Reviewerly/exHarmony huggingface dataset:
Image Captioning in Chinese and Its Application for Children with Autism Spectrum Disorder

This research looks into the applications of image captioning in Chinese. In order to improve abilities of children with Autism Spectrum Disorder (ASD) in spontaneous language and turn-taking, during interacting with them, rehabilitation robots are used to track their attention and to describe attractive objects and scenes. This method has three advantages. First, the robots may attract the attention of children with ASD by describing the objects that of interest to them. Second, the robots may improve their ability of spontaneous language which is beneficial to enhancing superior language expressions and social communication with the robots or human beings. Third, describing objects that are interesting may develop the ability in picture description and may further improve their cognitive ability in rehabilitation training. The present study equips a socially interactive robot designed specifically for children with ASD with a dense image captioning approach. The contributions of this article are three-fold. First, an image captioning algorithm was developed to output Chinese description, whereas algorithms in previous studies were mostly catered for English description. Second, image captioning was adopted for the first time in the field of socially interactive robots. Third, our study demonstrates its potential in applying image captioning in robot-enhanced therapy, especially for Chinese children with ASD.
We are the Greatest Showmen: Configuring a Framework for Project-Based Mobile Learning

Little research has explored how mobile-learning technologies could be used by students to produce interactive artefacts during project-based learning processes. Following a design-based approach, we report on engagements spanning classroom and outdoor learning with students (ages 6-13) and teachers from three different UK schools and a summer school of Travelling Showchildren. Working within the time constraints of each context, we deployed a variety of configurations of a project-based mobile learning (PBML) framework intended to support the production of student-designed mobile-learning activities. We contribute insights gained from these engagements, including how mobile technologies can harness students' existing desire for independence and how they can be configured to leverage the physical and social attributes of place and community as learning resources. We argue for further exploration of the potential roles for mobile technologies within project-based learning, and contribute our PBML framework with recommendations for its re-configuration in response to contextual constraints.
Multi-wavelength phase-shifting interferometry based on a two-step phase-shifting phase retrieval algorithm with a color CMOS

By combining the advantage of multi-wavelength phase-shifting interferometry and two-step phase-shifting phase retrieval algorithm, the measured phase with high precision and large measuring range can be retrieved rapidly from three multi-wavelength interferograms with unknown phase shifts recorded by a single-chip color CMOS. First, two normalized differential interferograms without backgrounds are obtained by using the subtraction and then normalization among the three color interferograms. Then the wrapped phases of single wavelength are retrieved by using Gram-Schmidt (GS) orthonormalization approach. Finally the unambiguous phase of the three synthetic wavelengths can be achieved by the subtraction between the wrapped phases of corresponding wavelengths. To reduce the phase noise amplification which introduced by the magnification of wavelengths, the phase unwrapping algorithm of reducing the noise of the three synthetic wavelength to the level of two-synthetic long wavelength, single wavelength, and two-synthetic short wavelength are implemented by turn. Compared with the conventional multi-wavelength phase shifting interferometry, only three interferograms with unknown phase shifts are needed to reconstruct the actual phase of the measured object with high measurement resolution and large measurement range and can be used in dynamic phase measurement. Both simulation and experimental results demonstrate the performance of the proposed method.
Sankeye: A Visualization Technique for AOI Transitions

Visually exploring AOI transitions aggregated from a group of eye tracked people is a challenging task. Many visualizations typically produce visual clutter or aggregate the temporal or visit order information in the data hiding the visual task solution strategies for the observer. In this paper we introduce the Sankeye technique that is based on the visual metaphor of Sankey diagrams applied to eye movement data, hence the name Sankeye. The technique encodes the frequencies of AOI transitions into differently thick rivers and subrivers. The distributions of the AOI transitions are visually represented by splitting and merging subrivers in a left-to-right reading direction. The technique allows to interactively adapt the number of predefined AOIs as well as the transition frequency number threshold with the goal to derive patterns and insights from eye movement data.
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory

Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks. At the core of their empirical successes is the learned feature representation, which embeds rich observations, e.g., images and texts, into the latent space that encodes semantic structures. Meanwhile, the evolution of such a feature representation is crucial to the convergence of temporal-difference and Q-learning. In particular, temporal-difference learning converges when the function approximator is linear in a feature representation, which is fixed throughout learning, and possibly diverges otherwise. We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve? If it converges, does it converge to the optimal one? We prove that, utilizing an overparameterized two-layer neural network, temporal-difference and Q-learning globally minimize the mean-squared projected Bellman error at a sublinear rate. Moreover, the associated feature representation converges to the optimal one, generalizing the previous analysis of Cai et al. (2019) in the neural tangent kernel regime, where the associated feature representation stabilizes at the initial one. The key to our analysis is a mean-field perspective, which connects the evolution of a finite-dimensional parameter to its limiting counterpart over an infinite-dimensional Wasserstein space. Our analysis generalizes to soft Q-learning, which is further connected to policy gradient.
Applications and innovations in typeface design for North American Indigenous languages

