Miracle-dz/UniFAR-FTUs huggingface dataset:
How Well Do Spaniards Sleep? Analysis of Sleep Disorders Based on Twitter Mining

Twitter is a social network that allows its users to exchange messages of 280 characters with the possibility of accompanying them with a photo, video and/or link. This social network has been used as a source of data for numerous research studies on the human being. This study aims to analyse and characterize the messages coming from Spanish-speaking users related to the most common sleep disorder in our society, insomnia. For this purpose, this study provides two machine learning classifiers that enable the classification of users with insomnia together with the self-reported cause. In this context, this paper proposes a novel feature extraction method that exploits the similarity measure that can be computed in word embeddings models. For training these classifiers, a dataset of tweets in Spanish containing the word insomnia has been manually annotated to draw conclusions about the geographical distribution, symptoms and the different topics that users with insomnia treat. In addition, a second dataset has been collected formed by two groups of users from Spain with insomnia and without insomnia. Analysing the timeline of both groups we have been able to extract the differences in the patterns of activity on Twitter of each of these groups.
Scalable Cloud Service For Multimedia Analysis Based on Deep Learning

Convolutional neural networks have proved their capability over wide areas of computer vision and has lead to superlative performance in the benchmarking tasks. Efforts to train and run deep neural networks is computationally expensive and requires a cloud infrastructure equiped with appropriate hardware. In this paper, we introduce the technical details and results from our cloud service for image analysis. It includes a ConvNet over region proposals, and is able to cater the requests from a wide range of object classification and detection tasks. Our approach also encompasses an auto-scaling system to handle intensive inquiries in a short amount of time and automatically sorts the queries based on their importance. The experimental results prove that our system could offer the state-of-the-art accuracy on users’ data.
A Review of Statistical and Neural Network Based Hybrid Machine Translators

Machine Translation has become one of the top research interests for a few years. Compared to others, statistical and neural network approaches toward machine translation are performing the best. However, both approaches have advantages over one another. In recent years, few researchers have conducted experiments to create hybrid machine translation systems that combine the strength of statistical and neural network approach to produce a better translation. In this paper, we review all of these research methodologies, datasets, evaluation scores and point out their shortcomings.
Structured Dictionary Learning for Compressive Speech Sensing

Sparse dictionary learning aims at training appropriate redundant dictionaries for specific tasks of signal processing, such as signal estimation, compression and classification. Most of the existing dictionary learning algorithms for compressive speech sensing only exploit speech samples to construct the dictionary. In this paper, we propose to leverage both the speech signal and its linear prediction coefficients jointly to learn a structured and sparse dictionary. The proposed dictionary is designed based on a new optimization strategy using both $l_{0}$ and $l_{2}$ norms to enforce sparsity and structure, respectively. The resulting optimization problem can be solved by a fast iterative algorithm in two stages. Experimental results indicate that our proposed algorithm converges faster than the reference methods while yielding a better objective evaluation performance in terms of segmental signal-to-noise ratio, perceptual evaluation of speech quality and short-time objective intelligibility of the reconstructed speech.
A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention Task

In this work, we tackle the problem of child engagement estimation while children freely interact with a robot in a friendly, room-like environment. We propose a deep learning-based multi-view solution that takes advantage of recent developments in human pose detection. We extract the child’s pose from different RGB-D cameras placed regularly in the room, fuse the results and feed them to a deep Neural Network (NN) trained for classifying engagement levels. The deep network contains a recurrent layer, in order to exploit the rich temporal information contained in the pose data. The resulting method outperforms a number of baseline classifiers and provides a promising tool for better automatic understanding of a child’s attitude, interest and attention while cooperating with a robot. The goal is to integrate this model in next-generation social robots as an attention monitoring tool during various Child Robot Interaction (CRI) tasks both for Typically Developed (TD) children and children affected by autism (ASD).
Minimal Influence of Extracellular DNA on Molecular Surveys of Marine Sedimentary Communities

