Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Multichannel Speech Recognition with Generalized Cross Correlation Inputs and Multitask Learning

Acoustic signals from microphone arrays are used to improve performance in distant speech recognition due to the availability of spatial information. And multichannel automatic speech recognition (ASR) systems often separate speech enhancement module from acoustic modeling, which may be not optimal for improving recognition accuracy. In this work, we propose to improve multichannel speech recognition by supplying the generalized cross correlation (GCC) between microphones, which encodes spatial information, as input features to a long short-term memory (LSTM) acoustic model in parallel with the regular acoustic features. Moreover, multitask learning architecture is incorporated and shows its ability to improve the robustness of the model. We performed experiments on the AMI and ICSI meeting corpora, with results indicating that the proposed model outperforms the model trained directly on the concatenation of multiple microphone outputs and the model trained on a beamformed channel.
Adaptive Time-Slice Block-Matching Optical Flow Algorithm for Dynamic Vision Sensors

Dynamic Vision Sensors (DVS) output asynchronous log intensity change events. They have potential applications in high-speed robotics, autonomous cars and drones. The precise event timing, sparse output, and wide dynamic range of the events are well suited for optical ﬂow, but conventional optical ﬂow (OF) algorithms are not well matched to the event stream data. This paper proposes an event-driven OF algorithm called adaptive block-matching optical ﬂow (ABMOF). ABMOF uses time slices of accumulated DVS events. The time slices are adaptively rotated based on the input events and OF results. Compared with other methods such as gradient-based OF, ABMOF can efﬁciently be implemented in compact logic circuits. We developed both ABMOF and Lucas-Kanade (LK) algorithms using our adapted slices. Results shows that ABMOF accuracy is comparable with LK accuracy on natural scene data including sparse and dense texture, high dynamic range, and fast motion exceeding 30,000 pixels per second.
Understanding The Aesthetic Styles of Social Images

Aesthetic perception is nearly the most direct impact people could receive from images. Recent research on image understanding is mainly focused on image analysis, recognition and classification, regardless of the aesthetic meanings embedded in images. In this paper, we systematically study the problem of understanding the aesthetic styles of social images. First, we build a two-dimensional Image Aesthetic Space (IAS) to describe image aesthetic styles quantitatively and universally. Then, we propose a Bimodal Deep Autoen-coder with Cross Edges (BDA-CE) to deeply fuse the social image related features (i.e. images' visual features, tags' textual features). Connecting BDA-CE with a regression model, we are able to map the features to the IAS. The experimental results on the benchmark dataset we build with 120 thousand Flickr images show that our model outperforms (+5.5% in terms of MSE) alternative baselines. Furthermore, we conduct an interesting case study to demonstrate the advantages of our methods.
The Use of Multivariate Weak-Locator Polynomials to Decode Cyclic Codes up to Actual Minimum Distance

A class of cyclic codes has recently been decoded by using the weak-locator polynomials instead of the conventional error-locator polynomials. In this paper, a generalization of weak-locator polynomials to the multivariate cases, called the multivariate weak-locator polynomials, is defined, and a new matrix whose determinant can be expressed as a multivariate weak-locator polynomial is developed for cyclic codes. Moreover, a modified matrix along with Gaussian elimination found herein enables one to determine error positions precisely. The presented matrices result in a reduction of the number of syndromes when compared with the previously known matrices. The simulation shows that, for example, with the capability of correcting more errors, a considerably sophistical scheme of decoding the (97, 49, 15) binary cyclic code based on the proposed matrices is around 115 times faster than other existing decoders. Therefore, in general, it is developed to facilitate faster decoding of a large class of cyclic codes and is naturally suitable for the software implementation.
Capacity bounds of Low-Dense NOMA over Rayleigh fading channels without CSI

