Reviewerly/exHarmony huggingface dataset:
Deep Recurrent Quantization for Generating Sequential Binary Codes

Quantization has been an effective technology in ANN (approximate nearest neighbour) search due to its high accuracy and fast search speed. To meet the requirement of different applications, there is always a trade-off between retrieval accuracy and speed, reflected by variable code lengths. However, to encode the dataset into different code lengths, existing methods need to train several models, where each model can only produce a specific code length. This incurs a considerable training time cost, and largely reduces the flexibility of quantization methods to be deployed in real applications. To address this issue, we propose a Deep Recurrent Quantization (DRQ) architecture which can generate sequential binary codes. To the end, when the model is trained, a sequence of binary codes can be generated and the code length can be easily controlled by adjusting the number of recurrent iterations. A shared codebook and a scalar factor is designed to be the learnable weights in the deep recurrent quantization block, and the whole framework can be trained in an end-to-end manner. As far as we know, this is the first quantization method that can be trained once and generate sequential binary codes. Experimental results on the benchmark datasets show that our model achieves comparable or even better performance compared with the state-of-the-art for image retrieval. But it requires significantly less number of parameters and training times. Our code is published online: https://github.com/cfm-uestc/DRQ.
One Step Back, Two Steps Forward: Interference and Learning in Recurrent Neural Networks

Artificial neural networks, trained to perform cognitive tasks, have recently been used as models for neural recordings from animals performing these tasks. While some progress has been made in performing such comparisons, the evolution of network dynamics throughout learning remains unexplored. This is paralleled by an experimental focus on recording from trained animals, with few studies following neural activity throughout training. In this work, we address this gap in the realm of artificial networks by analyzing networks that are trained to perform memory and pattern generation tasks. The functional aspect of these tasks corresponds to dynamical objects in the fully trained network—a line attractor or a set of limit cycles for the two respective tasks. We use these dynamical objects as anchors to study the effect of learning on their emergence. We find that the sequential nature of learning—one trial at a time—has major consequences for the learning trajectory and its final outcome. Specifically, we show that least mean squares (LMS), a simple gradient descent suggested as a biologically plausible version of the FORCE algorithm, is constantly obstructed by forgetting, which is manifested as the destruction of dynamical objects from previous trials. The degree of interference is determined by the correlation between different trials. We show which specific ingredients of FORCE avoid this phenomenon. Overall, this difference results in convergence that is orders of magnitude slower for LMS. Learning implies accumulating information across multiple trials to form the overall concept of the task. Our results show that interference between trials can greatly affect learning in a learning-rule-dependent manner. These insights can help design experimental protocols that minimize such interference, and possibly infer underlying learning rules by observing behavior and neural activity throughout learning.
Reliable training and estimation of variance networks

We propose and investigate new complementary methodologies for estimating predictive variance networks in regression neural networks. We derive a locally aware mini-batching scheme that results in sparse robust gradients, and we show how to make unbiased weight updates to a variance network. Further, we formulate a heuristic for robustly fitting both the mean and variance networks post hoc. Finally, we take inspiration from posterior Gaussian processes and propose a network architecture with similar extrapolation properties to Gaussian processes. The proposed methodologies are complementary, and improve upon baseline methods individually. Experimentally, we investigate the impact of predictive uncertainty on multiple datasets and tasks ranging from regression, active learning and generative modeling. Experiments consistently show significant improvements in predictive uncertainty estimation over state-of-the-art methods across tasks and datasets.
Learn2Earn

In rural parts of the developing world, spreading awareness about critical issues in health, governance, and other topics is challenging and costly. Traditional media such as print, radio and TV each have limitations and offer little guarantee that new information is absorbed or retained by the target population. This paper describes Learn2Earn, a system that leverages mobile payments to bolster public awareness campaigns in rural India. Users call an Interactive Voice Response (IVR) system, listen to a brief audio tutorial, and take a multiple-choice quiz to check their understanding. People who pass the quiz receive a mobile top-up (about $0.14) and have the opportunity to earn additional credits by referring others to the system. We describe a pilot deployment of Learn2Earn in rural India that spread via word-of-mouth to over 15,000 people within seven weeks. Usage was concentrated among young men, many of them students. In a mixed-methods study, we draw upon call logs, electronic surveys, qualitative interviews, and other sources of data to suggest that Learn2Earn could be an effective way to build awareness about important topics.
The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

