Miracle-dz/UniFAR-FTUs huggingface dataset:
GL-PAM RGB-D Gesture Recognition

The existing approaches for RGB-D gesture recognition mainly developed their systems based on the global features extracted from full sequences, which makes them unreliable for capturing some important movements. In this paper, we propose to combine the global and local context information extracted from posture, appearance, and motion sequences. Our experimental results on a large scale RGB-D gesture dataset show that the proposed global and local contexts can complement well with each other for efficiently characterizing gestures, and thus achieve the 2nd place in the ChaLearn LAP Large-scale Isolated Gesture Recognition Challenge (Round 2).
Intent Discovery Through Unsupervised Semantic Text Clustering

Conversational systems need to understand spoken language to be able to converse with a human in a meaningful coherent manner. This understanding (Spoken Language understanding - SLU) of the human language is operationalized through identifying intents and entities. While classiﬁcation methods that rely on labeled data are often used for SLU, creating large supervised data sets is extremely tedious and time consuming. This paper presents a practical approach to automate the process of intent discovery on unlabeled data sets of human language text through clustering techniques. We explore a range of representations for the texts and various clustering methods to validate the clustering stability through quantitative metrics like Adjusted Random Index (ARI). A ﬁnal alignment of the clusters to the semantic intent is determined through consensus labelling. Our experiments on public datasets demonstrate the effectiveness of our approach generating homogeneous clusters with 89% cluster accuracy, leading to better semantic intent alignments. Furthermore, we illustrate that the clustering offer an alternate and effective way to mine sentence variants that can aid the bootstrapping of SLU models.
Rate Control Optimization of X265 Using Information from Quarter-Resolution Pre-Motion-Estimation

The rate control of x265 capitalizes on the low-resolution frame pre-motion-estimation to analyze the information prorogation in consecutive frames, with which to detect the scene cut, to decide the slice type, and to adjust the quantization ($Q$) offsets in coding-unit (CU)-level, respectively. In this paper, we increase the accuracy of the frame-level base $Q$ calculation and the slice type decision in x265. We implemented the proposed algorithms in x265 version 2.4. Experiments revealed that, 0.5904dB BDPNSR on average and up to 1.7205dB coding quality improvement were achieved.
Monitoring Infant's Emotional Cry in Domestic Environments Using the Capsule Network Architecture

Automated recognition of an infant’s cry from audio can be considered as a preliminary step for the applications like remote baby monitoring. In this paper, we implemented a recently introduced deep learning topology called capsule network (CapsNet) for the cry recognition problem. A capsule in the CapsNet, which is deﬁned as a new representation, is a group of neurons whose activity vector represents the probability that the entity exists. Active capsules at one level make predictions, via transformation matrices, for the parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We employed spectrogram representations from the short segments of an audio signal as an input of the CapsNet. For experimental evaluations, we apply the proposed method on INTERSPEECH 2018 computational paralinguistics challenge (ComParE), crying sub-challenge, which is a three-class classiﬁcation task us-ing an annotated database (CRIED). Provided audio samples contains recordings from 20 healthy infants and categorized into the three classes namely neutral, fussing and crying. We show that the multi-layer CapsNet is competitive with the baseline performance on the CRIED corpus and is considerably better than a conventional convolutional net.
The Hippo pathway effector TAZ induces TEAD-dependent liver inflammation and tumors

Transcriptional cofactors YAP and TAZ have distinct roles in promoting proinflammatory cytokine production and tumorigenesis. TAZ drives inflammation Key effectors of the Hippo pathway, YAP and TAZ, are overexpressed in various cancers. Loss of upstream kinases that inhibit the activity of these transcriptional coactivators promotes inflammation. In patient-derived xenografts and TCGA data sets, Hagenbeek et al. found that only TAZ expression correlated strongly with inflammatory cytokine transcript abundance. Expression of hyperactivated TAZ, but not YAP, in the livers of mice augmented transcription factor TEAD-mediated systemic inflammation and tissue infiltration by myeloid cells. RNA-seq analysis identified distinct gene signatures in tumor cells driven by activated YAP or TAZ, suggesting that these Hippo pathway effectors have nonredundant functions. The Hippo signaling pathway regulates organ size and plays critical roles in maintaining tissue growth, homeostasis, and regeneration. Dysregulated in a wide spectrum of cancers, in mammals, this pathway is regulated by two key effectors, YAP and TAZ, that may functionally overlap. We found that TAZ promoted liver inflammation and tumor development. The expression of TAZ, but not YAP, in human liver tumors positively correlated with the expression of proinflammatory cytokines. Hyperactivated TAZ induced substantial myeloid cell infiltration into the liver and the secretion of proinflammatory cytokines through a TEAD-dependent mechanism. Furthermore, tumors with hyperactivated YAP and TAZ had distinct transcriptional signatures, which included the increased expression of inflammatory cytokines in TAZ-driven tumors. Our study elucidated a previously uncharacterized link between TAZ activity and inflammatory responses that influence tumor development in the liver.
The Effectiveness of Group Hope Therapy in Labor Pain and Mental Health of Pregnant Women

