Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention and Encoder-Decoder based models for transforming articulatory movements at different speaking rates

While speaking at different rates, articulators (like tongue, lips) tend to move differently and the enunciations are also of different durations. In the past, affine transformation and DNN have been used to transform articulatory movements from neutral to fast(N2F) and neutral to slow(N2S) speaking rates [1]. In this work, we improve over the existing transformation techniques by modeling rate specific durations and their transformation using AstNet, an encoder-decoder framework with attention. In the current work, we propose an encoder-decoder architecture using LSTMs which generates smoother predicted articulatory trajectories. For modeling duration variations across speaking rates, we deploy attention network, which eliminates the needto align trajectories in different rates using DTW. We performa phoneme specific duration analysis to examine how well duration is transformed using the proposed AstNet. As the range of articulatory motions is correlated with speaking rate, we also analyze amplitude of the transformed articulatory movements at different rates compared to their original counterparts, to examine how well the proposed AstNet predicts the extent of articulatory movements in N2F and N2S. We observe that AstNet could model both duration and extent of articulatory movements better than the existing transformation techniques resulting in more accurate transformed articulatory trajectories.
Short Call-broadcasts Fail to Detect Nesting Least Bitterns (Ixobrychus exilis)

Abstract We conducted 11 call-broadcast surveys at one location in each of four wetlands, and detected an Ixobrychus exilis (Least Bittern) on only 9 of the 44 (20%) surveys, while the observer was located on average 50.1 m ± 19.7 SD (range = 25–75 m, n = 11 nests) from at least one active bittern nest during each survey. For 8 of 9 (89%) detections, at least one bittern was already vocalizing at the beginning of the survey, before the Least Bittern call was played. We show that it is possible for this species to remain undetected with the use of call-broadcasts that are less than 30 seconds, even though the species may be nesting as close as 25 m to the location from which the calls are broadcasted. We suggest that systematic nest searches are more reliable than call-broadcast surveys for detecting nesting Least Bitterns.
Contextual RNN-T For Open Domain ASR

End-to-end (E2E) systems for automatic speech recognition (ASR), such as RNN Transducer (RNN-T) and Listen-Attend-Spell (LAS) blend the individual components of a traditional hybrid ASR system - acoustic model, language model, pronunciation model - into a single neural network. While this has some nice advantages, it limits the system to be trained using only paired audio and text. Because of this, E2E models tend to have difficulties with correctly recognizing rare words that are not frequently seen during training, such as entity names. In this paper, we propose modifications to the RNN-T model that allow the model to utilize additional metadata text with the objective of improving performance on these named entity words. We evaluate our approach on an in-house dataset sampled from de-identified public social media videos, which represent an open domain ASR task. By using an attention model and a biasing model to leverage the contextual metadata that accompanies a video, we observe a relative improvement of about 16% in Word Error Rate on Named Entities (WER-NE) for videos with related metadata.
The lifetimes of planetary debris discs around white dwarfs

The lifetime of a planetary disc that orbits a white dwarf represents a crucial input parameter into evolutionary models of that system. Here we apply a purely analytical formalism to estimate lifetimes of the debris phase of these discs, before they are ground down into dust or are subject to sublimation from the white dwarf. We compute maximum lifetimes for three different types of white dwarf discs, formed from (i) radiative YORP break-up of exo-asteroids along the giant branch phases at 2–100 au, (ii) radiation-less spin-up disruption of these minor planets at ${\sim} 1.5\! -\! 4.5\, \mathrm{R}_{\odot }$, and (iii) tidal disruption of minor or major planets within about $1.3\, \mathrm{R}_{\odot }$. We display these maximum lifetimes as a function of disc mass and extent, constituent planetesimal properties, and representative orbital excitations of eccentricity and inclination. We find that YORP discs with masses of up to 1024 kg live long enough to provide a reservoir of surviving cm-sized pebbles and m- to km-sized boulders that can be perturbed intact to white dwarfs with cooling ages of up to 10 Gyr. Debris discs formed from the spin or tidal disruption of these minor planets or major planets can survive in a steady state for up to, respectively, 1 or 0.01 Myr, although most tidal discs would leave a steady state within about 1 yr. Our results illustrate that dust-less planetesimal transit detections are plausible, and would provide particularly robust evolutionary constraints. Our formalism can easily be adapted to individual systems and future discoveries.
Path towards multilevel evolution of robots

Multi-level evolution is a bottom-up robotic design paradigm which decomposes the design problem into layered sub-tasks that involve concurrent search for appropriate materials, component geometry and overall morphology. Each of the three layers operate with the goal of building a library of diverse candidate solutions which will be used either as building blocks for the layer above or provided to the decision maker for final use. In this paper we provide a theoretical discussion on the concepts and technologies that could potentially be used as building blocks for this framework.
The Colour Red for Emotion in Cross-Cultural E-Commerce

