Reviewerly/exHarmony huggingface dataset:
Using augmented reality with speech input for non-native children's language learning

Augmented Reality (AR) offers an enhanced learning environment which could potentially influence children's experience and knowledge gain during the language learning process. Teaching English or other foreign languages to children with different native language can be difficult and requires an effective strategy to avoid boredom and detachment from the learning activities. With the growing numbers of AR education applications and the increasing pervasiveness of speech recognition, we are keen to understand how these technologies benefit non-native young children in learning English. In this paper, we explore children's experience in terms of knowledge gain and enjoyment when learning through a combination of AR and speech recognition technologies. We developed a prototype AR interface called TeachAR, and ran two experiments to investigate how effective the combination of AR and speech recognition was towards the learning of 1) English terms for color and shapes, and 2) English words for spatial relationships. We found encouraging results by creating a novel teaching strategy using these two technologies, not only in terms of increase in knowledge gain and enjoyment when compared with traditional strategy but also enables young children to finish the certain task faster and easier.
Optimizing Millions of Hyperparameters by Implicit Differentiation

We propose an algorithm for inexpensive gradient-based hyperparameter optimization that combines the implicit function theorem (IFT) with efficient inverse Hessian approximations. We present results about the relationship between the IFT and differentiating through optimization, motivating our algorithm. We use the proposed approach to train modern network architectures with millions of weights and millions of hyper-parameters. For example, we learn a data-augmentation network - where every weight is a hyperparameter tuned for validation performance - outputting augmented training examples. Jointly tuning weights and hyperparameters with our approach is only a few times more costly in memory and compute than standard training.
Blockchain: A new safeguard for agri-foods

Blockchain implementation in agriculture has begun. Blockchain is recognized as an emerging technology in the agri-foods industry which may provide an efficient and robust mechanism for enhancing food traceability and a transparent and reliable way to validate quality, safety, and sustainability, of agri-foods. However, the technology is in its nascency, therefore, this review was written to foster discussion and encourage the application of blockchain technology, especially in the agri-food industry. In this review, the working principle of blockchain for data recording and tracking is briefly described. The collaboration models for the current blockchain applications on agri-foods are summarized. Furthermore, the specific utilization of blockchain to enhance safety and quality of agri-foods is discussed in four aspects: enhance the data transparency, realize data traceability, improve the food safety and quality monitoring, and reduce the cost of financial transactions. A case study on a Walmart pork traceability system has been provided to demonstrate how blockchain may be used to enhance the food traceability. Finally, challenges and future trends of blockchain technology in agri-foods concerning data/cost management, data security, and data integration are discussed. Blockchain technology reveals a promising approach to foster a future of agri-foods system in a way that is safer, healthier, more sustainable, and reliable.
Cost-effective IoT devices as trustworthy data sources for a blockchain-based water management system in precision agriculture

This paper explores how the energy-efficient integration of IoT-based sensing and blockchains, an innovation in the field of Digital Infrastructure technologies, can be used to incentivize virtuous behaviors in agricultural practices. The novelty of the study lies specifically in the unprecedented use of constrained sensing devices as trustworthy data sources for a permissionless blockchain. Furthermore, we show how our research results, advancing the State-of-the-Art in the IoT and blockchain interactions, can support the interests of a diverse set of water management stakeholders in a concrete use-case implementation. To assess our contribution and validate our results we use a system architecture comprising constrained IoT devices for measuring water consumption used as direct data-source actors, a public blockchain infrastructure, and smart contracts that represent the interests of different water management stakeholders and regulate the distribution of incentives amongst virtuous farmers. Further validation on the usability of our results is obtained through the real implementation of a complete use case featuring the Ethereum network as a public blockchain and where six different types of IoT platforms are individually assessed for impact on the IoT devices, in terms of energy, processing time, and available memory. The findings show how solutions based on the proposed architecture can be implemented with only 6% of additional energy budget compared to the normal operations of the IoT devices. Besides showing new means to energy-efficiently integrate IoT data sources in a permissionless blockchain, the validation results make our contribution a strong candidate for use in automated and incentive-based irrigation water management systems as well as a key component in fostering increased sustainability of the whole agricultural sector.
Universal Fast-Flux Control of a Coherent, Low-Frequency Qubit

