Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Acoustic Model Training for Five-Lingual Code-Switched ASR

This paper presents recent progress in the acoustic modelling of under-resourced code-switched (CS) speech in multiple South African languages. We consider two approaches. The first constructs separate bilingual acoustic models corresponding to language pairs (English-isiZulu, English-isiXhosa, English-Setswana and English-Sesotho). The second constructs a single unified five-lingual acoustic model representing all the languages (English, isiZulu, isiXhosa, Setswana and Sesotho). For these two approaches we consider the effectiveness of semi-supervised training to increase the size of the very sparse acoustic training sets. Using approximately 11 hours of untranscribed speech, we show that both approaches benefit from semi-supervised training. The bilingual TDNN-F acoustic models also benefit from the addition of CNN layers (CNN-TDNN-F), while the five-lingual system does not show any significant improvement. Furthermore, because English is common to all language pairs in our data, it dominates when training a unified language model, leading to improved English ASR performance at the expense of the other languages. Nevertheless, the five-lingual model offers flexibility because it can process more than two languages simultaneously, and is therefore an attractive option as an automatic transcription system in a semi-supervised training pipeline.
NISER: Normalized Item and Session Representations with Graph Neural Networks

The goal of session-based recommendation (SR) models is to utilize theinformation from past actions (e.g. item/product clicks) in a session torecommend items that a user is likely to click next. Recently it has been shownthat the sequence of item interactions in a session can be modeled asgraph-structured data to better account for complex item transitions. Graphneural networks (GNNs) can learn useful representations for suchsession-graphs, and have been shown to improve over sequential models such asrecurrent neural networks [14]. However, we note that these GNN-basedrecommendation models suffer from popularity bias: the models are biasedtowards recommending popular items, and fail to recommend relevant long-tailitems (less popular or less frequent items). Therefore, these models performpoorly for the less popular new items arriving daily in a practical onlinesetting. We demonstrate that this issue is, in part, related to the magnitudeor norm of the learned item and session-graph representations (embeddingvectors). We propose a training procedure that mitigates this issue by usingnormalized representations. The models using normalized item and session-graphrepresentations perform significantly better: i. for the less popular long-tailitems in the offline setting, and ii. for the less popular newly introduceditems in the online setting. Furthermore, our approach significantly improvesupon existing state-of-the-art on three benchmark datasets.
A Method for the Destriping of an Orbita Hyperspectral Image with Adaptive Moment Matching and Unidirectional Total Variation

The Orbita hyperspectral satellite (OHS) is the first hyperspectral satellite with surface coating technology for sensors in the world. It includes 32 bands from visible to near-infrared wavelengths. However, technology such as the fabricating process of complementary metal–oxide–semiconductor (CMOS) sensors makes the image contain a lot of random and unsystematic stripe noise, which is so bad that it seriously affects visual interpretation, object recognition and the application of the OHS data. Although a large number of stripe removal algorithms have been proposed, very few of them take into account the characteristics of OHS sensors and analyze the causes of OHS data noise. In this paper, we propose a destriping algorithm for OHS data. Firstly, we use both the adaptive moment matching method and multi-level unidirectional total variation method to remove stripes. Then a model based on piecewise linear least squares fitting is proposed to restore the vertical details lost in the first step. Moreover, we further utilize the spectral information of the OHS image, and extend our 2-D destriping method to the 3-D case. Results demonstrate that the proposed method provides the optimal destriping result on both qualitative and quantitative assessments. Moreover, the experimental results show that our method is superior to the existing single-band and multispectral destriping methods. Also, we further use the algorithm to the stripe noise removal of other real remote sensing images, and excellent image quality is obtained, which proves the universality of the algorithm.
On the Realization of Compositionality in Neural Networks

We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task.The models are architecturally identical at inference time, but differ in the way that they are trained: our baseline model is trained with a task-success signal only, while the other model receives additional supervision on its attention mechanism (Attentive Guidance), which has shown to be an effective method for encouraging more compositional solutions (Hupkes et al., 2019).We first confirm that the models with attentive guidance indeed infer more compositional solutions than the baseline, by training them on the lookup table task presented by Liška et al. (2019).We then do an in-depth analysis of the structural differences between the two model types, focusing in particular on the organisation of the parameter space and the hidden layer activations and find noticeable differences in both these aspects.Guided networks focus more on the components of the input rather than the sequence as a whole and develop small functional groups of neurons with specific purposes that use their gates more selectively.Results from parameter heat maps, component swapping and graph analysis also indicate that guided networks exhibit a more modular structure with a small number of specialized, strongly connected neurons.
Career Age-Aware Scientific Collaborator Recommendation in Scholarly Big Data