Web stores tend to differentiate themselves from competitors by relying on hedonic aspects, such as colour. This study focusses on web store colour and emotions in the online shopping process. Preferences of Western and Asian consumers are compared when exposed to a web store with specific colour values of red. This was done in the context of an online booking web store for hotels. An online experimental survey was conducted with two conditions, viz., dark red and light red. A total of 220 participants (130 Dutch and 90 Vietnamese) completed the questionnaire. Contrary to expectation, the results showed no direct effect of colour: the light red web store did not induce the highest booking intentions, though an indirect colour effect was found through the pleasure emotion. The light red web store induced more pleasure, which in turn induced higher booking intentions. Cultural differences (indulgent vs. restricted), influenced the relationship between colour and the arousal emotion. Participants from the more indulgent culture were more induced by the light colour web store.
GPHQP: Hierarchical Quadratic Programming for Controlling a Gaussian Process Regression Model of Redundant Robot

Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which is common for minimally invasive surgery, intrinsic nonlinearities are important to consider. Traditional analytical methods allow to build the kinematic model of the system by making certain assumptions and simplifications on the nonlinearities. Machine learning techniques, instead, allow to recover a more complex model based on the acquired data. However, analytical models are more generalisable, but can be over-simplified; data-driven models, on the other hand, can cater for more complex models, but are less generalisable and the result is highly affected by the training dataset. In this paper, we present a novel approach to combining analytical and data-driven approaches to model the kinematics of nonlinear tendon-driven surgical robots. Gaussian Process Regression (GPR) is used for learning the data-driven model and the proposed method is tested on both simulated data and real experimental data.
A Survey on Speech Signal using Multirate Digital Signal Processing

— Multirate systems are most commonly used in Digital Signal Processing (DSP). Systems that use more than one sampling rates in the digital signal processing are called multirate DSP. Multirate DSP consists of decimation and interpolation which are rate conversion techniques. Decimation reduces the sampling rate whereas interpolation increases the sampling rate of the signals. In this paper, some of the multirate signals processing techniques are discussed and FIR filter designs are implemented using different windowing techniques. The performances provided by those techniques are compared based on SNR, RMS, MSE, ENBW and correlation.
GENDER PERSPECTIVE ON THE TRAIT EMOTIONAL INTELLIGENCE AS A PREDICTOR OF CAREER INDECISION

Emotional intelligence (EI) contributes to career decision-making. In the paper, the influence of trait EI on career decision-making, specifically on career indecision and career decision-making difficulties, plus gender differences, is investigated in a sample of 156 high-school students (Mage: 17.7 /SD=.40/; 59% of females) by t-test, correlation and regression analysis. Trait EI was assessed by Trait Emotional Intelligence Questionnaire – Short Form (TEIQue-SF; Petrides, 2009), career decidedness by Career Decidedness Scale (CDS; Lounsbury, & Gibson, 2011) and career decision-making difficulties by Emotional and Personality Career Difficulties Scale (EPCD, Saka, Gati, & Kelly, 2008). Results: 1. no inter-gender differences in the global trait EI, emotional and personality-related aspects of career decision-making difficulties, and career decidedness; 2. a general trend of positive relations between trait EI and career decidedness and negative relations between trait EI and career decision-making difficulties; 3. trait EI as a significant negative predictor of career decision-making difficulties, over and above decidedness in both samples, where trait EI predicted a significant 8% of unique variance in career decision-making difficulties after controlling for decidedness level in females sample and up to 5% in males sample supporting the incremental validity of trait EI, and its potential to predict difficulties in career decision-making.
Using an interpretable Machine Learning approach to study the drivers of International Migration

Globally increasing migration pressures call for new modelling approaches in order to design effective policies. It is important to have not only efficient models to predict migration flows but also to understand how specific parameters influence these flows. In this paper, we propose an artificial neural network (ANN) to model international migration. Moreover, we use a technique for interpreting machine learning models, namely Partial Dependence Plots (PDP), to show that one can well study the effects of drivers behind international migration. We train and evaluate the model on a dataset containing annual international bilateral migration from $1960$ to $2010$ from $175$ origin countries to $33$ mainly OECD destinations, along with the main determinants as identified in the migration literature. The experiments carried out confirm that: 1) the ANN model is more efficient w.r. t. a traditional model, and 2) using PDP we are able to gain additional insights on the specific effects of the migration drivers. This approach provides much more information than only using the feature importance information used in previous works.
NewB: 200, 000+ Sentences for Political Bias Detection