We describe the Second Multilingual Named Entity Challenge in Slavic languages.The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking.The Challenge was organized as part of the 7th Balto-Slavic Natural Language Processing Workshop, co-located with the ACL-2019 conference.Eight teams participated in the competition, which covered four languages and five entity types.Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge.Seven teams covered all four languages, and five teams participated in the cross-lingual entity linking task.Detailed evaluation information is available on the shared task web page.
Investigation of F0 Conditioning and Fully Convolutional Networks in Variational Autoencoder Based Voice Conversion

In this work, we investigate the effectiveness of two techniques for improving variational autoencoder (VAE) based voice conversion (VC). First, we reconsider the relationship between vocoder features extracted using the high quality vocoders adopted in conventional VC systems, and hypothesize that the spectral features are in fact F0 dependent. Such hypothesis implies that during the conversion phase, the latent codes and the converted features in VAE based VC are in fact source F0 dependent. To this end, we propose to utilize the F0 as an additional input of the decoder. The model can learn to disentangle the latent code from the F0 and thus generates converted F0 dependent converted features. Second, to better capture temporal dependencies of the spectral features and the F0 pattern, we replace the frame wise conversion structure in the original VAE based VC framework with a fully convolutional network structure. Our experiments demonstrate that the degree of disentanglement as well as the naturalness of the converted speech are indeed improved.
Discriminative Consistent Domain Generation for Semi-supervised Learning

Deep learning based task systems normally rely on a large amount of manually labeled training data, which is expensive to obtain and subject to operator variations. Moreover, it does not always hold that the manually labeled data and the unlabeled data are sitting in the same distribution. In this paper, we alleviate these problems by proposing a discriminative consistent domain generation (DCDG) approach to achieve a semi-supervised learning. The discriminative consistent domain is achieved by a double-sided domain adaptation. The double-sided domain adaptation aims to make a fusion of the feature spaces of labeled data and unlabeled data. In this way, we can fit the differences of various distributions between labeled data and unlabeled data. In order to keep the discriminativeness of generated consistent domain for the task learning, we apply an indirect learning for the double-sided domain adaptation. Based on the generated discriminative consistent domain, we can use the unlabeled data to learn the task model along with the labeled data via a consistent image generation. We demonstrate the performance of our proposed DCDG on the late gadolinium enhancement cardiac MRI (LGE-CMRI) images acquired from patients with atrial fibrillation in two clinical centers for the segmentation of the left atrium anatomy (LA) and proximal pulmonary veins (PVs). The experiments show that our semi-supervised approach achieves compelling segmentation results, which can prove the robustness of DCDG for the semi-supervised learning using the unlabeled data along with labeled data acquired from a single center or multicenter studies.
Unpaired Multi-contrast MR Image Synthesis Using Generative Adversarial Networks

Magnetic Resonance Imaging (MRI) has been established as an important diagnostic tool for research and clinical purposes. Multi-contrast scans can enhance the accuracy for many deep learning algorithms. However, these scans may not be available in some situations. Thus, it is valuable to synthetically generate non-existent contrasts from the available one. Existing methods based on Generative Adversarial Networks (GANs) lack the freedom to map one image to multiple contrasts using only a single generator and discriminator, hence, requiring training of multiple models for multi-contrast MR synthesis. We present a novel method for multi-contrast MR image synthesis with unpaired data using GANs. Our method leverages the strength of Star-GAN to translate a given image to n contrasts using a single generator and discriminator. We also introduce a new generation loss function, which enforces the generator to produce high-quality images which are perceptually closer to the real ones and exhibit high structural similarity as well. We experiment on IXI dataset to learn all possible mappings among T $$_1$$ -weighted, T $$_2$$ -weighted, Proton Density (PD) weighted and Magnetic Resonance Angiography (MRA) images. Qualitative and quantitative comparison against baseline method shows the superiority of our approach.
Hyperparameter Importance for Image Classification by Residual Neural Networks