Upper and lower bounds on capacity of low-dense code-domain NOMA were investigated in the context of 5G New Radio (5G-NR), under the worst hypothesis of no channel knowledge at the receiver, i.e. operation without channel state information (CSI). The impact of peculiar features of 5G-NR on capacity bounds of optimum receivers was investigated. The upper bound, defined as the system capacity with perfect CSI, and the lower bound corresponding to a pilot-based communication model, were found. These bounds indicated that the achievable rates of low-dense code-domain NOMA without CSI are lower, as expected, than those with perfect CSI, although the above gap vanishes to negligible values, when some of the system parameters, in particular speed of variation of the channel against symbol duration, number of simultaneous users, and system load, i.e. number of users vs. number of resource elements, favorably combine.
DOA Estimation in Heteroscedastic Noise with sparse Bayesian Learning

We consider direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e., source powers), leading to stochastic maximum likelihood (ML) DOA estimation. The DOAs are estimated from multi-snapshot array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots.
Local Behavior-Based Navigation in Rough Off-Road Scenarios Based on Vehicle Kinematics

This paper describes a novel behavior-based local navigation approach for rough off-road scenarios. Trajectory candidates are generated based on vehicle kinematics and dynamics as well as the desired global trajectory. In contrast to on-road local navigation approaches, the work at hand proposes the use of a shiftable elevation grid map instead of occupancy maps since traversability in rough terrains does not only depend on location, but also on the robot's orientation. The traversability is evaluated by determining tire contact points with the terrain to take various different safety and efficiency aspects like underbody collisions and rollover risk into account. By exploiting the behavior-based control paradigm, the navigation approach can be easily extended and its robustness is shown in experimental evaluations using an Unimog U5023.
Effects of sodium roasting on the leaching rate of boron-bearing tailings and its mechanism analysis

The study reported was intended to improve the leaching rate of boron-bearing tailings, using a method of sodium roasting that uses boron-bearing tailings as the raw material and Na2CO3 as the sodium agent. The effects of the roasting temperature and Na2CO3 amount on the leaching rate of boron-bearing tailings are mainly evaluated. The morphology and composition of the samples after sodium roasting are analysed by scanning electron microscopy and X-ray diffraction. The results show that sodium roasting can significantly improve the leaching rate of boron-bearing tailings. Under the optimal conditions where roasting temperature is 950°C, Na2CO3 amount is five times the theoretical amount and roasting time is 2 h, the leaching rate of boron-bearing tailings is up to 86.78%. Based on the analysis of the characterization results and the mechanism analysis of the sodium roasting process, the main reason for the increase of leaching rate is the reaction between Na2O produced by the decomposition of Na2CO3 and the boron in boron-bearing tailings resulting in soluble sodium borate. The results provide a scientific basis for the efficient comprehensive use of boron-bearing tailings.
Combined Therapy with SS31 and Mitochondria Mitigates Myocardial Ischemia-Reperfusion Injury in Rats

Myocardial ischemia-reperfusion (IR) injury contributes to adverse cardiac outcomes after myocardial ischemia, cardiac surgery, or circulatory arrest. In this study, we evaluated the ability of combined SS31-mitochondria (Mito) therapy to protect heart cells from myocardial IR injury. Adult male SD rats (n = 8/each group) were randomized: group 1 (sham-operated control), group 2 (IR, 30-min ischemia/72 h reperfusion), group 3 (IR-SS31 (2 mg intra-peritoneal injection at 30 min/24 h/48 h after IR)), group 4 (IR-mitochondria (2 mg/derived from donor liver/intra-venous administration/30 min after IR procedure)), and group 5 (IR-SS31-mitochondria). In H9C2 cells, SS31 suppressed menadione-induced oxidative-stress markers (NOX-1, NOX-2, oxidized protein) while it increased SIRT1/SIRT3 expression and ATP levels. In adult male rats 72 h after IR, left ventricular ejection fraction (LVEF) was highest in sham-operated control animals and lowest in the IR group. LVEF was also higher in IR rats treated with SS31-Mito than untreated IR rats or those treated with Mito or SS31 alone. Areas of fibrosis/collagen-deposition showed the opposite pattern. Likewise, levels of oxidative-stress markers (NOX-1, NOX-2, oxidized protein), inflammatory markers (MMP-9, CD11, IL-1β, TNF-α), apoptotic markers (mitochondrial-Bax, cleaved-caspase-3, PARP), fibrosis markers (p-Smad3, TGF-β), DNA-damage (γ-H2AX), sarcomere-length, and pressure/volume overload markers (BNP, β-MHC) all showed a pattern opposite that of LVEF. Conversely, anti-apoptotic (BMP-2, Smad1/5) and energy integrity (PGC-1α/mitochondrial cytochrome-C) markers exhibited a pattern identical to that of LVEF. This study demonstrates that the combined SS31-Mito therapy is superior to either therapy alone for protecting myocardium from IR injury and indicates that the responsible mechanisms involved increased SIRT1/SIRT3 expression, which suppresses inflammation and oxidative stress and protects mitochondrial integrity.
Node-based optimization of LoRa transmissions with Multi-Armed Bandit algorithms