We present the Newspaper Bias Dataset (NewB), a text corpus of more than 200,000 sentences from eleven news sources regarding Donald Trump. While previous datasets have labeled sentences as either liberal or conservative, NewB covers the political views of eleven popular media sources, capturing more nuanced political viewpoints than a traditional binary classification system does. We train two state-of-the-art deep learning models to predict the news source of a given sentence from eleven newspapers and find that a recurrent neural network achieved top-1, top-3, and top-5 accuracies of 33.3%, 61.4%, and 77.6%, respectively, significantly outperforming a baseline logistic regression model's accuracies of 18.3%, 42.6%, and 60.8%. Using the news source label of sentences, we analyze the top n-grams with our model to gain meaningful insight into the portrayal of Trump by media sources.We hope that the public release of our dataset will encourage further research in using natural language processing to analyze more complex political biases. Our dataset is posted at this https URL .
Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds

It has witnessed a growing demand for efficient representation learning on point clouds in many 3D computer vision applications. Behind the success story of convolutional neural networks (CNNs) is that the data (e.g., images) are Euclidean structured. However, point clouds are irregular and unordered. Various point neural networks have been developed with isotropic filters or using weighting matrices to overcome the structure inconsistency on point clouds. However, isotropic filters or weighting matrices limit the representation power. In this paper, we propose a permutable anisotropic convolutional operation (PAI-Conv) that calculates soft-permutation matrices for each point using dot-product attention according to a set of evenly distributed kernel points on a sphere's surface and performs shared anisotropic filters. In fact, dot product with kernel points is by analogy with the dot-product with keys in Transformer as widely used in natural language processing (NLP). From this perspective, PAI-Conv can be regarded as the transformer for point clouds, which is physically meaningful and is robust to cooperate with the efficient random point sampling method. Comprehensive experiments on point clouds demonstrate that PAI-Conv produces competitive results in classification and semantic segmentation tasks compared to state-of-the-art methods.
Extracting Spatiotemporal Demand for Public Transit from Mobility Data

With people constantly migrating to different urban areas, our mobility needs for work, services and leisure are transforming rapidly. The changing urban demographics pose several challenges for the efficient management of transit services. To forecast transit demand, planners often resort to sociological investigations or modelling that are either difficult to obtain, inaccurate or outdated. How can we then estimate the variegated demand for mobility? We propose a simple method to identify the spatiotemporal demand for public transit in a city. Using a Gaussian mixture model, we decompose empirical ridership data into a set of temporal demand profiles representative of ridership over any given day. A case of approximately 4.6 million daily transit traces from the Greater London region reveals distinct demand profiles. We find that a weighted mixture of these profiles can generate any station traffic remarkably well, uncovering spatially concentric clusters of mobility needs. Our method of analysing the spatiotemporal geography of a city can be extended to other urban regions with different modes of public transit.
Learning Neural Light Transport

In recent years, deep generative models have gained significance due to their ability to synthesize natural-looking images with applications ranging from virtual reality to data augmentation for training computer vision models. While existing models are able to faithfully learn the image distribution of the training set, they often lack controllability as they operate in 2D pixel space and do not model the physical image formation process. In this work, we investigate the importance of 3D reasoning for photorealistic rendering. We present an approach for learning light transport in static and dynamic 3D scenes using a neural network with the goal of predicting photorealistic images. In contrast to existing approaches that operate in the 2D image domain, our approach reasons in both 3D and 2D space, thus enabling global illumination effects and manipulation of 3D scene geometry. Experimentally, we find that our model is able to produce photorealistic renderings of static and dynamic scenes. Moreover, it compares favorably to baselines which combine path tracing and image denoising at the same computational budget.
Analytic expressions for the Cumulative Distribution Function of the Composed Error Term in Stochastic Frontier Analysis with Truncated Normal and Exponential Inefficiencies

In the stochastic frontier model, the composed error term consists of the measurement error and the inefficiency term. A general assumption is that the inefficiency term follows a truncated normal or exponential distribution. In a wide variety of models evaluating the cumulative distribution function of the composed error term is required. This work introduces and proves four representation theorems for these distributions - two for each distributional assumptions. These representations can be utilized for a fast and accurate evaluation.
DABANGG: Time for Fearless Flush based Cache Attacks