Extracellular DNA has been reported to comprise a large fraction of total DNA in near-seafloor sediment. However, the potential effect of extracellular DNA, arising from dead or moribund cells, on sequencing surveys is a critical concern that has largely not been addressed for marine sedimentary habitats. To address this concern, we interrogated freshly collected Arctic and Pacific sediment for extracellular 16S rRNA genes using the photoactive DNA-binding dye Propidium Monoazide. Significant differences between relative abundances of total (intracellular + extracellular) Bacterial 16S rRNA genes and relative abundances of intracellular Bacterial 16S rRNA genes are only detected in three of twelve shallow [10 cm below seafloor (cmbsf)] samples. Relative abundances of total Bacterial 16S rRNA genes are statistically indistinguishable from relative abundances of intracellular Bacterial 16S rRNA genes in all interrogated samples from depths greater than 10 cmbsf. 16S rRNA gene sequencing shows that even where significantly higher abundances of extracellular genes are detected, they have little or no influence on prokaryote community composition. Taxon-level analyses suggest that extracellular DNA, arising from in situ death, may be sourced from different organisms in sediment of different ages. However, the overall effect of extracellular genes on sequencing surveys of marine sedimentary prokaryotes is minimal.
Bi-Histogram Equalization with Adaptive Multi-Plateau Limits for Enhancing Magnetic Resonance Images

In this study, a highly efficient algorithm for enhancing the visual quality of brain Magnetic Resonance images is proposed. This method sequentially processes the gray-level histogram of the brain MR image by defining critical plateau limits. Since the lower and upper halves of the histogram are separately processed, the proposed algorithm produces significant results that facilitates subsequent morphological analysis effectively. Adaptive multiple plateau limits are computed on the upper and lower histogram. Thus the proposed algorithm not only rectifies the visual imperfections in the image but also avoids any unwanted noise to be introduced into the input image. The experimental results show that the proposed method is superior to many existing histogram-based algorithms, in terms of various standard quantitative metrics.
Unsupervised Feature Selection With Ordinal Preserving Self-Representation

Unsupervised feature selection is designed to select an optimal feature subset without any label information from high-dimensional data, which is implemented by eliminating the irrelevant and redundant features and has been attracted widespread attention in recent years. Specifically, the obtained low-dimensional representation is interpretable that is useful to machine learning applications. In this paper, we propose a novel unsupervised feature selection algorithm, namely ordinal preserving self-representation (OPSR) for image classification and clustering. First, each feature in high-dimensional data is represented by the linear combination of other features. Then, the topology information is introduced into the objective function for utilizing the ordinal locality of high-dimensional data adequately. At last, an efficient iteratively update algorithm is designed to solve the proposed OPSR, and its convergence is proved in detail. Extensive experimental results on six benchmark databases demonstrate that the effectiveness of the OPSR and its superiority also is verified by comparing with some state-of-the-art feature selection algorithms.
Online Recovery of Time- varying Signals Defined over Dynamic Graphs

The goal of this work is to devise least mean square (LMS) strategies for online recovery of time-varying signals defined over dynamic graphs, which are observed over a (randomly) time-varying subset of vertices. We also derive a mean-square analysis illustrating the effect of graph variations and sampling on the reconstruction performance. Finally, an optimization strategy is developed in order to design the sampling probability at each node in the graph, with the aim of finding the best tradeoff between steady-state performance, graph sampling rate, and learning rate of the proposed method. Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed learning strategies.
News Category Network based Approach for News Source Recommendations