Seeking a collaborator is one of the important academic activities of scholars because the right collaborators will help improve the quality of scholars’ research and accelerate their research process. Therefore, it is becoming more and more important to recommend scientific collaborators based on big scholarly data. However, previous works mainly consider the research topic as the key academic factor, whereas many scholars’ demographic characteristics such as career age, gender, etc are overlooked. It has been studied that scientific collaboration patterns may vary with scholars’ career ages. It is not surprising that scholars at different career ages may have different collaboration strategies. To this end, we aim to design a scientific collaboration recommendation model that is sensitive to scholars’ career age. For this purpose, we design a career age-aware scientific collaboration model. The model is mainly consisted of three parts, including authorship extraction from the digital libraries, topic extraction based on publication titles/abstract, and career age-aware random walk for measuring scholar similarity. Experimental results on two real-world datasets demonstrate that our proposed model can achieve the best performance by comparison with six baseline methods in terms of precision and recall.
“My Way of Telling a Story”: Persona based Grounded Story Generation

Visual storytelling is the task of generating stories based on a sequence of images.Inspired by the recent works in neural generation focusing on controlling the form of text, this paper explores the idea of generating these stories in different personas.However, one of the main challenges of performing this task is the lack of a dataset of visual stories in different personas.Having said that, there are independent datasets for both visual storytelling and annotated sentences for various persona.In this paper we describe an approach to overcome this by getting labelled persona data from a different task and leveraging those annotations to perform persona based story generation.We inspect various ways of incorporating personality in both the encoder and the decoder representations to steer the generation in the target direction.To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand.In our experiments we use five different personas to guide the generation process.We find that the models based on our hypotheses perform better at capturing words while generating stories in the target persona.
Studying the Impact of Noises in Build Breakage Data

Much research has investigated the common reasons for build breakages. However, prior research has paid little attention to builds that may break due to reasons that are unlikely to be related to development activities. For example, Continuous Integration (CI) builds may break due to timeout or connection errors while generating the build. Such kinds of build breakages potentially introduce noises to build breakage data. Not considering such noises may lead to misleading results when studying CI builds. In this paper, we propose three criteria to identify build breakages that can potentially introduce noises to build breakage data. We apply these criteria to a dataset of 350,246 builds from 153 GitHub projects that are linked with Travis CI. Our results reveal that 33 percent of the build breakages are due to environmental factors (e.g., errors in CI servers), 29 percent are due to (unfixed) errors in previous builds, and 9 percent are due to build jobs that were later deemed by developers as noisy (there is an overlap of 17 percent between these three types of breakages). We measure the impact of noises in build breakage data on modeling build breakages. We observe that models that use uncleaned build breakage data can lead to misleading associations between build breakages and development activities (e.g., the role of developer). However, such associations could not be observed after eliminating noisy build breakages. Moreover, we replicate a prior study that investigates the association between build breakages and development activities using data from 14 GitHub projects. We observe that some observations reported by the prior study (e.g., pull requests cause more breakages) do not hold after eliminating the noises from build breakage data.
SUPER-RESOLUTION FOR SENTINEL-2 IMAGES

Abstract. Obtaining Sentinel-2 imagery of higher spatial resolution than the native bands while ensuring that output imagery preserves the original radiometry has become a key issue since the deployment of Sentinel-2 satellites. Several studies have been carried out on the upsampling of 20 m and 60 m Sentinel-2 bands to 10 meters resolution taking advantage of 10 m bands. However, how to super-resolve 10 m bands to higher resolutions is still an open problem. Recently, deep learning-based techniques has become a de facto standard for single-image super-resolution. The problem is that neural network learning for super-resolution requires image pairs at both the original resolution (10 m in Sentinel-2) and the target resolution (e.g., 5 m or 2.5 m). Since there is no way to obtain higher resolution images for Sentinel-2, we propose to consider images from others sensors having the greatest similarity in terms of spectral bands, which will be appropriately pre-processed. These images, together with Sentinel-2 images, will form our training set. We carry out several experiments using state-of-the-art Convolutional Neural Networks for single-image super-resolution showing that this methodology is a first step toward greater spatial resolution of Sentinel-2 images.
Whatsapp sebagai Media Literasi Digital Siswa