Residual neural networks (ResNets) are among the state-of-the-art for image classification tasks. With the advent of automated machine learning (AutoML), automated hyperparameter optimization methods are by now routinely used for tuning various network types. However, in the thriving field of deep neural networks, this progress is not yet matched by equal progress on rigorous techniques that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following question: Given a residual neural network architecture, what are generally (across datasets) its most important hyperparameters? In order to answer this question, we assembled a benchmark suite containing 10 image classification datasets. For each of these datasets, we analyze which of the hyperparameters were most influential using the functional ANOVA framework. This experiment both confirmed expected patterns, and revealed new insights. With these experimental results, we aim to form a more rigorous basis for experimentation that leads to better insight towards what hyperparameters are important to make neural networks perform well.
"The Most Trustworthy Coin"

Bitcoin is an innovative technological network, a new, non-governmental currency, and a worldwide group of users. In other words, Bitcoin is a complex sociotechnical system with a complex set of risks and challenges for anyone using it. We investigated how everyday users of Bitcoin develop trust in Bitcoin on one of the largest online communities devoted to Bitcoin: the Reddit.com r/bitcoin forum. Using qualitative content analysis, we examined how trust in Bitcoin develops based on contributions to this community. On r/bitcoin, trust in Bitcoin is driven by a pervasive ideology we call the "True Bitcoiner" ideology. This ideological viewpoint in centered on the interpretation of Bitcoin as functionally "trustless" and risk-free. Despite widespread evidence of emerging individual and system-level risks with using Bitcoin, participants continue to maintain this ideological perspective. This ideology consists of three primary beliefs: viewing Bitcoin's technology as more trustworthy than its people; rejecting 'corrupt' social hierarchies related to money; and the importance of accumulating or 'HODLing' quantities of Bitcoin as a strategy to create an ideal future. We conclude that this "True Bitcoiner" ideology is maintained despite contradictory evidence in the world because it allows participants to more easily interpret Bitcoin and make decisions by reducing perceived risk and uncertainty in the system. The role of this ideology on r/bitcoin demonstrates an expanded conceptualization of how trust is created and socially-mediated in socio-technical contexts.
Building trans-national and multi-disciplinary academic curricula through adaptation of a project-based approach

Four pillars of learning identified in the UNESCO report in 1996 include four “L”s: Learning to know, Learning to do, Learning to live together, and Learning to be. Since then educators have been discussing how four pillars of learning can affect 21st century’s curriculum. Evidently, space-related education is based on traditional type curriculum. Therefore, integration of new approaches in aerospace engineering and science curriculum is often challenging. This paper argues that transdisciplinary and project-based approach can be used in building innovative aerospace engineering and other space-related disciplines core curricula. The paper proposes using SDTP/MSIP projects as foundation for creating multi-disciplinary curricula in different universities and countries. The proposed methodology includes pilot team projects performed during two-week summer workshop, task diversification between participating universities, comprehensive individual studies (analysis), and joint findings reports (synthesis). Such approach is proposed to be tested during SDTP-2017 and in 2017-2018 academic year in Russia (MGTU) and USA (University of Houston). The paper concludes with an analysis of potential opportunities and complications for the development and integration of presented academic strategy to increase adaptability of diverse hands-on experiences into already established educational programs in Russia and the US. In a summary, a platform for further inquiry into multi-disciplinary academic strategies is discussed for more in-depth investigation in the future.
Life Transitions and Social Technologies

When people experience major changes in their lives (e.g., relationship changes, transition from high school to college, realizing an LGBTQ identity, etc.), they often turn to social technologies to help navigate shifting identities and networks and find support and resources. People's experiences using social technologies during times of life transition, and how to better design such technologies, has been a major focus of social computing research. This workshop will gather researchers working in this space to discuss eight themes: life events vs. processes; changing identities; multiple overlapping life events; physical and digital transitions; technology non-use during life transitions; liminality framework; theoretical frames; and methodological considerations. Collaboratively, we will 1) synergize insights from workshop organizers' and participants' research to determine how social technologies can be designed to better support people during life transitions and 2) outline an agenda for the future of social computing work on life transitions.
Robust-ODAL: Learning from heterogeneous health systems without sharing patient-level data