Flush based cache attacks like Flush+Reload and Flush+Flush are one of the highly effective cache attacks. In fact, the Flush+Flush attack is stealthy too. Most of the ﬂush based attacks provide high accuracy in controlled environments where attacker and victim are the only two processes that are running on a system by sharing OS pages. However, we observe that these attacks lose their effectiveness (prone to low accuracy) on a noisy multi-core system where co-running applications run along with the attacker and the victim. Two root causes for the varying accuracy of ﬂush based attacks are: (i) the dynamic nature of core frequencies that ﬂuctuate depending on the system load, and (ii) the relative placement of victim and attacker threads in the processor (same logical core, same physical core, different physical cores). The variation in the processor frequencies and placement of threads affect one of the critical attack steps (the cache latency calibration step as the latency threshold set to distinguish a cache hit from a miss becomes inaccurate). We propose a set of reﬁnements (DABANGG reﬁne-ments) to make existing ﬂush attacks resilient to frequency changes and thread placement in the processor, and therefore system noise. We propose reﬁnements to pre-attack and attack steps and make it conscious about the latency change. We evaluate DABANGG-enabled Flush+Reload and Flush+Flush attacks (DABANGG+Flush+Reload and DA-BANGG+Flush+Flush, respectively) against the standard Flush+Reload and Flush+Flush attacks across ﬁve scenarios for eight different combinations of system noise capturing different levels of compute, memory, and I/O noise intensities: (i) a side-channel attack based on user input (single-character and multi-character key-logging), (ii) a side-channel on AES, (iii) a side-channel on RSA, (iv) a covert-channel, and (v) a transient execution attack in the form the Spectre attack. We also discuss the cross-VM feasibility for each attack scenario. For all the scenarios, DABANGG+Flush+Reload and DA-BANGG+Flush+Flush outperform the standard Flush+Reload and Flush+Flush attacks in terms of F 1 score and accuracy.
End-to-End Adversarial Text-to-Speech

Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech audio outputs. Our proposed generator is feed-forward and thus efficient for both training and inference, using a differentiable alignment scheme based on token length prediction. It learns to produce high fidelity audio through a combination of adversarial feedback and prediction losses constraining the generated audio to roughly match the ground truth in terms of its total duration and mel-spectrogram. To allow the model to capture temporal variation in the generated audio, we employ soft dynamic time warping in the spectrogram-based prediction loss. The resulting model achieves a mean opinion score exceeding 4 on a 5 point scale, which is comparable to the state-of-the-art models relying on multi-stage training and additional supervision.
Framework on Deep Learning-Based Joint Hybrid Processing for mmWave Massive MIMO Systems

For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is essential to significantly reduce the complexity and cost but is quite challenging to be jointly optimized over the transmitter and receiver. In this paper, deep learning (DL) is applied to design a novel joint hybrid processing framework (JHPF) that allows end-to-end optimization by using back propagation. The proposed framework includes three parts: hybrid processing designer, signal flow simulator, and signal demodulator, which outputs the hybrid processing matrices for the transceiver by using neural networks (NNs), simulates the signal transmission over the air, and maps the detected symbols to the original bits by using the NN, respectively. By minimizing the cross-entropy loss between the recovered and original bits, the proposed framework optimizes the analog and digital processing matrices at the transceiver jointly and implicitly instead of approximating pre-designed label matrices, and its trainability is proved theoretically. It can be also directly applied to orthogonal frequency division multiplexing systems by simply modifying the structure of the training data. Simulation results show the proposed DL-JHPF outperforms the existing hybrid processing schemes and is robust to the mismatched channel state information and channel scenarios with the significantly reduced runtime.
Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform

In this paper we show that inference in 2-variable Markov logic networks (MLNs) with cardinality and function constraints is domain-liftable. To obtain this result we use existing domain-lifted algorithms for weighted first-order model counting (Van den Broeck et al, KR 2014) together with discrete Fourier transform of certain distributions associated to MLNs.
Propensity to Engage in Unethical Behavior (PUB-CL): Adaptation and Validation of a Questionnaire for Leaders of Christian Organizations

Though many instruments have been developed to measure attitudes toward unethical behavior in business, there is a scarcity of instruments to measure it among leaders of Christian organizations. Hence, the purpose of this paper is to adapt Chen and Tang’s (2006) Unethical Behavior Scale in order to make it appropriate for leaders of Christian organizations. The process consisted of calculating content and face validity, construct validity through factor analysis, and reliability through Cronbach’s  coefficient. Differing with Chen and Tang’s (2006) operationalization, the exploratory factor analysis revealed three factors of unethical behavior that were labeled major misbehaviors, minor misbehaviors, and not whistle blowing. The items revealed factor loadings > 0.5, and Cronbach’s  coefficient was . 969 for the complete scale and between . 869 and . 973 for the subscales. Tested among 203 participants, the final scale contains 27 items that are valid, reliable, and useful for measuring propensity to engage in unethical behavior among leaders of Christian organizations.