Whatsapp is as one of the most influential social media and widely used by people in Indonesia. Students in Indonesia in the digital era are now using gadgets in their daily activities both at school and outside of school. However, most of these students use social media applications, especially Whatsapp, only for social media, whereas in the application, there are benefits that can improve digital literacy skills. The purpose of this research is to find out: 1) the use of whatsapp as the digital literacy media of students and 2) the digital literacy skills of students in terms of language skills. The method used in this study is to refer, note, and study the literature. This research is a qualitative descriptive study. The results of this study are, 1) the level of digital literacy of students with whatsapp social media is still lacking and 2) improvement of students’ digital literacy ability in terms of aspects of language ability, namely, listening, speaking, reading, watching, writing, and writing.
Astrophotonics: The Rise of Integrated Photonics in Astronomy

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text Barnaby Norris and Joss Bland-Hawthorn, "Astrophotonics: The Rise of Integrated Photonics in Astronomy," Optics & Photonics News 30(5), 26-33 (2019) Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
The Feasibility Study of Augmented Reality Technology in Early Childhood Education

With the rapid development of science and technology, the development and improvement of hardware and software make the cost of developers and users greatly reduced, educational knowledge and learning environment and communication methods have undergone great changes. Based on the characteristics of augmented reality technology and the characteristics of children's psychology, this paper puts forward the feasibility analysis of augmented reality technology in the field of early childhood education.
Informative Feature Selection for Domain Adaptation

Domain adaptation aims at extracting knowledge from an auxiliary source domain to assist the learning task in a target domain. When the data distribution of the target domain is different from that of the source domain, the direct use of source data for building a classifier for the target learning task cannot achieve promising performance. In this work, we propose a novel unsupervised domain adaptation method called Feature Selection for Domain Adaptation (FSDA), in which we aim to select a set of informative features. The benefits are two-fold. The first is to reduce the mismatch between the data distributions in the source and target domains by selecting a set of informative features in which they share similar properties. The second is to remove noisy features in the source domain such that the learning performance can be enhanced. We formulate a new sparse learning model for structured multiple outputs, including a vector to select informative features that can be used to jointly minimize the domain discrepancy and eliminate noisy features, and a classifier to perform prediction on the selected features. We develop a cutting-plane algorithm to solve the resulting optimization problem. Extensive experiments on real-world data sets are tested to demonstrate the effectiveness of the proposed method compared with the other existing methods.
Regionwise Generative Adversarial Image Inpainting for Large Missing Areas

Recently, deep neural networks have achieved promising performance for in-filling large missing regions in image inpainting tasks. They have usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur, and other artifacts. Moreover, most inpainting approaches cannot handle well the case of a large contiguous missing area. To address these problems, we propose a generic inpainting framework capable of handling incomplete images with both contiguous and discontiguous large missing areas. We pose this in an adversarial manner, deploying regionwise operations in both the generator and discriminator to separately handle the different types of regions, namely, existing regions and missing ones. Moreover, a correlation loss is introduced to capture the nonlocal correlations between different patches, and thus, guide the generator to obtain more information during inference. With the help of regionwise generative adversarial mechanism, our framework can restore semantically reasonable and visually realistic images for both discontiguous and contiguous large missing areas. Extensive experiments on three widely used datasets for image inpainting task have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, on the large contiguous and discontiguous missing areas.
Stochastic Information Granules Extraction for Graph Embedding and Classification

Graphs are data structures able to efficiently describe real-world systems and, as such, have been extensively used in recent years by many branches of science, including machine learning engineering. However, the design of efficient graph-based pattern recognition systems is bottlenecked by the intrinsic problem of how to properly match two graphs. In this paper, we investigate a granular computing approach for the design of a general purpose graph-based classification system. The overall framework relies on the extraction of meaningful pivotal substructures on the top of which an embedding space can be build and in which the classification can be performed without limitations. Due to its importance, we address whether information can be preserved by performing stochastic extraction on the training data instead of performing an exhaustive extraction procedure which is likely to be unfeasible for large datasets. Tests on benchmark datasets show that stochastic extraction can lead to a meaningful set of pivotal substructures with a much lower memory footprint and overall computational burden, making the proposed strategies suitable also for dealing with big datasets.
A new hybrid-parameter recurrent neural network for online handwritten chinese character recognition

