efficacy requires chronic administration. Although palovarotene inhibited chondrogenic differentiation in vitro and reduced HO in juvenile FOP mice, daily dosing resulted in aggressive synovial joint overgrowth and long bone growth plate ablation. These results highlight the challenge of inhibiting pathological bone formation prior to skeletal maturation.
Watch, Listen Once, and Sync: Audio-Visual Synchronization With Multi-Modal Regression Cnn

Recovering audio-visual synchronization is an important task in the field of visual speech processing. In this paper, we present a multi-modal regression model that uses a convolutional neural network (CNN) for recovering audio-visual synchronization of single-person speech videos. The proposed model takes audio and visual features of multiple frames as the input and predicts a drifted frame number of the audiovisual pair which we input. We treat this synchronization task as a regression problem. Thus, the model does not need to search with a sliding window which would increase the computational cost. Experimental results show that the proposed method outperforms other baseline methods for recovered accuracy and computational cost.
On the Use of Grapheme Models for Searching in Large Spoken Archives

This paper explores the possibility to use grapheme-based word and sub-word models in the task of spoken term detection (STD). The usage of grapheme models eliminates the need for expert-prepared pronunciation lexicons (which are often far from complete) and/or trainable grapheme-to-phoneme (G2P) algorithms that are frequently rather inaccurate, especially for rare words (words coming from a different language). Moreover, the G2P conversion of the search terms that need to be performed on-line can substantially increase the response time of the STD system. Our results show that using various grapheme-based models, we can achieve STD performance (measured in terms of ATWV) comparable with phoneme-based models but without the additional burden of G2P conversion.
What Effects do Recommendations Have on Customers' Online Purchasing Willingness? An Empirical Study from WeChat Official Accounts

With the increasing market share of WeChat in mobile constant messaging application, there are large amounts of sellers establishing official accounts in WeChat. Many of these accounts would make recommendations to customers. But whether these recommendations work and what effects they will have remain to be studied. In this paper, we investigate the key factors influencing customers' online shopping intentions, in the context of recommendations from WeChat official accounts. Based on Expectation Confirmation Model, we add some special situation factors to extend our conceptual model. Results indicate the importance of users' trust towards official accounts, since trust influences users' perceived usefulness and attitude, which are positively related to purchasing willingness. It is suggested that sellers should gain more trust from consumers, and improving reputation of official accounts can be helpful. This analysis model can offer important implications for commercial activities in WeChat, and provide some useful advice for official accounts
An Overview of Negative Feedback Compensation Techniques for Operational Transconductance Amplifiers

Numerous compensation techniques are currently presented for ensuring the stability of CMOS operational transconductance amplifiers, from the Classical Miller compensation method of pole splitting using capacitors, to employing a nulling resistor, current buffer, up to nested Miller Compensation and its variants. Moreover, various other tactics of compensation like cascoding, pole zero cancellation and inserting the direct feedforward path into the circuit are suggested by many researchers. However, designers have no results on the frequency performance realizable that allow to deliberately choosing the best methodology to meet the prescribed specifications competently. In this paper, a comparison among various negative feedback compensation methods is carried out by analyzing compromise between gain, bandwidth, phase margin, slew rate and power utility for conventional CMOS technologies and two beyond CMOS technologies like FinFET and CNFET.
An Efficient Residual Echo Suppression for Multi-Channel Acoustic Echo Cancellation Based on the Frequency-Domain Adaptive Kalman Filter

Emerging use cases, such as keyword spotting while listening to FM radio, or participating in a teleconference utilizing the hands-free system in a vehicle, require the utilization of multi-channel acoustic echo cancellation (AEC). Addressing the typically remaining residual echo, it is common practice to apply a postfilter for residual echo suppression (RES) in a subsequent processing stage. In this paper we propose two RES approaches for the multi-channel frequency-domain adaptive Kalman filter, both being optimal under certain assumptions, extremely efficient and robust by exploiting a tight relation to the Kalman stepsize already available from the AEC.
Dielectric Properties of Alumina based Epoxy Composites for Electrical Insulation