In this contribution, we draw attention to prevailing issues that many speakers of Indigenous North American languages face when typing their languages, and identify examples of typefaces that have been developed and harnessed by historically marginalized language communities. We offer an overview of the field of typeface design as it serves endangered and Indigenous languages in North America, and we identify a clear role for typeface designers in creating typefaces tailored to the needs of Indigenous languages and the communities who use them. While cross-platform consistency and reliability are basic requirements that readers and writers of dominant world languages rightly take for granted, they are still only sporadically implemented for Indigenous languages whose speakers and writing systems have been subjected to sustained oppression and marginalization. We see considerable innovation and promise in this field, and are encouraged by collaborations between type designers and members of Indigenous communities. Our goal is to identify enduring challenges and draw attention to positive innovations, applications and grounds for hope in the development of typefaces by and with speakers and writers of Indigenous languages in North America.
Concepts of Educational Collaborations and Innovative Directions for University Development: Knowledge Export Educational Programs

Nowadays, the necessity for development of export educational programs in higher educational institutions is known as a priority task of the country’s development. It is considered as being urgent after the national project introduction, where the route maps for the requirements for knowledge export in Russian higher education institutions are specified. The aim of the article is to study the peculiarities of educational collaborations and the export educational programs’ implementation in various activities. Export educational programs represent resources which are relevant and required at the international educational market. The methods of research are represented by system analysis and synthesis, statistical methods of information processing, and expert methods. The practical value of the study and educational collaboration is proved by the fact that the integration of performers’ efforts leads to a synergetic effect which positively affects the smart university’s development, the designing and development of its export opportunities, and further increasing its competitiveness at the international educational market.
Multiple Kernel SVM Based on Two-Stage Learning

In this paper we introduce the idea of two-stage learning for multiple kernel SVM (MKSVM) and present a new MKSVM algorithm based on two-stage learning (MKSVM-TSL). The first stage is the pre-learning and its aim is to obtain the information of data such that the “important” samples for classification can be generated in the formal learning stage and these samples are uniformly ergodic Markov chain (u.e.M.c.). To study comprehensively the proposed MKSVM-TSL algorithm, we estimate the generalization bound of MKSVM based on u.e.M.c. samples and obtain its fast learning rate. And in order to show the performance of the proposed MKSVM-TSL algorithm for better, we also perform the numerical experiments on various publicly available datasets. From the experimental results, we can find that compared to three classical multiple kernel learning (MKL) algorithms, the proposed MKSVM-TSL algorithm has better performance in three aspects of the total time of sampling and training, the accuracy and the sparsity of classifiers, respectively.
Bi-directional online transfer learning: a framework

Abstract Transfer learning uses knowledge learnt in source domains to aid predictions in a target domain. When source and target domains are online, they are susceptible to concept drift, which may alter the mapping of knowledge between them. Drifts in online environments can make additional information available in each domain, necessitating continuing knowledge transfer both from source to target and vice versa. To address this, we introduce the Bi-directional Online Transfer Learning (BOTL) framework, which uses knowledge learnt in each online domain to aid predictions in others. We introduce two variants of BOTL that incorporate model culling to minimise negative transfer in frameworks with high volumes of model transfer. We consider the theoretical loss of BOTL, which indicates that BOTL achieves a loss no worse than the underlying concept drift detection algorithm. We evaluate BOTL using two existing concept drift detection algorithms: RePro and ADWIN. Additionally, we present a concept drift detection algorithm, Adaptive Windowing with Proactive drift detection (AWPro), which reduces the computation and communication demands of BOTL. Empirical results are presented using two data stream generators: the drifting hyperplane emulator and the smart home heating simulator, and real-world data predicting Time To Collision (TTC) from vehicle telemetry. The evaluation shows BOTL and its variants outperform the concept drift detection strategies and the existing state-of-the-art online transfer learning technique.
Old Document Restoration using Super Resolution GAN and Semantic Image Inpainting

