Reviewerly/exHarmony huggingface dataset:
Facilitators, best practices and barriers to integrating family planning data in Uganda’s health management information system

Health management information systems (HMIS) are instrumental in addressing health delivery problems and strengthening health sectors by generating credible evidence about the health status of clients. There is paucity of studies which have explored possibilities for integrating family planning data from the public and private health sectors in Uganda’s national HMIS. This study sought to investigate the facilitators, best practices and barriers of integrating family planning data into the district and national HMIS in Uganda. We conducted a qualitative study in Kampala, Jinja, and Hoima Districts of Uganda, based on 16 key informant interviews and a multi-stakeholder dialogue workshop with 11 participants. Deductive and inductive thematic methods were used to analyze the data. The technical facilitators of integrating family planning data from public and private facilities in the national and district HMIS were user-friendly software; web-based and integrated reporting; and availability of resources, including computers. Organizational facilitators included prioritizing family planning data; training staff; supportive supervision; and quarterly performance review meetings. Key behavioral facilitators were motivation and competence of staff. Collaborative networks with implementing partners were also found to be essential for improving performance and sustainability. Significant technical barriers included limited supply of computers in lower level health facilities, complex forms, double and therefore tedious entry of data, and web-reporting challenges. Organizational barriers included limited human resources; high levels of staff attrition in private facilities; inadequate training in data collection and use; poor culture of information use; and frequent stock outs of paper-based forms. Behavioral barriers were low use of family planning data for planning purposes by district and health facility staff. Family planning data collection and reporting are integrated in Uganda’s district and national HMIS. Best practices included integrated reporting and performance review, among others. Limited priority and attention is given to family planning data collection at the facility and national levels. Data are not used by the health facilities that collect them. We recommend reviewing and tailoring data collection forms and ensuring their availability at health facilities. All staff involved in data reporting should be trained and regularly supervised.
Heavy metal pollution at mine sites estimated from reflectance spectroscopy following correction for skewed data

The heavy metal concentration of soil samples often exhibits a skewed distribution, especially for soil samples from mining areas with an extremely high concentration of heavy metals. In this study, to model soil contamination in mining areas using reflectance spectroscopy, the skewed distribution was corrected and heavy metal concentration estimated. In total, 46 soil samples from a mining area, along with corresponding field soil spectra, were collected. Laboratory spectra of the soil samples and the field spectra were used to estimate copper (Cu) concentration in the mining area. A logarithmic transformation was used to correct the skewed distribution, and based on the sorption of Cu on spectrally active soil constituents, the spectral bands associated with iron oxides were extracted from the visible and near-infrared (VNIR) region and used in the estimation. A genetic algorithm was adopted for band selection, and partial least squares regression was used to calibrate the estimation model. After transforming the distribution of Cu concentration, the accuracies (R2) of the estimation of Cu concentration using laboratory and field spectra separately were 0.94 and 0.96. The results indicate that Cu concentration in the mining area can be estimated using reflectance spectroscopy following correction of skewed distribution.
Using the MetaMask Chrome Extension

In the previous chapter, you learned how to create your own private Ethereum test network so that you can try out the various Ethereum transactions, such as transferring Ethers to different accounts and performing mining. You also learned how to create accounts so that you can hold your own Ethers. In this chapter, you will learn how to use a Chrome extension known as the MetaMask. The MetaMask Chrome extension is an Ethereum wallet that allows you to hold your Ethereum account, and it will be an essential tool to help you develop and test Smart Contracts in the next few chapters.
Quantum Blockchain: A Decentralized, Encrypted and Distributed Database Based on Quantum Mechanics

Quantum blockchain can be understood as a decentralized, encrypted and distributed database based on quantum computation and quantum information theory. Once the data is recorded in the quantum blockchain, it will not be maliciously tampered with. In recent years, the development of quantum computation and quantum information theory makes more and more researchers focus on the research of quantum blockchain. In this paper, we review the developments in the field of quantum blockchain, and briefly analyze its advantages compared with the classical blockchain. The construction and the framework of the quantum blockchain are introduced. Then we introduce the method of applying quantum technology to a certain part of the general blockchain. In addition, the advantages of quantum blockchain compared with classical blockchain and its development prospects are summarized.
PAN: Projective Adversarial Network for Medical Image Segmentation

Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D semantics in an effective yet computationally efficient way remains an open problem. In this study, we address this computational burden by proposing a novel projective adversarial network, called PAN, which incorporates high-level 3D information through 2D projections. Furthermore, we introduce an attention module into our framework that helps for a selective integration of global information directly from our segmentor to our adversarial network. For the clinical application we chose pancreas segmentation from CT scans. Our proposed framework achieved state-of-the-art performance without adding to the complexity of the segmentor.
Methodology for the Implementation of Virtual Assistants for Education Using Google Dialogflow

We developed a virtual assistant that enables students to access interactive content adapted for an introductory undergraduate course on artificial intelligence. This chatbot is able to show answers to frequently asked questions in a hierarchical structured manner, leading students by either voice, text or tactile input to the content that better solves their questions and doubts. It was developed using Google Dialogflow as a simple way to generate and train a natural language model. Another convenience of this platform is its ability to collect usage data that is potentially useful for lecturers as learning indicators. The main purpose of this paper is to outline the methodology that guided our implementation so that it can be reproduced in different educational contexts and study chatbots as tools for learning. At the moment, several articles, news and blogs are writing about the potential, implementation and impact chatbots have in general contexts, however there is little to no literature proposing a methodology to reproduce them for educational purposes. In that respect, we developed four main categories as a generic structure of course content and focused on quick implementation, easy updating and generalization. The final product received a general approbation of the students due to its accessibility and well structured data.
Improving Local Trajectory Optimisation using Probabilistic Movement Primitives

Local trajectory optimisation techniques are a powerful tool for motion planning. However, they often get stuck in local optima depending on the quality of the initial solution and consequently, often do not find a valid (i.e. collision free) trajectory. Moreover, they often require fine tuning of a cost function to obtain the desired motions. In this paper, we address both problems by combining local trajectory optimisation with learning from demonstrations. The human expert demonstrates how to reach different target end-effector locations in different ways. From these demonstrations, we estimate a trajectory distribution, represented by a Probabilistic Movement Primitive (ProMP). For a new target location, we sample different trajectories from the ProMP and use these trajectories as initial solutions for the local optimisation. As the ProMP generates versatile initial solutions for the optimisation, the chance of finding poor local minima is significantly reduced. Moreover, the learned trajectory distribution is used to specify the smoothness costs for the optimisation, resulting in solutions of similar shape as the demonstrations. We demonstrate the effectiveness of our approach in several complex obstacle avoidance scenarios.
Is useful research data usually shared? An investigation of genome-wide association study summary statistics

Primary data collected during a research study is often shared and may be reused for new studies. To assess the extent of data sharing in favourable circumstances and whether data sharing checks can be automated, this article investigates summary statistics from primary human genome-wide association studies (GWAS). This type of data is highly suitable for sharing because it is a standard research output, is straightforward to use in future studies (e.g., for secondary analysis), and may be already stored in a standard format for internal sharing within multi-site research projects. Manual checks of 1799 articles from 2010 and 2017 matching a simple PubMed query for molecular epidemiology GWAS were used to identify 314 primary human GWAS papers. Of these, only 13% reported the location of a complete set of GWAS summary data, increasing from 3% in 2010 to 23% in 2017. Whilst information about whether data was shared was typically located clearly within a data availability statement, the exact nature of the shared data was usually unspecified. Thus, data sharing is the exception even in suitable research fields with relatively strong data sharing norms. Moreover, the lack of clear data descriptions within data sharing statements greatly complicates the task of automatically characterising shared data sets.
Distilling the Knowledge of BERT for Sequence-to-Sequence ASR