News sources overall distribute their news content utilizing category words in their URLs to keep up the Web directory structure on their sites. These news categories can be of two sorts either generic, for example, “Business, Sports, etc.” or time-specific “ICC Champions Trophy 2017” and “Delhi Earthquake” or both. These news categories can be utilized for different applications, for example, news source recommendations and time specific news category extraction. The current frameworks, for example, Dmoz directory and Yahoo registry are mostly human annotated and do not consider the time dynamism of categories of news sources. Incited by aforementioned issues, in this paper, we propose a system that recommends the news sources with the help of news category network for the given news category. To build news category network, we use a dynamic approach that iteratively modifies the category network on the basis of URL categories. We additionally propose an approach that rank the news sources for the given categories using three parameters such as Traffic Based Website Importance, Social media Analysis and Category Wise Article Freshness. Experiments are done on category URLs captured from GDELT project to evaluate our proposed system.
Effect of Bifidobacterium breve in Combination With Different Antibiotics on Clostridium difficile

While combinations of probiotics with antibiotics have exhibited beneficial and adverse effects in the treatment of Clostridium difficile infection (CDI), no substantive explanation has been provided for these effects. In this study, C. difficile ATCC 9689 (CD) was treated with Bifidobacterium breve (YH68) in combination with five different antibiotics to explore the effects of the different combinations on C. difficile. Cell-free culture supernatant (CFCS) of YH68 was combined with metronidazole (MTR), vancomycin (VAN), clindamycin (CLI), ceftazidime (CAZ) or ampicillin (AMP) to treat CD. The plate counting method was used to determine the growth and spore production of CD, and cell damage was assessed by the measurement of extracellular ATP levels with a luminescence-based kit. The production of toxin A/B was measured with an ELISA kit. The gene expression levels of tcdA and tcdB in CD were evaluated by real-time qPCR. The CFCS of YH68 (3 × 109 CFU/mL) at 0.25 times the minimal inhibitory concentration (MIC) (0.25YH68) in combination with the five antibiotics exerted stronger inhibitory effects on the growth and spore production of CD than the same antibiotics in the absence of 0.25YH68, except 0.25YH68&MTR&, 0.25YH68&MTR&CAZ, and 0.25YH68&VAN&CLI. However, treatment with 0.25YH68&VAN, 0.25YH68&, 0.25YH68&MTR&CAZ, 0.25YH68&VAN&CAZ, 0.25YH68&VAN&, and 0.25YH68&CAZ& resulted in increased cell damage. In addition, the different combinations, except 0.25YH68&CLI, 0.25YH68&MTR& and 0.25YH68&VAN&CLI, dramatically reduced the production of toxin A/B in comparison with the effects of the same antibiotics in the absence of 0.25YH68. The gene expression levels of tcdA and tcdB in CD were lowered upon treatment with 0.25YH68 in combination with MTR, CLI, CAZ, MTR&CAZ, MTR&, CLI&CAZ, and CLI&, whereas the levels were enhanced by 0.25YH68 in combination with VAN, AMP, MTR&CLI, VAN&CLI, VAN&, and CAZ&. In summary, YH68 in combination with specific antibiotics could enhance the inhibitory effects of antibiotics against CD. In addition, the antagonistic effects between some antibiotics could be weakened by YH68.
Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling

Knowledge distillation is an effective technique that transfers knowledge from a large teacher model to a shallow student. However, just like massive classification, large scale knowledge distillation also imposes heavy computational costs on training models of deep neural networks, as the softmax activations at the last layer involve computing probabilities over numerous classes. In this work, we apply the idea of importance sampling which is often used in Neural Machine Translation on large scale knowledge distillation. We present a method called dynamic importance sampling, where ranked classes are sampled from a dynamic distribution derived from the interaction between the teacher and student in full distillation. We highlight the utility of our proposal prior which helps the student capture the main information in the loss function. Our approach manages to reduce the computational cost at training time while maintaining the competitive performance on CIFAR-100 and Market-1501 person re-identification datasets.
Implementation of Bangla Speech Recognition in Voice Input Speech Output (VISO) Calculator