Restoration of damaged images is a fundamental problem that has been attempted before the advent of digital image processing technology. In this paper, one of the deep neural network technologies (GAN), we propose an image restoration network using Generative Adversarial Network. The proposed system is the image generation network, the generation result plateIt consists of a star network. Old documents not only contain information, but also we can learn about historical people's thought and consciousness from the past. Old document restoration is referred to as the restoration of documents that are usually made of parchment which are damaged either naturally or artificially. Missing regions in old documents are filled based on the current visual data which is a hard task in image inpainting. In this paper, we present Super Resolution Generative Adversarial Network (SRGAN) and semantic image inpainting for restoring the old documents so that they can be reused.
A brief note on the Scott measure as a multipartite entanglement criterion

In recent decades, various multipartite entanglement measures have been proposed by many researchers, with different characteristics. Meanwhile, Scott studied various interesting aspects of multipartite entanglement measures and he has defined a class of related multipartite entanglement measures in an obvious manner. Recently, Jafarpour et al. (Int. J. Quantum Inform. 13, 1550047 2015) have calculated the entanglement quantity of two-dimensional 5-site spin system by means of Scott measure. As it was presented in their calculation Q_{2}>Q_{3}. In this paper, we would like to point out that in the 5-qudit system, Q_{2} does not provide a stronger entanglement than Q_{3}, while there is a simple relation between them.
Modeling and Analyzing Architectural Diversity of Open Platforms

Nowadays, successful software companies attain enhanced business objectives by opening their platforms to thousands of third-party providers. When developing an open platform many architectural design decisions have to be made, which are driven from the companies' business objectives. The set of decisions results in an overwhelming design space of architectural variabilities. Until now, there are no architectural guidelines and tools that explicitly capture design variabilities of open platforms and their relation to business objectives. As a result, systematic knowledge is missing; platform providers have to fall back to ad-hoc decision-making; this bears consequences such as risks of failure and extra costs. In this paper, we present a pattern-driven approach called SecoArc to model diverse design decisions of open platforms and to analyze and compare alternative architectures with respect to business objectives. SecoArc consists of a design process, a modeling language, and an automated architectural analysis technique. It is implemented and ready-to-use in a tool. We evaluate the approach by means of a real-world case study. Results show that the approach improves the decision-making. Future platform providers can reduce risks by making informed decisions at design time.
WaveNODE: A Continuous Normalizing Flow for Speech Synthesis

In recent years, various flow-based generative models have been proposed to generate high-fidelity waveforms in real-time. However, these models require either a well-trained teacher network or a number of flow steps making them memory-inefficient. In this paper, we propose a novel generative model called WaveNODE which exploits a continuous normalizing flow for speech synthesis. Unlike the conventional models, WaveNODE places no constraint on the function used for flow operation, thus allowing the usage of more flexible and complex functions. Moreover, WaveNODE can be optimized to maximize the likelihood without requiring any teacher network or auxiliary loss terms. We experimentally show that WaveNODE achieves comparable performance with fewer parameters compared to the conventional flow-based vocoders.
A Bayesian Approach to Heavy-Tailed Finite Mixture Autoregressive Models

In this paper, a Bayesian analysis of finite mixture autoregressive (MAR) models based on the assumption of scale mixtures of skew-normal (SMSN) innovations (called SMSN–MAR) is considered. This model is not simultaneously sensitive to outliers, as the celebrated SMSN distributions, because the proposed MAR model covers the lightly/heavily-tailed symmetric and asymmetric innovations. This model allows us to have robust inferences on some non-linear time series with skewness and heavy tails. Classical inferences about the mixture models have some problematic issues that can be solved using Bayesian approaches. The stochastic representation of the SMSN family allows us to develop a Bayesian analysis considering the informative prior distributions in the proposed model. Some simulations and real data are also presented to illustrate the usefulness of the proposed models.
ARID: A New Dataset for Recognizing Action in the Dark

The task of action recognition in dark videos is useful in various scenarios, e.g., night surveillance and self-driving at night. Though progress has been made in the action recognition task for videos in normal illumination, few have studied action recognition in the dark. This is partly due to the lack of sufficient datasets for such a task. In this paper, we explored the task of action recognition in dark videos. We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset. It consists of over 3,780 video clips with 11 action categories. To the best of our knowledge, it is the first dataset focused on human actions in dark videos. To gain further understandings of our ARID dataset, we analyze the ARID dataset in detail and exhibited its necessity over synthetic dark videos. Additionally, we benchmarked the performance of several current action recognition models on our dataset and explored potential methods for increasing their performances. Our results show that current action recognition models and frame enhancement methods may not be effective solutions for the task of action recognition in dark videos.
Temporally-Extended {\epsilon}-Greedy Exploration

Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expense of generality. Recent empirical studies suggest that, when applied to a broader set of domains, some sophisticated exploration methods are outperformed by simpler counterparts, such as {\epsilon}-greedy. In this paper we propose an exploration algorithm that retains the simplicity of {\epsilon}-greedy while reducing dithering. We build on a simple hypothesis: the main limitation of {\epsilon}-greedy exploration is its lack of temporal persistence, which limits its ability to escape local optima. We propose a temporally extended form of {\epsilon}-greedy that simply repeats the sampled action for a random duration. It turns out that, for many duration distributions, this suffices to improve exploration on a large set of domains. Interestingly, a class of distributions inspired by ecological models of animal foraging behaviour yields particularly strong performance.
Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations

Abstract This paper is focused on the nonlinear state estimation problem with finite-step correlated noises and packet loss. Firstly, by using the projection theorem repeatedly, the mean and covariance of process noise and measurement noise in the condition of measurements before the current epoch are calculated. Then, based on the Gaussian approximation recursive filter (GASF) and the prediction compensation mechanism, one-step predictor and filter with packet dropouts are derived, respectively. Based on these, a nonlinear Gaussian recursive filter is proposed. Subsequently, the numerical implementation is derived based on the cubature Kalman filter (CKF), which is suitable for general nonlinear system and with higher accuracy compared to the algorithm expanded from linear system to nonlinear system through Taylor series expansion. Finally, the strong nonlinearity model is used to show the superiority of the proposed algorithm.
Interpretable Biometrics: Should We Rethink How Presentation Attack Detection is Evaluated?

Presentation attack detection (PAD) methods are commonly evaluated using metrics based on the predicted labels. This is a limitation, especially for more elusive methods based on deep learning which can freely learn the most suitable features. Though often being more accurate, these models operate as complex black boxes which makes the inner processes that sustain their predictions still baffling. Interpretability tools are now being used to delve deeper into the operation of machine learning methods, especially artificial networks, to better understand how they reach their decisions. In this paper, we make a case for the integration of interpretability tools in the evaluation of PAD. A simple model for face PAD, based on convolutional neural networks, was implemented and evaluated using both traditional metrics (APCER, BPCER and EER) and interpretability tools (Grad-CAM), using data from the ROSE Youtu video collection. The results show that interpretability tools can capture more completely the intricate behavior of the implemented model, and enable the identification of certain properties that should be verified by a PAD method that is robust, coherent, meaningful, and can adequately generalize to unseen data and attacks. One can conclude that, with further efforts devoted towards higher objectivity in interpretability, this can be the key to obtain deeper and more thorough PAD performance evaluation setups.
Online Positive and Unlabeled Learning

Positive and Unlabeled learning (PU learning) aims to build a binary classifier where only positive and unlabeled data are available for classifier training. However, existing PU learning methods all work on a batch learning mode, which cannot deal with the online learning scenarios with sequential data. Therefore, this paper proposes a novel positive and unlabeled learning algorithm in an online training mode, which trains a classifier solely on the positive and unlabeled data arriving in a sequential order. Specifically, we adopt an unbiased estimate for the loss induced by the arriving positive or unlabeled examples at each time. Then we show that for any coming new single datum, the model can be updated independently and incrementally by gradient based online learning method. Furthermore, we extend our method to tackle the cases when more than one example is received at each time. Theoretically, we show that the proposed online PU learning method achieves low regret even though it receives sequential positive and unlabeled data. Empirically, we conduct intensive experiments on both benchmark and real-world datasets, and the results clearly demonstrate the effectiveness of the proposed method.
Noisy distributed sensing in the Bayesian regime

We consider non-local sensing of scalar signals with specific spatial dependence in the Bayesian regime. We design schemes that allow one to achieve optimal scaling and are immune to noise sources with a different spatial dependence than the signal. This is achieved by using a sensor array of spatially separated sensors and constructing a multi-dimensional decoherence free subspace. While in the Fisher regime with sharp prior and multiple measurements only the spectral range $\Delta$ is important, in single-shot sensing with broad prior the number of available energy levels $L$ is crucial. We study the influence of $L$ and $\Delta$ also in intermediate scenarios, and show that these quantities can be optimized separately in our setting. This provides us with a flexible scheme that can be adapted to different situations, and is by construction insensitive to given noise sources.