The \textit{heavy-fluxonium} circuit is a promising building block for superconducting quantum processors due to its long relaxation and dephasing time at the half-flux frustration point. However, the suppressed charge matrix elements and low transition frequency have made it challenging to perform fast single-qubit gates using standard protocols. We report on new protocols for reset, fast coherent control, and readout, that allow high-quality operation of the qubit with a 14 MHz transition frequency, an order of magnitude lower in energy than the ambient thermal energy scale. We utilize higher levels of the fluxonium to initialize the qubit with $97$\% fidelity, corresponding to cooling it to $190~\mathrm{\mu K}$. We realize high-fidelity control using a universal set of single-cycle flux gates, which are comprised of directly synthesizable fast pulses, while plasmon-assisted readout is used for measurements. On a qubit with $T_1, T_{2e}\sim$~300~$\mathrm{\mu s}$, we realize single-qubit gates in $20-60$~ns with an average gate fidelity of $99.8\%$ as characterized by randomized benchmarking.
Trade openness, FDI, and income inequality: Evidence from sub‐Saharan Africa

Abstract The motivation for this study stems from the United Nations Sustainable Development Goals (UN‐SDGs) and their impact by 2030. The UN highlights 17 SDGs that address pertinent local and global issues, one of which—SDG‐10—has been devoted to reducing inequality. This study investigates the nexus between trade openness, foreign direct investment (FDI), and income inequality in sub‐Saharan Africa using panel data from 2000 to 2015 and the generalized method of moment (GMM) technique approach. The findings show that FDI and income have a negative, statistically significant relationship with income inequality, signifying that as FDI and income per capita increase, the level of income inequality decreases. However, trade openness, education, political stability, corruption, and rule of law have a positive, statistically significant relationship with inequality. This study, therefore, offers some recommendations that will help policymakers. First, develop good policies to attract more foreign investors, which will contribute to creating employment opportunities in the region. Second, create more infrastructures to provide good quality education. Third, implement a good policy to motivate local production which will contribute to creating jobs. Fourth, build a strong institution(s) to fight against corruption.
COVID-19 pandemic improves market signals of cryptocurrencies–evidence from Bitcoin, Bitcoin Cash, Ethereum, and Litecoin

The COVID-19 global pandemic has disrupted business-as-usual, hence, affecting sustained economic development across countries. However, it appears economic uncertainty following COVID-19 containment measures favor market signals of cryptocurrencies. Here, this study empirically and structurally investigates the implication of COVID-19 health outcomes on market prices of Bitcoin, Bitcoin Cash, Ethereum, and Litecoin. Evidence from the novel Romano-Wolf multiple hypotheses reveal COVID-19 shocks spur Litecoin by 3.20-3.84%, Bitcoin by 2.71-3.27%, Ethereum by 1.43-1.75%, and Bitcoin Cash by 1.34-1.62%.
Development of machine learning model for diagnostic disease prediction based on laboratory tests

Abstract The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.
Entangled photon-pair sources based on three-wave mixing in bulk crystals

Entangled photon pairs are a critical resource in quantum communication protocols ranging from quantum key distribution to teleportation. The current workhorse technique for producing photon pairs is via spontaneous parametric down conversion (SPDC) in bulk nonlinear crystals. The increased prominence of quantum networks has led to a growing interest in deployable high performance entangled photon-pair sources. This manuscript provides a review of the state-of-the-art bulk-optics-based SPDC sources with continuous wave pump and discusses some of the main considerations when building for deployment.
Word Alignment by Fine-tuning Embeddings on Parallel Corpora

Word alignment over parallel corpora has a wide variety of applications, including learning translation lexicons, cross-lingual transfer of language processing tools, and automatic evaluation or analysis of translation outputs.The great majority of past work on word alignment has worked by performing unsupervised learning on parallel text.Recently, however, other work has demonstrated that pre-trained contextualized word embeddings derived from multilingually trained language models (LMs) prove an attractive alternative, achieving competitive results on the word alignment task even in the absence of explicit training on parallel data.In this paper, we examine methods to marry the two approaches: leveraging pre-trained LMs but finetuning them on parallel text with objectives designed to improve alignment quality, and proposing methods to effectively extract alignments from these fine-tuned models.We perform experiments on five language pairs and demonstrate that our model can consistently outperform previous state-of-the-art models of all varieties.In addition, we demonstrate that we are able to train multilingual word aligners that can obtain robust performance on different language pairs.Our aligner, AWE-SOME (Aligning Word Embedding Spaces Of Multilingual Encoders), with pre-trained models is available at https://github.com/neulab/awesome-align.
Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation

Acquiring sufficient ground-truth supervision to train deep vi- sual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated in some structured prediction tasks, such as semantic segmen- tation, which requires pixel-level annotations. This work ad- dresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level anno- tations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models se- mantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models. In particular, we devise a graph neural network (GNN) for group-wise semantic min- ing, wherein input images are represented as graph nodes, and the underlying relations between a pair of images are char- acterized by an efficient co-attention mechanism. Moreover, in order to prevent the model from paying excessive atten- tion to common semantics only, we further propose a graph dropout layer, encouraging the model to learn more accurate and complete object responses. The whole network is end-to- end trainable by iterative message passing, which propagates interaction cues over the images to progressively improve the performance. We conduct experiments on the popular PAS- CAL VOC 2012 and COCO benchmarks, and our model yields state-of-the-art performance. Our code is available at: https://github.com/Lixy1997/Group-WSSS.
GWO-Based Optimal Tuning of Type-1 and Type-2 Fuzzy Controllers for Electromagnetic Actuated Clutch Systems

The goal of this paper is to develop the optimal fuzzy controllers for nonlinear processed represented by electromagnetic actuated clutch systems in order to improve the control system performance. A Takagi-Sugeno type-1 fuzzy logic controller and a Takagi-Sugeno type-2 fuzzy logic controller are designed here to enhance position tracking performance of electromagnetic actuated clutch systems, which are subjected to parametric uncertainties. The parameters of membership functions and the rules of the proposed fuzzy controllers are optimally tuned using a recent metaheuristic optimization algorithm, namely the Grey Wolf Optimizer. A comparison of Takagi-Sugeno type-1 and Takagi-Sugeno type-2 fuzzy control systems is carried out in various simulation scenarios, which are discussed and analyzed. The results highlight that the type-2 fuzzy controller performs efficiently for these complex processes.
Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review

Missing data is a common problem during the development, evaluation, and implementation of prediction models. Although machine learning (ML) methods are often said to be capable of circumventing missing data, it is unclear how these methods are used in medical research. We aim to find out if and how well prediction model studies using machine learning report on their handling of missing data.We systematically searched the literature on published papers between 2018 and 2019 about primary studies developing and/or validating clinical prediction models using any supervised ML methodology across medical fields. From the retrieved studies information about the amount and nature (e.g. missing completely at random, potential reasons for missingness) of missing data and the way they were handled were extracted.We identified 152 machine learning-based clinical prediction model studies. A substantial amount of these 152 papers did not report anything on missing data (n = 56/152). A majority (n = 96/152) reported details on the handling of missing data (e.g., methods used), though many of these (n = 46/96) did not report the amount of the missingness in the data. In these 96 papers the authors only sometimes reported possible reasons for missingness (n = 7/96) and information about missing data mechanisms (n = 8/96). The most common approach for handling missing data was deletion (n = 65/96), mostly via complete-case analysis (CCA) (n = 43/96). Very few studies used multiple imputation (n = 8/96) or built-in mechanisms such as surrogate splits (n = 7/96) that directly address missing data during the development, validation, or implementation of the prediction model.Though missing values are highly common in any type of medical research and certainly in the research based on routine healthcare data, a majority of the prediction model studies using machine learning does not report sufficient information on the presence and handling of missing data. Strategies in which patient data are simply omitted are unfortunately the most often used methods, even though it is generally advised against and well known that it likely causes bias and loss of analytical power in prediction model development and in the predictive accuracy estimates. Prediction model researchers should be much more aware of alternative methodologies to address missing data.
Condenser: a Pre-training Architecture for Dense Retrieval

Pre-trained Transformer language models (LM) have become go-to text representation encoders.Prior research fine-tunes deep LMs to encode text sequences such as sentences and passages into single dense vector representations for efficient text comparison and retrieval.However, dense encoders require a lot of data and sophisticated techniques to effectively train and suffer in low data situations.This paper finds a key reason is that standard LMs' internal attention structure is not ready-to-use for dense encoders, which needs to aggregate text information into the dense representation.We propose to pre-train towards dense encoder with a novel Transformer architecture, Condenser, where LM prediction CONditions on DENSE Representation.Our experiments show Condenser improves over standard LM by large margins on various text retrieval and similarity tasks. 1
Effectiveness evaluation of digital contact tracing for COVID-19 in New South Wales, Australia

