Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody

We describe a new application of deep-learning-based speech synthesis, namely multilingual speech synthesis for generating controllable foreign accent. Specifically, we train a DBLSTM-based acoustic model on non-accented multilingual speech recordings from a speaker native in several languages. By copying durations and pitch contours from a pre-recorded utterance of the desired prompt, natural prosody is achieved. We call this paradigm “cyborg speech” as it combines human and machine speech parameters. Segmentally accented speech is produced by interpolating specific quinphone linguistic features towards phones from the other language that represent non-native mispronunciations. Experiments on synthetic American-English-accented Japanese speech show that subjective synthesis quality matches monolingual synthesis, that natural pitch is maintained, and that naturalistic phone substitutions generate output that is perceived as having an American foreign accent, even though only non-accented training data was used.
How do we know what we don’t know? Exploring Deaf people’s experiences of supporting their Deaf family member living with dementia

Deaf sign language users living with dementia and their carers, some of whom are Deaf, routinely face everyday barriers in accessing information, support (both formal and informal) and services. The familial care situation is further complicated given that most Deaf people will choose a life partner who is Deaf and most Deaf couples will have hearing children. This study focussed specifically on the everyday experiences of Deaf carers and the impact of caring for a loved one with dementia. Drawing on data from a wider consultation about dementia care, three Deaf carers were directly interviewed in British Sign Language by a Deaf researcher about their everyday experiences of care, support, and services. Thematic analysis focussed on: access is more than the provision of interpreters; effective care for the carers; and unknowing risk taking. Findings demonstrate the multifaceted effects of barriers to knowledge and information when the care partner is also Deaf, the urgent need for effective support for Deaf carers and unrecognised safeguarding concerns that are a result of lack of access to forms of basic knowledge about living with someone with dementia and potential coping strategies. Nonetheless, the participants demonstrated novel solutions and resilience in the face of these multiple challenges. Implications are drawn for future targeted services to supported Deaf carers of people affected by dementia.
Low-Complexity Weighted Mrt Multicast Beamforming in Massive Mimo Cellular Networks

We consider downlink multicast beamforming in a massive MIMO multi-cell network. Aiming at maximizing the minimum SINR among users, for both non-cooperative and cooperative multicasting, we propose a multicast beamforming scheme based on weighted maximum ratio transmission (MRT), and transform the beamforming optimization problem into a weight optimization problem that is solved via the semi-definite relaxation (SDR) approach. The proposed method has a low computational complexity which does not grow with the number of antennas, and thus is suitable for massive MIMO systems. Simulation shows that our proposed multicast beamforming solution yields comparable or better performance than existing approaches but with significantly lower complexity for practical systems with a large but finite number of antennas.
A Comparative Quality Assessment Study for Gaming and Non-Gaming Videos

Recent years have seen a tremendous increase in video traffic with the rise of Over The Top (OTT) services. Along with traditional Video on demand (VoD) streaming services (e.g., Netflix, YouTube), live video services (e.g., Twitch. tv, YouTubeGaming, Facebook Live) have also resulted in a tremendous share of Internet traffic. Among the live streaming services, gaming video streaming has a major share, with Twitch.tv alone currently responsible for the fourth highest peak Internet traffic in the US. As a consequence of this, and due to the fact that gaming videos are artificial and synthetic, it is worth investigating the specificity of gaming videos in relation to compression and the consequent end user QoE. In this paper, we present an objective and subjective quality comparison study for regular videos and gaming videos, with 30 video sequences (15 per type), encoded using the state of the art encoder HEVC. We discuss the similarity and dissimilarity between the two video types and also discuss how these observations can be used to improve the end user QoE.
Light-Field Intrinsic Dataset

Light-ﬁeld imaging has various advantages over the traditional 2D photography, such as depth estimation and occlusion detection, which can aid intrinsic decomposition. The extracted intrinsic layers enable multiple applications, such as light-ﬁeld appearance editing. However, the current light-ﬁeld intrinsic decomposition techniques primarily resort to qualitative comparisons, due to lack of ground-truth data. In this work, we address this problem by providing intrinsic dataset for real world and synthetic 4D and 3D (only horizontal parallax) light ﬁelds. The ground-truth intrinsic data comprises albedo, shading and specularity layers for all sub-aperture images. In case of synthetic data, we also provide ground-truth depth, normals, and further decomposition of shading into direct and indirect components. For real-world data acquisition, we make use of custom hardware
Cell Detection by Functional Inverse Diffusion and Nonnegative Group Sparsity—Part I: Modeling and Inverse Problems