Electronic Health Records (EHR) contain extensive patient data on various health outcomes and risk predictors, providing an efficient and wide-reaching source for health research. Integrated EHR data can provide a larger sample size of the population to improve estimation and prediction accuracy. To overcome the obstacle of sharing patient-level data, distributed algorithms were developed to conduct statistical analyses across multiple clinical sites through sharing only aggregated information. However, the heterogeneity of data across sites is often ignored by existing distributed algorithms, which leads to substantial bias when studying the association between the outcomes and exposures. In this study, we propose a privacy-preserving and communication-efficient distributed algorithm which accounts for the heterogeneity caused by a small number of the clinical sites. We evaluated our algorithm through a systematic simulation study motivated by real-world scenarios and applied our algorithm to multiple claims datasets from the Observational Health Data Sciences and Informatics (OHDSI) network. The results showed that the proposed method performed better than the existing distributed algorithm ODAL and a meta-analysis method.
Features of the use of cloud-based translation systems in the process of forming information competence of translators

The current trends in the translator training are shown, which reflect the orientation towards the use of cloud-based automated translation systems. The possibilities of studying cloud-based translation systems in the educational process of training the translator are considered. The role of mastering modern translation tools for forming information competence of translators, particularly technological component, was described. The definition of the list and type of basic translation tools that should be mastered in the studying process was discussed. These tools should include automated translation systems and terminological management systems. It is advisable to provide for the study of both desktop and cloud-based systems. The inclusion in the content of the training translators the study of cloud-based systems of automated translation after desktop systems is proposed. A number of advantages of cloud-based translation systems for the use in the process of training the translators is defined and substantiated. A comparative analysis of the functional of cloud-based automated translation systems (Wordfast Anywhere, XTM Cloud, and MemSource) with the aim of including them in the content of the training program for translators has been carried out.
Data-Independent Neural Pruning via Coresets

Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy. Model compression became a central research topic, as it is crucial for deployment of neural networks on devices with limited computational and memory resources. The majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample. We propose the first efficient, data-independent neural pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample. Our method is based on the coreset framework, which finds a small weighted subset of points that provably approximates the original inputs. Specifically, we approximate the output of a layer of neurons by a coreset of neurons in the previous layer and discard the rest. We apply this framework in a layer-by-layer fashion from the top to the bottom. Unlike previous works, our coreset is data independent, meaning that it provably guarantees the accuracy of the function for any input $x\in \mathbb{R}^d$, including an adversarial one. We demonstrate the effectiveness of our method on popular network architectures. In particular, our coresets yield 90% compression of the LeNet-300-100 architecture on MNIST while improving the accuracy.
Underwater surveillance in non-Gaussian noisy environment

The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly throughout the process and (b) repeatedly at some particular time samples. The efficiency of these filters with the increase in non-Gaussian noise samples is discussed in this paper. The performance of filters is compared with that of Cramer-Rao Lower Bound. Doppler-bearing extended Kalman filter and Doppler-bearing unscented Kalman filter are chosen for this work.
Comparing Research Performance of Private Universities in India with IITs, Central Universities and NITs

During the last two decades the number of private universities in India has increased significantly.According to AISHE report of 2016, out of 799 universities in India, 277 are private universities, i.e. one out of every three universities in India is a private university.A significant proportion of colleges (about 78%) are also privately managed, as they do not contribute much to research activities and hence are not included in this analysis.Private universities are now becoming a major component of the Indian higher education system.Some of the private universities are exclusively positioning and projecting themselves as universities for high quality research and innovation.A few of them are now well placed in the national-level NIRF ranking framework.It is in this context that this paper presents a comparative account of research performance of the 25 most productive private universities with the set of Indian Institutes of Technology (IITs), Central Universities (CUs) and National Institutes of Technology (NITs), all of which have a well-established environment and culture of research.A set-based comparison methodology is followed.The results show good performance of private universities in research, especially in terms of output and rate of growth of output.However, on quality and productivity per capita and per rupee spent, they have a long way to go to match the performance levels of well-established centrally funded higher education institutions of India.This study presents detailed scientometric assessment of some most productive private universities in India.