Bidirectional RNN (BRNN) is a common method of modeling time series and has been widely applied in many areas, including speech recognition, machine translation, natural language processing, scene text recognition. Compared with the unidirectional RNN, the bidirectional RNN usually obtains better performance but higher computational cost due to additional backward processing of an input sequence. In this paper, we present a new hybrid-parameter RNN which consists of two virtual unidirectional recurrent neural networks. The computational cost of the proposed RNN is only three-fourths of that of the bidirectional RNNs. In addition, we accumulate the feature vectors from different layers to obtain the output of the RNN system, which is an efficient way to combine all the feature vectors without increasing the model size. The experimental results on IAHCC-UCAS2016 dataset and ICDAR2013 competition database show that the hybrid-parameter RNN obtains a better recognition performance with lower computational cost, compared with the bidirectional RNN.
Evolving and Ensembling Deep CNN Architectures for Image Classification

Deep convolutional neural networks (CNNs) have traditionally been hand-designed owing to the complexity of their construction and the computational requirements of their training. Recently however, there has been an increase in research interest towards automatically designing deep CNNs for specific tasks. Ensembling has been shown to effectively increase the performance of deep CNNs, although usually with a duplication of work and therefore a large increase in computational resources required. In this paper we present Swarm Optimised Block Architecture Ensembles (SOBAE), a method for automatically designing and ensembling deep CNN models with a central weight repository to avoid work duplication. The models are trained and optimised together using particle swarm optimisation (PSO), with architecture convergence encouraged. At the conclusion of this combined process a base model nomination method is used to determine the best candidates for the ensemble. Two base model nomination methods are proposed, one using the local best particle positions from the PSO process, and one using the contents of the central weight repository. Once the base model pool has been created, the models inherit their parameters from the central weight repository and are then finetuned and ensembled in order to create a final system. We evaluate our system on the CIFAR-10 classification dataset and demonstrate improved results over the single global best model suggested by the optimisation process, with a minor increase in resources required by the finetuning process. Our system achieves an error rate of 4.27% on the CIFAR-10 image classification task with only 36 hours of combined optimisation and training on a single NVIDIA GTX 1080Ti GPU.
Simultaneous input and state estimation for stochastic nonlinear systems with additive unknown inputs

This paper investigates simultaneous input and state estimation for a class of nonlinear stochastic systems. We propose a recursive filter to concurrently estimate system states and unknown inputs. We show that the estimation errors of the proposed filter are Practically Exponentially Stable in probability, and the estimation error covariance matrices are uniformly bounded.
Harnessing 2D Networks and 3D Features for Automated Pancreas Segmentation from Volumetric CT Images

Segmenting pancreas from abdominal CT scans is an important prerequisite for pancreatic cancer diagnosis and precise treatment planning. However, automated pancreas segmentation faces challenges posed by shape and size variances, low contrast with regard to adjacent tissues and in particular negligibly small proportion to the whole abdominal volume. Current coarse-to-fine frameworks, either using tri-planar schemes or stacking 2D pre-segmentation as prior to 3D networks, have limitation on effectively capturing 3D information. While iterative updates on region of interest (ROI) in refinement stage alleviate accumulated errors caused by coarse segmentation, extra computational burden is introduced. In this paper, we harness 2D networks and 3D features to improve segmentation accuracy and efficiency. Firstly, in the 3D coarse segmentation network, a new bias-dice loss function is defined to increase ROI recall rates to improve efficiency by avoiding iterative ROI refinements. Secondly, for a full utilization of 3D information, dimension adaptation module (DAM) is introduced to bridge 2D networks and 3D information. Finally, a fusion decision module and parallel training strategy are proposed to fuse multi-source feature cues extracted from three sub-networks to make final predictions. The proposed method is evaluated in NIH dataset and outperforms the state-of-the-art methods in comparison, with mean Dice-Sørensen coefficient (DSC) of 85.22%, and with averagely 0.4 min for each instance.