The use of Low Power Wide Area Networks (LPWANs) is growing due to their advantages in terms of low cost, energy efficiency and range. Although LPWANs attract the interest of industry and network operators, it faces certain constraints related to energy consumption, network coverage and quality of service. In this paper we demonstrate the possibility to optimize the performance of the LoRaWAN (Long Range Wide Area Network) technology, one of the most widely used LPWAN technology. We suggest that nodes use light-weight learning methods, namely, multi-armed bandit algorithms, to select the communication parameters (spreading factor and emission power). Extensive simulations show that such learning methods allow to manage the trade-off between energy consumption and packet loss much better than an Adaptive Data Rate (ADR) algorithm adapting spreading factors and transmission powers on the basis of Signal to Interference and Noise Ratio (SINR) values.
Unsupervised Cross-Corpus Speech Emotion Recognition Using Domain-Adaptive Subspace Learning

In this paper, we investigate an interesting problem, i.e., unsupervised cross-corpus speech emotion recognition (SER), in which the training and testing speech signals come from two different speech emotion corpora. Meanwhile, the training speech signals are labeled, while the label information of the testing speech signals is entirely unknown. Due to this setting, the training (source) and testing (target) speech signals may have different feature distributions and therefore lots of existing SER methods would not work. To deal with this problem, we propose a domain-adaptive subspace learning (DoSL) method for learning a projection matrix with which we can transform the source and target speech signals from the original feature space to the label space. The transformed source and target speech signals in the label space would have similar feature distributions. Consequently, the classifier learned on the labeled source speech signals can effectively predict the emotional states of the unlabeled target speech signals. To evaluate the performance of the proposed DoSL method, we carry out extensive cross-corpus SER experiments on three speech emotion corpora including EmoDB, eNTERFACE, and AFEW 4.0. Compared with recent state-of-the-art cross-corpus SER methods, the proposed DoSL can achieve more satisfactory overall results.
Sure-Based Dual Domain Image Denoising

Recently developed Dual Domain Image Denoising (DDID) algorithm is a simple version of block-matching 3D filtering (BM3D) by combining bilateral filter and frequency-based method. DDID and its invariants have achieved competitive results compared with state-of-the-art methods. However, this kind of methods share a common drawback: there are a few parameters of the algorithms that are data- and noise-dependent, and difficult to tune. In this paper, we propose to use Stein's unbiased risk estimate (SURE) to measure the mean square error (MSE) of the DDID algorithm for restoration of an image contaminated with additive white Gaussian noise. We derive an explicit expression for SURE value to optimize parameters without access to the noise-free signal. Experimental results demonstrate the effectiveness of the proposed parameter selection in term of both quantitative and qualitative metrics.
Humanoids at Work: The WALK-MAN Robot in a Postearthquake Scenario