In the present study, the effect of group hope therapy on labor pain and mental health of pregnant women was investigated, in consideration of the Iranian health policy’s emphasis on encouraging women to have a healthy delivery. The research plan was a semiexperimental pretest–posttest design with a control group. A total of 36 pregnant women were assigned to the treatment and control group using a random procedure. General Health Questionnaire and a Visual Analog Scale were used to collect data about dependent variables. For the analysis of hypotheses, independent samples t test and covariance analysis were used. The findings showed that hope therapy had a significant effect on labor pain (t = 5.17; P < 0.001) and mental health (F = 28.21; P < 0.001). In general, the findings of the present study confirm the application of group hope therapy in relieving labor pain and increasing the mental health of pregnant women. This therapeutic approach can simultaneously reduce the labor pain, generate positive emotions in women, and turn the experience of delivery into a pleasant one.
Color Image Demosaicking Using a 3-Stage Convolutional Neural Network Structure

Color demosaicking (CDM) is a critical first step for the acquisition of high-quality RGB images with single chip cameras. Conventional CDM approaches are mostly based on interpolation schemes and hand-crafted image priors, which result in unpleasant visual artifacts in some cases. Motivated by the special characteristics of inter-channel correlations (higher correlations for R/G and G/B channels than that for R/B), in this paper, a 3-stage convolutional neural network (CNN) structure for CDM is proposed. In the first stage, the G channel is reconstructed independently. Then, by using the reconstructed G channel as guidance, the R and B channels are recovered in the second stage. Finally, high-quality RGB color images are reconstructed in the third stage. The objective and visual quality evaluation results show that the proposed structure achieves noticeable quality improvements in comparison to the state-of-the-art approaches.
Integral Contour Angle: An Invariant Shape Descriptor for Classification and Retrieval of Leaf Images

Leaf identification is a significant and challenging task for computer vision community due to the large intra-class variations and small inter-class differences of leaf image patterns. In this paper, a novel shape descriptor, called Integral Contour Angle (ICA), is proposed for accurate classification and retrieval of leaf images. For a contour point, two bunches of vectors emanate from it and ends at its left neighbor contour points and its right neighbor ones respectively. Their average vectors form an angle (termed ICA) having inherent invariance to translation, rotation and scaling of leaf shape. Changing the size of the neighborhood of the contour point naturally yields a set of ICAs at different scales. Group them to form a multiscale descriptor (called mICA) for the contour point. Collect the mICAs of all the contour points to construct a mICA set. The dissimilarity between two leaf shapes is measured by calculating the enhanced Hausdorff distance between their mICA sets. The experiment results on two popular leaf image datasets indicate that the proposed method outperforms the state-of-the-art methods.
Threshold-Based Heuristics for Trust Inference in a Social Network

Trust among the users of a social network plays a pivotal role in item recommendation, particularly for the cold start users. Due to the sparse nature of these networks, trust information between any two users may not be always available. To infer the missing trust values, one well-known approach is path based trust estimation, which suggests a user to believe all of its neighbors in the network. In this context, we propose two threshold-based heuristics to overcome the limitation of computation for the path based trust inference. It uses the propagation phenomena of trust and decides a threshold value to select a subset of users for trust propagation. While the first heuristic creates the inferred network considering only the subset of users, the second one is able to preserve the density of the inferred network coming from all users selection. We implement the heuristics and analyze the inferred networks with two real-world datasets. We observe that the proposed threshold based heuristic can recover up to 70 % of the paths with much less time compared to its deterministic counterpart. We also show that the heuristic based inferred trust is capable of preserving the recommendation accuracy.
A 4D DCT-Based Lenslet Light Field Codec

Light fields aim to represent visual information in 3D space. They are 4D structures that contain the images of a given scene from a sampled 2D range of viewpoints. When acquired using a lenslet camera, in addition to the ordinary intra-view redundancy, these views have a great deal of inter-view redundancy. In this work we propose a light field codec that fully exploits the 4D redundancy of light fields by using a 4D transform and hexadeca-trees. It initially divides the light field into 4D blocks and computes a 4D Discrete Cosine Transform of each one. Then the transform coefficients of the 4D block are grouped using hexadeca-trees on a bitplane-by-bitplane basis, and the generated stream is encoded using an adaptive arithmetic coder. The proposed codec has been employed to encode the JPEG Pleno lenslet light fields. The rate-distortion results have been assessed using test conditions comparable to the ones presented at the ICIP 2017 Light Field Coding Grand Challenge. The proposed codec, despite being conceptually simple, achieves competitive rate-distortion performance.
Perceptual Evaluation of Light Field Image