Automatic Speech-recognition (ASR) is an engaging way for human-computer interaction. It is a process of deriving linguistic information from acoustic signals while removing noise signal associated with it. The speaker-independent Voice Input Speech Output (VISO) calculator application is a promising implementation of speech-recognition technology, which is designed to recognize numbers and symbols to perform mathematical operations. But none of such applications provide support for Bangla language yet. Thus, in this paper, we have designed a VISO calculator application that can recognize both isolated and continuous speech in Bangla language and derive mathematical expression. The expression is then calculated and the auditory output is pronounced in Bangla. The task of speech-to-text (STT) conversion is performed by Hidden Markov models (HMM) in CMU Sphinx, while the result is converted to speech using Android Text-to-Speech (TTS) API. The application is able to recognize Bangla real numbers such as , (3) etc. and several mathematical symbols such as , (.) etc. This voice-based application is helpful for people having physical disabilities such as congenital disorder or visual impairment and people with less mathematical knowledge, who use Bangla as their native language.
Gradient-Based Iterative Learning Control for Decentralised Collaborative Tracking

Collaborative tracking control of multi-agent sys tems involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. However existing, predominately centralised, control structures are sensitive to communication delays and data drop-out leading to inaccurate tracking. It- erative learning control (ILC) has been applied to increase performance using past experience, but reliance on inverse dynamics has severely reduced robustness to model uncertainty. This paper proposes the first general decentralized iterative learning framework to address this problem, thereby enabling a wide range of existing ILC methodologies to be applied to this area. This framework is illustrated through the derivation of a decentralised gradient based ILC algorithm which ensures convergence to the required reference trajectory, while simultaneously optimising the control input energy. In addition, a novel balancing algorithm is also proposed to distribute the input energy of each agent and hence avoid sub agent overloading.
Hybrid Beamforming Based on Uniform Channel Decomposition for Millimeter Wave Massive MIMO Systems

Hybrid beamforming architecture is a feasible candidate for massive multiple-input multiple-output millimeter wave (mmWave) systems, which can decompose the signal processing into analog and digital domains. In this paper, we propose a hybrid beamforming structure with a two-stage design procedure for the mmWave systems to optimize the bit error ratio (BER) performance. Inspired by the geometric mean decomposition algorithm, we apply an iterative update algorithm to solve the constrained analog beamforming problem, yielding an effective channel with the maximized geometric mean of singular values. We then apply a uniform channel decomposition (UCD) scheme with a minimum mean squared error criterion to design the digital beamformers, optimizing the system capacity via the water-filling algorithm. We combine the hybrid UCD scheme with a vertical-Bell Labs layered space-time (VBLAST) detector to eliminate inter-subchannel interference. The simulation results show that the proposed hybrid UCD-VBLAST scheme significantly outperforms the traditional beamforming strategies on transmission BER and simultaneously achieves a reasonable spectral efficiency close to that of fully-digital scheme.
Compact Dual-Polarized Shared-Dipole Antennas for Base Station Applications

Crossed-dipole (CD) antennas have been widely employed for dual polarization in wireless communication systems in recent years. In this paper, a novel design concept of dual-polarized shared-dipole (DPSD) antenna is presented. Different from the traditional CD antennas, the arms of the DPSD antenna are shared for two orthogonal polarizations. This design technique leads to significant size reduction and high isolation compared to the traditional CD antennas. The operation principle of the proposed antenna is theoretically analyzed. To validate the presented design concept, two novel DPSD antennas are designed, fabricated, and measured. The first design is a four-port DPSD antenna, which is a straightforward demonstration of the operation principle of the DPSD antenna. The second design is a highly integrated DPSD antenna, which avoids the use of a feed network and provides a simple configuration to design the dual-polarized antenna. Both of the DPSD antennas are designed to operate at 1.7–2.7 GHz for base station applications. The simulated and measured results confirm that the two DPSD antennas have wide bandwidth with VSWR < 1.5 and isolation > 35 dB dB. In addition, stable gain and half-power beamwidth are obtained with the variance less than ±0.55 dB and ±3.5°, respectively.
Cross-Dataset Data Augmentation for Convolutional Neural Networks Training