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs’ Green function to derive a novel parameterization of the acquired images. This parameterization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a nonnegative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology.
Model Predictive Control of a Multi-Rotor with a Suspended Load for Avoiding Obstacles

This paper investigates a multi-rotor with a suspended load in perspectives of 1) real-time path planning, 2) obstacle avoidance, and 3) transportation of a suspended object. A suspended load cannot be controlled with conventional controllers designed for nominal multi-rotors due to the dynamic coupling between the multi-rotor and load. Although several control and planning algorithms have been proposed based on elaborately derived dynamic equations, most existing studies separate control and path planning problems by following predefined trajectories after trajectory generation. Moreover, many state-of-the-art trajectory generation algorithms cannot work real-time for a system with high degrees of freedom, which makes it not suitable to operate the system in dynamic environments where obstacles appear abruptly or move unexpectedly. With this in mind, we apply Model Predictive Control (MPC) with Sequential Linear Quadratic (SLQ) solver to compute feasible and optimal trajectory real-time and to operate a multi-rotor with a suspended load in dynamic environments. We design an obstacle-avoidance algorithm suitable for the current platform flying in cluttered environments. Flight experiments shows that the proposed algorithm successfully controls the multi-rotor and allows to avoid obstacles simultaneously.
The Automatic Recognition of Sepedi Speech Emotions Based on Machine Learning Algorithms

Over the past years, speech emotion recognition (SER) studies have been gaining much interest in the fields of affective computing and human-computer interaction (HCI). The idea was to improve the interaction between human beings and machines. In this paper, an SER system that classifies and recognise six basic emotions (anger, sadness, disgust, fear, happiness, and neutral) from speech spoken in Sepedi language (one of South Africa's official languages) is discussed. Speech recordings were collected from the Sepedi language speakers and TV drama broadcast to create emotional speech corpora. 34 speech features were then extracted from the speech corpora, using the pyAudioAnalysis tool, to train and compare different algorithms using 10 folds cross-validation. The experiments were conducted using WEKA data-mining software. The results showed that Auto-WEKA outperforms all the standard algorithms (SVM. KNN and MLP). Recorded speech corpus yielded good recognition accuracy compare to TV broadcast speech corpus.
Combining Multiple Deep Features for Glaucoma Classification

Glaucoma is one of the leading cause of blindness. Although there is still no cure, early detection can prevent serious vision loss. Therefore automated glaucoma detection/classification is an important issue. In the past decade, segmentation based approach such as those based on cup-to-disc-ratio are popular, but single indicator limit its performance. Recently, convolutional neural network based image classification approaches that can use more image cues achieve good performance. In this paper, we propose a new glaucoma classification by combining multiple features extracted by different convolutional neural networks. Its effectiveness is clearly demonstrated on the publicly available Origa [1] dataset. It achieves an area under the receiver operating characteristic curve of 0.8483, which better than the 0.838 given by on manual marked cup-to-disc-ratio. To our knowledge, it is the first approach surpass human in glaucoma classification.
Adaptive Attitude Control for a Tail-Sitter UAV with Single Thrust-Vectored Propeller

Tail-sitter unmanned aerial vehicles (UAVs) have gained extensive popularity in recent years due to their inherent advantages of both fixed wing and rotary wing UAVs. However, these advantages are accompanied with control challenges because of two different flight regimes and drastically changing dynamics during transition flights. This paper focuses on the design of a unified controller free from cumbersome controller switchings and applicable in all attitude range for a tail-sitter with single thrust-vectored propeller. To achieve this, both thrust vectoring model and full-regime aerodynamics model are built first, after which a complete attitude dynamics model of the tail-sitter is established utilizing the quaternion attitude description to avoid the singularity problem. An adaptive controller is then derived based on a simplified model using the Lyapunov stability theory with unknown system parameters identified online by forgetting factor recursive least square (FF-RLS) method. Flight experiments are conducted to demonstrate the feasibility and effectiveness of the proposed control scheme.
Scheduling of Multistatic Sonobuoy Fields Using Multi-Objective Optimization