Today, human intervention is the only effective course of action after a natural or artificial disaster. This is true both for relief operations, where search and rescue of survivors is the priority, and for subsequent activities, such as those devoted to building assessment. In these contexts, the use of robotic systems would be beneficial to drastically reduce operators? risk exposure. However, the readiness level of robots still prevents their effective exploitation in relief operations, which are highly critical and characterized by severe time constraints. On the contrary, current robotic technologies can be profitably applied in procedures like building assessment after an earthquake. To date, these operations are carried out by engineers and architects who inspect numerous buildings over a large territory, with a high cost in terms of time and resources, and with a high risk due to aftershocks. The main idea is to have the robot acting as an alter ego of the human operator, who, thanks to a virtual-reality device and a body-tracking system based on inertial sensors, teleoperates the robot.
Applying Big Data, Machine Learning, and SDN/NFV to 5G Traffic Clustering, Forecasting, and Management

Traffic clustering, forecasting, and management play a crucial role in improving network efficiency, network quality, load balancing (LB), and energy saving of mobile networks. Especially, in 5G networks, a dense heterogeneous architecture of various types of cells (macro cells and small cells) make traffic management become more complicated. Moreover, investigating and understanding traffic patterns of a huge number of cells are challenging issues, but valuable for network operators. On the other hand, big data, machine learning (ML), software-defined network (SDN), and network functions virtualization (NFV) have recently been proposed as emerging technologies and the necessary tools for empowering the SON of 5G to address the intensive computation and optimization issues. In this study, the authors applied those technologies to build a practical and powerful framework for clustering, forecasting, and managing traffic behaviors for a huge number of base stations with different statistical traffic characteristics of different types of cells (GSM, 3G, 4G). Besides, several applications based on traffic forecasting were also introduced. Finally, the performance of the proposed models was evaluated by applying them to a real dataset that collected traffic KPIs (key performance indicators) of more than 6000 cells of a real network during the years, 2016 and 2017.
Role of Prosodic Features on Children's Speech Recognition

In this paper, we have explored the role of combining prosodic variables with the existing acoustic features in the context of children's speech recognition using acoustic models trained on adults' speech. The explored acoustic features are Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction cepstral coefficients (PLPCC) while the considered prosodic variables are loudness, voice-intensity and voice-probability. An analysis presented in this paper shows that, given that the textual content remains the same, the considered prosodic variables exhibit very similar contours for adults' and children's speech. At the same time, the contours differ a lot when the context is different. Consequently, inclusion of prosodic information reduces the inter-speaker differences and increases the class discrimination. This subsequently improves the recognition performance. Further improvements are obtained by projecting the feature vectors obtained by combining the two features to a lower-dimensional subspace. The same has been experimentally verified in this study for mismatched speech recognition using deep neural network (DNN) based system. On combining MFCC (PLPCC) and prosodic features, a relative improvement of 16% (14%) is noted on decoding children's speech using adult data trained DNN models.
Autonomic dysfunction and white matter microstructural changes in drug-naïve patients with Parkinson’s disease

Background Autonomic dysfunction (AD) is one of the non-motor features of Parkinson’s disease (PD). Some symptoms tend to occur in the early stages of PD. AD also has a great impact on patient’s quality of life. In this study, we aimed to discover the association between AD (Scales for Outcomes in Parkinson’s disease-Autonomic, SCOPA-AUT) and microstructural changes in white matter tracts in drug-naïve early PD patients to elucidate the central effects of autonomic nervous system impairments. Method In total, this study included 85 subjects with PD recruited from the Parkinson’s Progression Markers Initiative (PPMI) database. Among the 85 PD patients, 38 were in Hoehn & Yahr stage 1 (HY1PD) and 47 were in stage 2 (HY2PD). Diffusion magnetic resonance imaging (DMRI) data were reconstructed in the MNI space using q-space diffeomorphic reconstruction to obtain the spin distribution function. The spin distribution function (SDF) values were used in DMRI connectometry analysis. We investigated through diffusion MRI connectometry the structural correlates of white matter tracts with SCOPA-AUT subscores and total score. Results Connectometry analysis also revealed positive association with white matter density in bilateral corticospinal tract in HY1PD patients and negative association in genu of corpus callosum (CC) and, bilateral cingulum in both groups. In addition, there were associations between gastrointestinal, sexual, thermoregulatory and urinary items and structural brain connectivity in PD. Conclusion Our study reveals positive correlation, suggesting neural compensations in early PD. Cingulum and CC tracts have well-known roles in PD pathology, compatible with our findings that bring new insights to specific areas of AD and its role in central nervous system (CNS) neurodegeneration, paving the way for using prodromal makers in the diagnosis and treatment of PD.
Level-Headed: Evaluating Gimbal-Stabilised Visual Teach and Repeat for Improved Localisation Performance