Epoxy resin is widely used polymeric insulating material in power engineering applications including transformer and machine insulation, GIS spacers, bushing, cable terminations etc. Addition of micro and nano particles in base matrix is in common practice since last few decades aiming to improve electrical, thermal and mechanical properties of insulating material. In this paper dielectric properties of epoxy composites such as dielectric constant, dielectric loss and volume resistivity were investigated at different alumina filler loading. Experiments were performed to measure dielectric constant and dielectric loss as a function of frequency. Further DC volume resistivity was measured using high resistance meter and resistivity cell. Results indicate decrement in permittivity and dielectric loss at low filler loading of nano particles. Further, with increase in filler concentration permittivity tends to increases. In case of DC volume resistivity for all the epoxy composites resistivity is found lower than the neat epoxy resin.
A Novel 3D Non-Stationary Wireless MIMO Channel Simulator and Hardware Emulator

In this paper, a new WINNER+-based 3-D non-stationary geometry-based stochastic model (GBSM) for multiple-input multiple-output channels is proposed, as well as extended evolving algorithms of time-variant channel parameters. Meanwhile, important statistical properties of the channel model, i.e., time-variant autocorrelation function, time-variant cross-correlation function, and time-variant Doppler power spectrum density are derived and analyzed. Moreover, we propose an efficient hardware implementation method, namely, sum-of-frequency-modulation (SoFM) method, to generate non-stationary channel coefficients. By utilizing compact hardware architecture with SoFM modules, the proposed 3-D non-stationary GBSM is realized on a field-programmable gate array hardware platform. Simulations and hardware measurement results demonstrate that our proposed channel simulator and emulator can get more accurate and realistic Doppler frequency than those of the existing models. In addition, hardware measurements of statistical properties are also well consistent with the corresponding theoretical ones, which verify the correctness of both the hardware emulation scheme and theoretical derivations.
Perceived Stiffness Estimation for Robot Force Control

Typical robot force control architectures have a positive force feedback loop to decouple robot dynamics from contact dynamics. Due to the noisy profile of force measurements, it is common to filter force signals by low pass filters. This paper shows that, when force feedback is filtered, robot and environment dynamics are no longer decoupled, affecting force control performance. Additionally, the perceived stiffness from the force control perspective, is correlated with the robot effective mass. To cope with this issue, a force based stiffness estimation strategy that also includes the inertial properties (effective mass) in the estimation algorithm is proposed, allowing to adapt control gains based on the robot effective mass. In this way, the perceived stiffness can be seen as a control optimization parameter, rather than a well defined physical property. Simulation and experimental results with a 1-DoF robot and 7-DoF manipulator, respectively, validate the estimation strategy, showing better force control results with the perceived stiffness in the control loop, as compared to the real environment stiffness.
Use of Non-linear and Complexity Features for EEG Based Dementia & Alzheimer Disease Diagnosis

Alzheimer disease is one of the most common and fastest growing neurodegenerative diseases in the western countries. Development of different biomarkers tools are key issues for diagnosis of Alzheimer disease and its progression. Prediction of cognitive performance of subjects from EEG and identification of relevant biomarkers are some of the research problems. EEG signal analysis can be well suited for automated diagnosis of Alzheimer's disease. Although, EEG based techniques are helpful in screening of Alzheimer and dementia; still there is a scope of improvement in terms of diagnostic accuracy, sensitivity and specificity. Thus, many issues are still left out in field of Alzheimer diagnosis using EEG signals related to the choice of features which can help in distinguishing the two or more subjects. This paper focuses on new features for diagnosis of Alzheimer's disease using EEG signals with effective increase in diagnostic accuracy. The use of new complexity based features is proposed in this paper which increases the diagnostic accuracy and helps in early Alzheimer's diagnosis.
An Investigation of a Knowledge Distillation Method for CTC Acoustic Models