BackgroundDigital proximity tracing apps were rolled out early in the COVID-19 pandemic in many countries to complement conventional contact tracing. Empirical evidence about their benefits for pandemic response remains scarce. We evaluated the effectiveness and usefulness of COVIDSafe, Australia's national smartphone-based proximity tracing app for COVID-19.MethodsIn this prospective study, done in New South Wales (NSW), Australia, we included all individuals in the state who were older than 12 years with confirmed, locally acquired SARS-CoV-2 infection between May 4 and Nov 4, 2020. We used data from the NSW Notifiable Conditions Information Management System, the national COVIDSafe database, and information from case interviews, including information on app usage, the number of app-suggested contacts, and the number of app-suggested contacts determined by public health staff to be actual close contacts. We calculated the positive predictive value and sensitivity of COVIDSafe, its additional contact yield, and the number of averted public exposure events. Semi-structured interviews with public health staff were done to assess the app's perceived usefulness.FindingsThere were 619 confirmed COVID-19 cases with more than 25 300 close contacts identified by conventional contact tracing during the study period. COVIDSafe was used by 137 (22%) cases and detected 205 contacts, 79 (39%) of whom met the close contact definition. Its positive predictive value was therefore 39%. 35 (15%) of the 236 close contacts who could have been expected to have been using the app during the study period were identified by the app, making its estimated sensitivity 15%. 79 (0·3%) of the estimated 25 300 contacts in NSW were app-suggested and met the close contact definition. The app detected 17 (<0·1%) additional close contacts who were not identified by conventional contact tracing. COVIDSafe generated a substantial additional perceived workload for public health staff and was not considered useful.InterpretationThe low uptake of the app among cases probably led to a reduced sensitivity estimate in our study, given that only contacts who were using the app could be detected. COVIDSafe was not sufficiently effective to make a meaningful contribution to the COVID-19 response in Australia's most populous state over a 6 month period. We provide an empirical evaluation of this digital contact tracing app that questions the potential benefits of digital contact tracing apps to the public health response to COVID-19. Effectiveness evaluations should be integrated into future implementations of proximity contact tracing systems to justify their investment.FundingNew South Wales Ministry of Health (Australia); National Health and Medical Research Council (Australia)
FedProto: Federated Prototype Learning across Heterogeneous Clients

Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space. For example, clients may differ in terms of data distribution, network latency, input/output space, and/or model architecture, which can easily lead to the misalignment of their local gradients. To improve the tolerance to heterogeneity, we propose a novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients. FedProto aggregates the local prototypes collected from different clients, and then sends the global prototypes back to all clients to regularize the training of local models. The training on each client aims to minimize the classification error on the local data while keeping the resulting local prototypes sufficiently close to the corresponding global ones. Moreover, we provide a theoretical analysis to the convergence rate of FedProto under non-convex objectives. In experiments, we propose a benchmark setting tailored for heterogeneous FL, with FedProto outperforming several recent FL approaches on multiple datasets.
A Hybrid of Deep CNN and Bidirectional LSTM for Automatic Speech Recognition

Abstract Deep neural networks (DNNs) have been playing a significant role in acoustic modeling. Convolutional neural networks (CNNs) are the advanced version of DNNs that achieve 4–12% relative gain in the word error rate (WER) over DNNs. Existence of spectral variations and local correlations in speech signal makes CNNs more capable of speech recognition. Recently, it has been demonstrated that bidirectional long short-term memory (BLSTM) produces higher recognition rate in acoustic modeling because they are adequate to reinforce higher-level representations of acoustic data. Spatial and temporal properties of the speech signal are essential for high recognition rate, so the concept of combining two different networks came into mind. In this paper, a hybrid architecture of CNN-BLSTM is proposed to appropriately use these properties and to improve the continuous speech recognition task. Further, we explore different methods like weight sharing, the appropriate number of hidden units, and ideal pooling strategy for CNN to achieve a high recognition rate. Specifically, the focus is also on how many BLSTM layers are effective. This paper also attempts to overcome another shortcoming of CNN, i.e. speaker-adapted features, which are not possible to be directly modeled in CNN. Next, various non-linearities with or without dropout are analyzed for speech tasks. Experiments indicate that proposed hybrid architecture with speaker-adapted features and maxout non-linearity with dropout idea shows 5.8% and 10% relative decrease in WER over the CNN and DNN systems, respectively.