Attention-based sequence-to-sequence (seq2seq) models have achieved promising results in automatic speech recognition (ASR). However, as these models decode in a left-to-right way, they do not have access to context on the right. We leverage both left and right context by applying BERT as an external language model to seq2seq ASR through knowledge distillation. In our proposed method, BERT generates soft labels to guide the training of seq2seq ASR. Furthermore, we leverage context beyond the current utterance as input to BERT. Experimental evaluations show that our method significantly improves the ASR performance from the seq2seq baseline on the Corpus of Spontaneous Japanese (CSJ). Knowledge distillation from BERT outperforms that from a transformer LM that only looks at left context. We also show the effectiveness of leveraging context beyond the current utterance. Our method outperforms other LM application approaches such as n-best rescoring and shallow fusion, while it does not require extra inference cost.
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets

In this paper we propose a novel data augmentation approach where guided outputs of a language generation model, e.g.GPT-2, when labeled, can improve the performance of text classifiers through an active learning process.We transform the data generation task into an optimization problem which maximizes the usefulness of the generated output, using Monte Carlo Tree Search (MCTS) as the optimization strategy and incorporating entropy as one of the optimization criteria.We test our approach against a Non-Guided Data Generation (NGDG) process that does not optimize for a reward function.Starting with a small set of data, our results show an increased performance with MCTS of 26% on the TREC-6 Questions dataset, and 10% on the Stanford Sentiment Treebank SST-2 dataset.Compared with NGDG, we are able to achieve increases of 3% and 5% on TREC-6 and SST-2.
A Memory-Efficient Encoding Method for Processing Mixed-Type Data on Machine Learning

The most common machine-learning methods solve supervised and unsupervised problems based on datasets where the problem’s features belong to a numerical space. However, many problems often include data where numerical and categorical data coexist, which represents a challenge to manage them. To transform categorical data into a numeric form, preprocessing tasks are compulsory. Methods such as one-hot and feature-hashing have been the most widely used encoding approaches at the expense of a significant increase in the dimensionality of the dataset. This effect introduces unexpected challenges to deal with the overabundance of variables and/or noisy data. In this regard, in this paper we propose a novel encoding approach that maps mixed-type data into an information space using Shannon’s Theory to model the amount of information contained in the original data. We evaluated our proposal with ten mixed-type datasets from the UCI repository and two datasets representing real-world problems obtaining promising results. For demonstrating the performance of our proposal, this was applied for preparing these datasets for classification, regression, and clustering tasks. We demonstrate that our encoding proposal is remarkably superior to one-hot and feature-hashing encoding in terms of memory efficiency. Our proposal can preserve the information conveyed by the original data.
LF-GDPR: A Framework for Estimating Graph Metrics With Local Differential Privacy

Local differential privacy (LDP) is an emerging technique for privacy-preserving data collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be easily applied to real-world graph analysis tasks such as community detection and centrality analysis due to its high implementation complexity and low data utility. In this paper, we address these two issues by presenting LF-GDPR, the first LDP-enabled graph metric estimation framework for graph analysis. It collects two atomic graph metrics—the adjacency bit vector and node degree—from each node locally. LF-GDPR simplifies the job of implementing LDP-related steps (e.g., local perturbation, aggregation and calibration) for a graph metric estimation task by providing either a complete or a parameterized algorithm for each step. To address low data utility of LDP, it optimally allocates privacy budget between the two atomic metrics during data collection. To demonstrate the usage of LF-GDPR, we show use cases on two common graph analysis tasks, namely, clustering coefficient estimation and community detection. The privacy and utility achieved by LF-GDPR are verified through theoretical analysis and extensive experimental results.
Nearest Neighbor Machine Translation

We introduce k-nearest-neighbor machine translation (kNN-MT), which predicts tokens with a nearest-neighbor classifier over a large datastore of cached examples, using representations from a neural translation model for similarity search. This approach requires no additional training and scales to give the decoder direct access to billions of examples at test time, resulting in a highly expressive model that consistently improves performance across many settings. Simply adding nearest-neighbor search improves a state-of-the-art German-English translation model by 1.5 BLEU. kNN-MT allows a single model to be adapted to diverse domains by using a domain-specific datastore, improving results by an average of 9.2 BLEU over zero-shot transfer, and achieving new state-of-the-art results---without training on these domains. A massively multilingual model can also be specialized for particular language pairs, with improvements of 3 BLEU for translating from English into German and Chinese. Qualitatively, kNN-MT is easily interpretable; it combines source and target context to retrieve highly relevant examples.
P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees

With the growth of participating clients, the centralized parameter server (PS) will seriously limit the scale and efficiency of Federated Learning (FL). A straightforward approach to scale up the FL system is to construct a Parallel FL (PFL) system with multiple PSes. However, it is unclear whether PFL can really achieve a faster convergence rate or not. Even if the answer is yes, it is non-trivial to design a highly efficient parameter average algorithm for a PFL system. In this paper, we propose a completely parallelizable FL algorithm called P-FedAvg under the PFL architecture. P-FedAvg extends the well-known FedAvg algorithm by allowing multiple PSes to cooperate and train a learning model together. In P-FedAvg, each PS is only responsible for a fraction of total clients, but PSes can mix model parameters in a dedicatedly designed way so that the FL model can well converge. Different from heuristic-based algorithms, P-FedAvg is with theoretical guarantees. To be rigorous, we conduct theoretical analysis on the convergence rate of P-FedAvg, and derive the optimal weights for each PS to mix parameters with its neighbors. We also examine how the overlay topology formed by PSes affects the convergence rate and robustness of a PFL system. Lastly, we perform extensive experiments with real datasets to verify our analysis and demonstrate that P-FedAvg can significantly improve convergence rates than traditional FedAvg and other competitive baselines. We believe that our work can help to lay a theoretical foundation for building more efficient PFL systems.
MedPath: Augmenting Health Risk Prediction via Medical Knowledge Paths

The broad adoption of electronic health records (EHR) data and the availability of biomedical knowledge graphs (KGs) on the web have provided clinicians and researchers unprecedented resources and opportunities for conducting health risk predictions to improve healthcare quality and medical resource allocation. Existing methods have focused on improving the EHR feature representations using attention mechanisms, time-aware models, or external knowledge. However, they ignore the importance of using personalized information to make predictions. Besides, the reliability of their prediction interpretations needs to be improved since their interpretable attention scores are not explicitly reasoned from disease progression paths. In this paper, we propose MedPath to solve these challenges and augment existing risk prediction models with the ability to use personalized information and provide reliable interpretations inferring from disease progression paths. Firstly, MedPath extracts personalized knowledge graphs (PKGs) containing all possible disease progression paths from observed symptoms to target diseases from a large-scale online medical knowledge graph. Next, to augment existing EHR encoders for achieving better predictions, MedPath learns a PKG embedding by conducting multi-hop message passing from symptom nodes to target disease nodes through a graph neural network encoder. Since MedPath reasons disease progression by paths existing in PKGs, it can provide explicit explanations for the prediction by pointing out how observed symptoms can finally lead to target diseases. Experimental results on three real-world medical datasets show that MedPath is effective in improving the performance of eight state-of-the-art methods with higher F1 scores and AUCs. Our case study also demonstrates that MedPath can greatly improve the explicitness of the risk prediction interpretation.1
The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

An adaptive genetic algorithm based on collision detection (AGACD) is proposed to solve the problems of the basic genetic algorithm in the field of path planning, such as low convergence path quality, many iterations required for convergence, and easily falling into the local optimal solution. First, this paper introduces the Delphi weight method to evaluate the weight of path length, path smoothness, and path safety in the fitness function, and a collision detection method is proposed to detect whether the planned path collides with obstacles. Then, the population initialization process is improved to reduce the program running time. After comprehensively considering the population diversity and the number of algorithm iterations, the traditional crossover operator and mutation operator are improved, and the adaptive crossover operator and adaptive mutation operator are proposed to avoid the local optimal solution. Finally, an optimization operator is proposed to improve the quality of convergent individuals through the second optimization of convergent individuals. The simulation results show that the adaptive genetic algorithm based on collision detection is not only suitable for simulation maps with various sizes and obstacle distributions but also has excellent performance, such as greatly reducing the running time of the algorithm program, and the adaptive genetic algorithm based on collision detection can effectively solve the problems of the basic genetic algorithm.