Recently, light field image has attracted wide attention. However, much less work has been conducted on the perceptual evaluation of light field image. In this work, we create the first windowed 5 degree of freedom light field image database (Win5-LID) based on stereoscopic display, which provides windowed 5 DOF experience and all the depth cues of light field image. The database consists of light field images with representative compression and reconstruction artifacts. We assume that the light field quality is not only affected by sub-views quality but also depth cues. Picture quality and overall quality are then evaluated and the results validate our assumption. Finally, the performance of existing image quality metrics is analyzed on our database. The results indicate that the performance of the state-of-the-art image quality metrics remains to be improved.
Tongue Segmentation with Geometrically Constrained Snake Model

Articulatory visualization aims at providing precise visual information of the speech organs (tongue, lips, and velum) that accompany with speech signals. It is often critical in fundamental studies and certain applications. To construct an articulatory visualization system, the profile of the speech organs must be segmented from images acquired by various types of medical equipments. In this paper, a geometrically constrained snake model is proposed to segment tongue profiles from mid-sagittal MRI to deal with the situation in which the tongue contacts with the surrounding structures and the target object with inhomogeneity nature. The result indicates that the proposed method improves segmentation performance significantly compared with the traditional snake model.
Temporal Consistency for Still Image Based Defocus Blur Estimation Methods

Many Defocus blur estimation methods have been proposed in recent years but, when applied to video sequences in a frame-by-frame manner, they typically exhibit temporal inconsistencies or flickering. This paper presents a temporal coherence scheme that can be coupled to any existing defocus blur estimation for still images, aiming to produce spatiotemporally coherent defocus blur map videos. The proposed method is based on the design of a Kalman Filter which is applied on a patch level. Experimental results show that the proposed method can smooth out undesirable temporal fluctuations whilst still being able to preserve the abrupt local appearance changes due to motion, occlusions or dis-occlusions.
Speaker Diarization with Enhancing Speech for the First DIHARD Challenge

We design a novel speaker diarization system for the first DIHARD challenge by integrating several important modules of speech denoising, speech activity detection (SAD), i-vector design, and scoring strategy. One main contribution is the proposed long short-term memory (LSTM) based speech denoising model. By fully utilizing the diversified simulated training data and advanced network architecture using progressive multitask learning with dense structure, the denoising model demonstrates the strong generalization capability to realistic noisy environments. The enhanced speech can boost the performance for the subsequent SAD, segmentation and clustering. To the best of our knowledge, this is the first time we show significant improvements of deep learning based single-channel speech enhancement over state-of-the-art diarization systems in highly mismatch conditions. For the design of i-vector extraction, we adopt a residual convolutional neural network trained on large dataset including more than 30,000 people. Finally, by score fusion of different i-vectors based on all these techniques, our systems yield diarization error rates (DERs) of 24.56% and 36.05% on the evaluation sets of Track1 and Track2, which are both in the second place among 14 and 11 participating teams, respectively.
Analysis and Detection of Phonation Modes in Singing Voice using Excitation Source Features and Single Frequency Filtering Cepstral Coefficients (SFFCC)

In this study, classiﬁcation of the phonation modes in singing voice is carried out. Phonation modes in singing voice can be described using four categories: breathy, neutral, ﬂow and pressed phonations. Previous studies on the classiﬁcation of phonation modes use voice quality features derived from inverse ﬁltering which lack in accuracy. This is due to difﬁculty in deriving the excitation source features using inverse ﬁltering from singing voice. We propose to use the excitation source features that are derived directly from the signal. It is known that, the characteristics of the excitation source vary in different phonation types due to the vibration of the vocal folds together with the respiratory effort (lungs effort). In the present study, we are exploring excitation source features derived from the modiﬁed zero frequency ﬁltering (ZFF) method. Apart from excitation source features, we also explore cepstral coefﬁcients derived from single frequency ﬁltering (SFF) method for the analysis and classiﬁcation of phonation types in singing voice.
Neural Network Architecture That Combines Temporal and Summative Features for Infant Cry Classification in the Interspeech 2018 Computational Paralinguistics Challenge