End-to-end acoustic models, such as connectionist temporal classification (CTC) and the attention model, have been studied, and their speech recognition accuracies come close to those of conventional deep neural network (DNN)-hidden Markov models. However, most high-performance end-to-end models are not suitable for real-time (streaming) speech recognition because they are based on bidirectional recurrent neural networks (RNNs). In this study, to improve the performance of unidirectional RNN-based CTC, which is suitable for real-time processing, we investigate the knowledge distillation (KD)-based model compression method for training a CTC acoustic model. we evaluate a frame-level KD method and a sequence-level KD method for CTC model. The speech recognition experiments on Wall Street Journal tasks demonstrate that, the frame-level KD worsens the WERs ofunidirectional CTC model, whereas sequence-level KD can improve the WERs of the model.
F0 Estimation for DNN-Based Ultrasound Silent Speech Interfaces

State-of-the-art silent speech interface systems apply vocoders to generate the speech signal directly from articulatory data. Most of these approaches concentrate on estimating just the spectral features of the vocoder, and use the original F0, a constant F0 or white noise as excitation. This solution is based on the assumption that the F0 curve is unpredictable from articulatory data that does not contain direct measurements of the vocal fold vibration. Here, we experimented with deep neural networks to perform articulatory-to-acoustic conversion from ultrasound images, with an emphasis on estimating the voicing feature and the F0 curve from the ultrasound input. Contrary to the common belief that F0 is unpredictable, we attained a correlation rate of 0.74 between the original and the predicted F0 curve. What is more, the listening tests revealed that our subjects could not distinguish the sentences synthesized using the DNN-estimated and the original F0 curve, and ranked them as having the same quality.
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation

Performance on benchmark datasets has drastically improved with advances in deep learning. Still, cross-dataset generalization performance remains relatively low due to the domain shift that can occur between two different datasets. This domain shift is especially exaggerated between synthetic and real datasets. Significant research has been done to reduce this gap, specifically via modeling variation in the spatial layout of a scene, such as occlusions, and scene environmental factors, such as time of day and weather effects. However, few works have addressed modeling the variation in the sensor domain as a means of reducing the synthetic to real domain gap. The camera or sensor used to capture a dataset introduces artifacts into the image data, which are unique to the sensor model, suggesting that sensor effects may also contribute to domain shift. To address this, we propose a learned augmentation network composed of physically-based augmentation functions. Our proposed augmentation pipeline transfers specific effects of the sensor model—chromatic aberration, blur, exposure, noise, and color temperature—from a real dataset to a synthetic dataset. We provide experiments which demonstrate that augmenting synthetic training datasets with the proposed learned augmentation framework reduces the domain gap between synthetic and real domains for object detection in urban driving scenes.
Implementation of Video Stabilization Algorithm for Surveillance System

Aerial video surveillance plays an important role in gathering the information for the public as well as military applications. Now-a-days cameras used for capturing video are of high quality, but because of the unintentional movement of cameras, the video gets unstabilized. The main aim of the video stabilization is to remove the unintentional motion and shakiness in the videos and preserve the desired motion. In this approach, speeded up robust features (SURF) and scale-invariant feature transform (SIFT) methods are used to detect and match the feature points of interest. The outliers and noise are removed using RANSAC, while affine transform is used to estimate the motion of the interest points. Finally, the video gets stabilized by compensating the global motion points obtained by the affine transform. These approaches for video stabilization show promising results in terms of Inter Transformation Fidelity (ITF) values for SURF and SIFT algorithms.
Introduction to the Special Issue on Language in Social Media: Exploiting Discourse and Other Contextual Information