Sonobuoy fields, comprising a network of transmitters and receivers, are commonly deployed to find and track underwater targets. For a given environment and sonobuoy field layout, the performance of such a field depends on the scheduling, that is, deciding which source should transmit, and which from a library of available waveforms should be transmitted at any given time. In this paper, we propose a novel scheduling framework based on multi-objective optimization. Specifically, we pose the two tasks of the sonobuoy field-tracking and searching-as separate, competing, objective functions. Using this framework, we propose a characterization of scheduling based on Pareto optimality. This characterization describes the trade-off between the search-track objectives and is demonstrated on realistic multistatic sonobuoy simulations.
Design and Analysis of Frequency Selective Surface Integrated Circular Disc Antenna

In this paper, a novel stop-band single layer frequency selective surface (FSS) integrated with a low profile $/10 circular disc antenna has been proposed. The peak gain of the circular disc microstrip patch antenna undergoes an improvement of 7 dB in the designed frequency range. The unit cell (6.5mm x 6.0mm) of the proposed FSS comprises four triangular patches enclosed in a square loop. The FSS is designed to give a wide stop band response from 7.5–11.5 GHz. The antenna is designed to give a peak gain of 7dB in the frequency range 6.1–8.7 GHz. The design concept has been demonstrated and experimentally verified using simulated results.
A Nonsense Consonant-Vowel-Consonant Word Test to Assess Auditory Processing

Abstract Background: Dichotic listening (DL), or how the two ears work together as a team, is often used in the assessment of auditory processing disorders in both children and adults. Currently, the battery of dichotic tests includes stimuli containing words, digits, and nonsense consonant–vowel syllables. Single-syllable nonsense words are of particular use in assessing processing abilities because they can evaluate auditory processing without a listener’s dependence on linguistic knowledge. Therefore, nonsense words may assess auditory processes independently of previous vocabulary knowledge. Purpose: This study is designed to assess the clinical applicability and face validity of a nonsense word DL test in a young adult population. Research Design: This study included an experimental design to investigate the performance of young adult listeners on a Dichotic Nonsense Word (DNW) test spoken by a male and female speaker. The results were compared with one study that investigated young adult listener’s performance on dichotic tests of English words. Study Sample: A total of 100 young adult participants were recruited from the School of Health and Rehabilitation Sciences at the University of Pittsburgh to participate in the study. The participants ranged in age from 20 to 30, with an average age of 23, and all participants had normal hearing. Data Collection and Analysis: DL performance was measured in all participants using the Dichotic Words Test (DWT) and the newly developed DNW test. Kolmogorov–Smirnoff tests of normality were used to assess distribution of right- and left-ear scores. Criterion cutoff scores were determined for the percent correct scores in the nondominant ear and dominant ear and for ear advantage. Results: Scores were significantly different between the two tests in the right ear, Z = −8.258, p < 0.001, and in the left ear, Z = −8.471, p < 0.001. Scores within each test were higher for the right ear than for the left ear, and scores for both ears were significantly lower on the DNW test than for the DWT. Ear advantage scores from the DNW test were significantly larger than those obtained from the DWT. The low and high 95% criterion cutoff ranges for the DNW test were considerably wider than the 95% criterion cutoff ranges for the DWT. Conclusions: Results indicate that the new DNW test may be a useful clinical tool within a test battery for evaluating auditory processing skills independent of vocabulary knowledge.
An Integrated Bias Voltage Control Method for SPAD Arrays

An integrated bias voltage control method is proposed for alleviating the impact of breakdown voltage non-uniformity in single-photon avalanche diode (SPAD) arrays. A shared resistor string digital-to-analog converter is utilized to provide an optional potential for the anode of SPAD. Adjusting the anode voltage of SPAD according to its breakdown voltage can guarantee each detector in the array with similar excess bias voltage. The circuit was fabricated in TSMC 0.18- $\mu \text{m}$ CMOS technology. The measurements show that the bias voltage of each pixel is adjustable from 0 to 1.2 V with the step length of 80 mV and the maximum error is 10 mV. With the bias voltage control method, the performance difference of the detectors in the array is substantially reduced.
Common and Individual Feature Extraction Using Tensor Decompositions: a Remedy for the Curse of Dimensionality?