This paper describes the application of a novel deep neural network architecture to the classification of infant vocalisations as part of the Interspeech 2018 Computational Paralinguistics Challenge. Previous approaches to infant cry classification have either applied a statistical classifier to summative features of the whole cry, or applied a syntactic pattern recognition technique to a temporal sequence of features. In this work we explore a deep neural network architecture that exploits both temporal and summative features to make a joint classification. The temporal input comprises centi-second frames of low-level signal features which are input to LSTM nodes, while the summative vector comprises a large set of statistical functionals of the same frames that are input to MLP nodes. The combined network is jointly optimized and evaluated using leave-one-speaker-out cross-validation on the challenge training set. Results are compared to independently-trained temporal and summative networks and to a baseline SVM classifier. The combined model outperforms the other models and the challenge baseline on the training set. While problems remain in finding the best configuration and training protocol for such networks, the approach seems promising for future signal classification tasks.
Breathy to Tense Voice Discrimination using Zero-Time Windowing Cepstral Coefficients (ZTWCCs)

In this paper, we consider breathy to tense voices, which are often considered to be opposite ends of a voice quality continuum. Along with these, other aspects of a speaker’s voice play an important role to convey the information to the listener such as mood, attitude and emotional state. The glottal pulse characteristics in different phonation types vary due to the tension of laryngeal muscles together with the respiratory effort. In the present study, we are deriving the features that can capture effects of excitation on the vocal tract system through a signal processing method, called as zero-time windowing (ZTW) method. The ZTW method gives the instantaneous spectrum which captures the changes in the speech production mechanism, providing higher spectral resolution. The cepstral coefﬁ-cients derived from ZTW method are used for the classiﬁcation of phonation types. Along with zero-time windowing cepstral coefﬁcients (ZTWCCs), we use the excitation source features derived from zero frequency ﬁltering (ZFF) method. The excitation features used are: strength of excitation, energy of excitation, loudness measure and ZFF signal energy. Classiﬁcation experiments using ZTWCC and excitation features reveal a signiﬁcant improvement in the detection of phonation type compared to the existing voice quality features and MFCC features.
Dysarthric Speech Recognition Using Time-delay Neural Network Based Denoising Autoencoder

Dysarthria is a manisfestation of the disruption in the neuromuscular physiology resulting in uneven, slow, slurred, harsh or quiet speech. Dysarthric speech poses serious challenges to automatic speech recognition, considering this speech is difficult to decipher for both humans and machines. The objective of this work is to enhance dysarthric speech features to match that of healthy control speech. We use a Time-Delay Neural Network based Denoising Autoencoder (TDNN-DAE) to enhance the dysarthric speech features. The dysarthric speech thus enhanced is recognized using a DNN-HMM based Automatic Speech Recognition (ASR) engine. This methodology was evaluated for speaker-independent (SI) and speaker-adapted (SA) systems. Absolute improvements of 13% and 3% was observed in the ASR performance for SI and SA systems respectively as compared with unenhanced dysarthric speech recognition.
Empowering Rural Women for Gender Equality in Ashanti Region of Ghana Through Programmes and Projects

Women empowerment has been an issue for discussion all over the world. One of the major means of empowering women throughout the world is by the use of programmes and projects. This study is designed to ascertain whether or not the available women empowerment programme in Ghana really empower women to challenge the gender inequality status quo and, to explore an alternative direction to improve on women empowerment programme. Thus, the study attempts to assess the efforts made by two of such programmes – Livelihood Empowerment against Poverty (LEAP), a Government intervention and, Care International project for women empowerment, an non-governmental intervention. Theories such as Marx’s class theory, functionalism theory as well as conflict theory were used to explain why gender inequality exist and the need to consider the basic causes of gender inequality in designing any women empowerment programme. The central theory used in explaining how these available interventions approach women empowerment is deficit thinking theory. The study used qualitative and descriptive statistics method. As the study explored an alternative direction for women empowerment programmes, it used exploratory research design. Interview and focus group discussions were used to elicit information from the women. The analysis were made based on the data collected from the selected programme and project such as CARE international and Livelihood Empowerment Against Poverty (LEAP). The main findings was that the available projects and programmes only provide mitigation measures to support women and do not address the root causes of what disempower women and therefore their effort do not challenge the status quo. What the study found to be the better practice for women empowerment programmes to improve the women status is to target a particular gender inequality and design an appropriate women empowerment programme that tackles the root cause of it.
Image Classification Using Convolutional Neural Networks and Kernel Extreme Learning Machines

We know that convolutional neural networks are good at learning invariant features, but not always optimal for classification. Contrarily, Kernel Extreme Learning Machines (KELMs) are good at approximating any target continuous function with extremely fast speed, but cannot learn complicated invariances. In this paper, we propose a novel image classification framework, in which KELM instead of Softmax function is adopted as a classifier in the convolutional neural network (CNN) architecture for promoting the performance of image classification. Experiments conducted on the publicly available datasets demonstrate the superior performance of the proposed method.