Operating in rough, unstructured terrain is an essential requirement for any truly field-deployable ground robot. Search-and-rescue, border patrol and agricultural work all require operation in environments with little established infrastructure for easy navigation. This presents challenges for sensor-based navigation such as vision, where erratic motion and feature-poor environments test feature tracking and hinder the performance of repeat matching of point features. For vision-based route-following methods such as Visual Teach and Repeat (VT&R), maintaining similar visual perspective of salient point features is critical for reliable odometry and accurate localisation over long periods. In this paper, we investigate a potential solution to these challenges by integrating a gimbaled camera with VT&R on a Grizzly Robotic Utility Vehicle (RUV) for testing at high speeds and in visually challenging environments. We examine the benefits and drawbacks of using an actively gimbaled camera to attenuate image motion and control viewpoint. We compare the use of a gimbaled camera to our traditional fixed stereo configuration and demonstrate cases of improved performance in Visual Odometry (VO), localisation and path following in several sets of outdoor experiments.
Comparative Study on Routing Protocols for Vehicular Ad-Hoc Networks (VANETs)

The advent of Vehicular Ad-hoc Network (VANET) has become the attraction to the researchers, scientists and engineers, in Intelligent Transportation systems (ITS). VANET is an important part of intelligent transportation system to improve safety and information transmission proficiency on the road. Emerge of the VANET has resulted in a systematic automotive technology with safety applications, flexibility, connectivity for both drivers and passengers that has enhanced efficient and safe transport systems. Early warning signals for drivers and inter-vehicle communication are some of the VANET's benefits. The routing protocol takes a critical role for the safety applications. The core interest of this paper is centered on different routing protocols associated with VANET such as Ad-hoc On Demand Distance Vector (AODV), Ad-hoc Distance Vector (ADV, Dynamic Source Routing (DSR), and Destination Sequenced Distance Vector Routing (DSDV). In addition, to investigating different available VANET routing protocol, the paper further evaluates the performance of these routing protocols and compares different routing based on performance yardsticks such as packet delivery ratio, average throughput, delay, as well as the overall energy consumption. Based on the comparative studies, the appropriate recommendation is made for the suitable routing protocol for highly dense traffic areas.
Automated Timing Characterization of High-Performance Macroblocks for Latency Insensitive FPGA Designs

Traditional FPGA design workflow requires the complete system in order to perform timing characterization. However, high-performance digital systems implementing scientific applications frequently occupy a large area and are suitably developed following a latency insensitive design approach to achieve multi-level parallelism. The produced circuits are composed of several deeply pipelined specialized computational macroblocks and try to maximize both the operating frequency and the usage of FPGA device resources to full capacity. These goals are most appropriately attainable through a decentralized control strategy and the optimization of performance for individual blocks independently from each other. This work proposes a synthesizable virtual wrapper architecture which does not add any functionality but simulates complete system conditions for the timing characterization of individual blocks using standard low-level synthesis tools. It further presents a high-level tool to generate both the computational blocks and the wrapper from a functional specification in C language, and shows that large blocks with inputs and outputs exceeding the available number of FPGA pins can be automatically generated and characterized. In particular, results for the blocks generated for a real computational fluid dynamics application are provided.