Within modern Deep Learning setups, data augmentation is the weapon of choice when dealing with narrow datasets or with a poor range of different samples. However, the benefits of data augmentation are abysmal when applied to a dataset which is inherently unable to cover all the categories to be classified with a significant number of samples. To deal with such desperate scenarios, we propose a possible last resort: Cross-Dataset Data Augmentation. That is, the creation of new samples by morphing observations from a different source into credible specimens for the training dataset. Of course specific and strict conditions must be satisfied for this trick to work. In this paper we propose a general set of strategies and rules for Cross-Dataset Data Augmentation and we demonstrate its feasibility over a concrete case study. Even without defining any new formal approach, we think that the preliminary results of our paper are worth to produce a broader discussion on this topic.
Distance-Azimuth Joint Cramér-Rao Lower Bound for Spherical-wavefront-based Scatterer Localization

In our previous work a scatterer-localization algorithm based on maximum-likelihood estimation was derived using spherical wavefront assumption. Individual Cramér-Rao Lower Bounds (CRLBs) for distance and azimuth estimation were presented. As a continuation, we now derive a joint CRLB for both distance and azimuth. The novelty lying in the joint CRLB is that the underlying Fisher Information Matrix (FIM) is a non-diagonal partition matrix, yielding the fact that the CRLB obtained is tighter than the CRLBs derived individually. A closed-form representation of the approximate of the joint CRLB is presented which is expressed as a function of the FIM elements. Monte-Carlo simulations are performed to assess the validity and the accuracy of the derived bounds. Finally, the applicability of the derived CRLB is illustrated for vehicle or obstacle localization when a vehicle-mounted millimeter-wave environment-sensing system is considered.
Real-time Multi Source Speech Enhancement based on Sound Source Separation using Microphone Array

This paper proposes a Sound Source Localization method based on source separation algorithm technique, which can be applied to application of reverberant environment where multiple acoustic sources are present. In this paper, we proposed the study and implementation of sound source localization and sound separation algorithm which uses three sources with eight microphones system is presented. The proposed system uses the signals which are acquired by a set of microphone arrays at different positions are considered as an input source. The recorded signals are applied into the source localization to obtain the Direction Of Arrival (DOA) of each source pair. The phase angle between the sources are estimated based on TDOA algorithm and mixture of source signals are separated based on the radial bins which is given into the system and the particular direction of signal is separated from the mixed signals. The Separated output noise level is tested with the input speech signal and it is found that noise level is comparatively reduced with the applied input signal.
Learning Framework for Virtual Network Function Instance Migration

To implement service function chains (SFCs), virtual network function instances (VNFIs) are placed on virtual machines decoupled from underlying hardwares. For cloud service providers, satisfying the quality of service of clients and reducing the resource consumption to accommodate more clients are both important. However, in the network with dynamic load, fixed VNFI placement strategies can not reach these goals. VNFI migration, which provides cloud service providers with a flexible fine-tuning strategy, becomes a potential way to solve this problem. In this paper, we formulate the VNFI migration problem with the goal to minimize the total cost in an operational cycle. Then a novel Migration-Monitor-Learning Framework (MMLF) is proposed to solve the VNFI migration problem. The most distinctive work of MMLF is that it solves the action space problem when applying reinforcement learning for VNFI migration. Action space for selection of VNFI for migration is very large and thus results in high complexity and poor convergence performance. To overcome this problem, MMLF involves a dedicated neural network to produce continuous actions under time-varying network load. Moreover, two mapping methods are proposed to map continuous actions to discrete VNFIs selected for migration. We evaluate MMLF and other state-of- the-art approaches with real trace data. The experimental results show that MMLF outperforms other approaches under various scenarios.