A novel method for common and individual feature analysis from exceedingly large-scale data is proposed, in order to ensure the tractability of both the computation and storage and thus mitigate the curse of dimensionality, a major bottleneck in modern data science. This is achieved by making use of the inherent redundancy in so-called multi-block data structures, which represent multiple observations of the same phenomenon taken at different times, angles or recording conditions. Upon providing an intrinsic link between the properties of the outer vector product and extracted features in tensor decompositions (TDs), the proposed common and individual information extraction from multi-block data is performed through constraints which impose physical meaning on otherwise unconstrained factorisation approaches. This is shown to dramatically reduce the dimensionality of search spaces in subsequent classification procedures and to yield greatly enhanced accuracy. Simulations on a multi-class classification task of large-scale extraction of individual features from a collection of partially related real-world images demonstrate the advantages of the “blessing of dimensionality” associated with TDs.
Three-Dimensional Attention-Based Deep Ranking Model for Video Highlight Detection

The video highlight detection task is to localize key elements (moments of user's major or special interest) in a video. Most of existing highlight detection approaches extract features from the video segment as a whole without considering the difference of local features both temporally and spatially. Due to the complexity of video content, this kind of mixed features will impact the final highlight prediction. In temporal extent, not all frames are worth watching because some of them only contain the background of the environment without human or other moving objects. In spatial extent, it is similar that not all regions in each frame are highlights especially when there are lots of clutters in the background. To solve the above problem, we propose a novel three-dimensional (3-D) (spatial+temporal) attention model that can automatically localize the key elements in a video without any extra supervised annotations. Specifically, the proposed attention model produces attention weights of local regions along both the spatial and temporal dimensions of the video segment. The regions of key elements in the video will be strengthened with large weights. Thus, the more effective feature of the video segment is obtained to predict the highlight score. The proposed 3-D attention scheme can be easily integrated into a conventional end-to-end deep ranking model that aims to learn a deep neural network to compute the highlight score of each video segment. Extensive experimental results on the YouTube and SumMe datasets demonstrate that the proposed approach achieves significant improvement over state-of-the-art methods. With the proposed 3-D attention model, video highlights can be accurately retrieved in spatial and temporal dimensions without human supervision in several domains, such as gymnastics, parkour, skating, skiing, surfing, and dog activities, on the public datasets.
Telecomm Fraud Detection via Attributed Bipartite Network

Internet malicious incident occur frequently in recent years, bringing huge loss in different fields such as telecommunication, finance, etc. Such incidents generally entail fraudulent behaviors, which deviate from normal behavioral patterns. Particularly, in telecommunication, those anomalous interactions can be manifested by the network formed through interactions between callers and callees. Telecommunication network is indeed a bipartite, directed, and attributed network, and the anomalous network structure can be better captured by simultaneously considering these distinctive perspectives. To that end, in this paper, we propose a novel metric to measure the abnormality of dense subgraph structure by considering the structure, temporal changes, as well the attributes on nodes and edges. We further formulate an objective function and propose a greedy approach to discover the structure and corresponding fraudsters. The proposed metric and algorithm are experimented on a real-world telecommunication network dataset, which is shown to achieve competitive fraud detection performance than the baseline methods.
Deploying South African social honeypots on Twitter

Inspired by the simple, yet effective, method of tweeting gibberish to attract automated social agents (bots), we attempt to create localised honeypots in the South African political context. We produce a series of defined techniques and combine them to generate interactions from users on Twitter. The paper offers two key contributions. Conceptually, an argument is made that honeypots should not be confused for bot detection methods, but are rather methods to capture low-quality users. Secondly, we successfully generate a list of 288 local low quality users active in the political context.
Non-Parallel Voice Conversion Using Variational Autoencoders Conditioned by Phonetic Posteriorgrams and D-Vectors

This paper proposes novel frameworks for non-parallel voice conversion (VC) using variational autoencoders (VAEs). Although conventional VAE-based VC models can be trained using non-parallel speech corpora with given speaker representations, phonetic contents of the converted speech tend to vanish because of an over-regularization issue often observed in latent variables of the VAEs. To overcome the issue, this paper proposes a VAE-based non-parallel VC conditioned by not only the speaker representations but also phonetic contents of speech represented as phonetic posteriorgrams (PPGs). Since the phonetic contents are given during the training, we can expect that the VC models effectively learn speaker-independent latent features of speech. Focusing on the point, this paper also extends the conventional VAE-based non-parallel VC to many-to-many VC that can convert arbitrary speakers' characteristics into another arbitrary speakers' ones. We investigate two methods to estimate speaker representations for speakers not included in speech corpora used for training VC models: 1) adapting conventional speaker codes, and 2) using d-vectors for the speaker representations. Experimental results demonstrate that 1) PPGs successfully improve both naturalness and speaker similarity of the converted speech, and 2) both speaker codes and d-vectors can be adopted to the VAE-based many-to-many non-parallel VC.