Social media content is changing the way people interact with each other and share information, personal messages, and opinions about situations, objects, and past experiences. Most social media texts are short online conversational posts or comments that do not contain enough information for natural language processing (NLP) tools, as they are often accompanied by non-linguistic contextual information, including meta-data (e.g., the user’s profile, the social network of the user, and their interactions with other users). Exploiting such different types of context and their interactions makes the automatic processing of social media texts a challenging research task. Indeed, simply applying traditional text mining tools is clearly sub-optimal, as, typically, these tools take into account neither the interactive dimension nor the particular nature of this data, which shares properties with both spoken and written language. This special issue contributes to a deeper understanding of the role of these interactions to process social media data from a new perspective in discourse interpretation. This introduction first provides the necessary background to understand what context is from both the linguistic and computational linguistic perspectives, then presents the most recent context-based approaches to NLP for social media. We conclude with an overview of the papers accepted in this special issue, highlighting what we believe are the future directions in processing social media texts.
Facial Recognition with Encoded Local Projections

Encoded Local Projections (ELP) is a recently introduced dense sampling image descriptor which uses projections in small neighbourhoods to construct a histogram/descriptor for the entire image. ELP has shown to be as accurate as other state-of-the-art features in searching medical images while being time and resource efficient. This paper attempts for the first time to utilize ELP descriptor as primary features for facial recognition and compare the results with LBP histogram on the Labeled Faces in the Wild dataset. We have evaluated descriptors by comparing the chi-squared distance of each image descriptor versus all others as well as training Support Vector Machines (SVM) with each feature vector. In both cases, the results of ELP were better than LBP in the same sub-image configuration.
System Reliability Estimation with Failure Modes Analysis Applying Bayesian Approach - Case Study

This paper describes an approach for estimating system's reliability using the information and data gathered and analyzed by FMEA of the system and its components during design. This approach suggests estimating failure mode probability distribution, based on their Prior estimation, following the Bayesian methodology. In order to avoid a massive and time consuming procedure, only critical, dominant and uncertain failure modes should be considered. The prediction of failure mode probability is based on estimating the lower and upper probability limits at defined percentiles of failure mechanisms which cause this failure mode. In reality, often there is very limited understanding and ability to estimate those limits or “confidence interval” for the failure probability. In order to eliminate the effect of this limited ability, very strict rules and procedures were defined.
Recurrent Multi-frame Single Shot Detector for Video Object Detection

Deep convolutional neural networks (CNNs) have recently proven extremely capable of performing object detection in single-frame images. In this work, we extend a class of CNNs designed for static image object detection to multi-frame video object detection. Our Multi-frame Single Shot Detector (Mf-SSD) augments the Single Shot Detector (SSD) meta-architecture [16] to incorporate temporal information from video data. By adding a convolutional recurrent layer to an SSD architecture our model fuses features across multiple frames and takes advantage of the additional spatial and temporal information available in video data to improve the overall accuracy of the object detector. Our solution uses a fully convolutional network architecture to retain the impressive speed of single-frame SSDs. In particular, our Recurrent Mf-SSD (based on the SqueezeNet+ architecture) can perform object detection at 50 frames per second (FPS). This model improves upon a state-of-the-art SSD model by 2.7 percentage points in mean average precision (mAP) on the challenging KITTI dataset. Additional experiments demonstrate that our Mf-SSD can incorporate a wide range of underlying network architectures and that in each case, the multi-frame model consistently improves upon single-frame base-lines. We further validate the efﬁcacy of our RMf-SSD on the Caltech Pedestrians dataset and ﬁnd similar improvements of 5% on the pre-deﬁned Reasonable set.
Contrast Enhancement Using Phase Transition Information and Total Variation

Contrast enhancement is an important preprocessing step for the analysis of images. The main aim of contrast enhancement techniques is to increase the visibility of the objects by modifying the spatial characteristics of the image. In this paper, phase transition based contrast enhancement framework is proposed to overcome the limitations of existing image enhancement techniques. The proposed image enhancement framework transforms the changes in image phase into the variations of magnitude to enhance the structural details of the image and to improve visibility. In addition, the concept of Selective Variation (SV) technique is introduced and enhancement parameters are optimized using SV. The experimental studies that were carried out on TID2008 dataset, show that the proposed scheme obtains promising results on contrast enhancement.