Reviewerly/exHarmony huggingface dataset:
Potential of blockchain technology in supply chain management: a literature review

Purpose The purpose of this paper is to review the existing literature on blockchain technology, present some trends and consider its potential value in supply chain management (SCM). Design/methodology/approach Papers that contained the word “blockchain” in their titles, keywords or abstracts were selected for conducting trend analyses. Findings The blockchain technology is rapidly making inroads in many industries and there is tremendous potential to eliminate intermediaries and to make SCM more efficient. Research limitations/implications This analysis is limited to 299 papers from the EBSCO database through December 2018. Practical implications This paper highlights the imperative role of blockchain technology that has created a discourse in the world of innovation and technology. This work will help academics to further the understanding of blockchain technology. Social implications Blockchain technology will provide transparency to consumers. Originality/value This paper presents the first review of blockchain technology and delves into its value in SCM. This work will help researchers in identifying the areas where blockchain is the most desirable and can be implemented.
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking

Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution. Going beyond these sub-optimal frameworks, we propose a simple online model named Chained-Tracker (CTracker), which naturally integrates all the three subtasks into an end-to-end solution (the first as far as we know). It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module). The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data. The source code of CTracker can be found at: github.com/pjl1995/CTracker .
A Semi-Supervised Graph Attentive Network for Financial Fraud Detection

With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection. To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semi-supervised attentive graph neural network, namedSemiGNN to utilize the multi-view labeled and unlabeled data for fraud detection. Moreover, we propose a hierarchical attention mechanism to better correlate different neighbors and different views. Simultaneously, the attention mechanism can make the model interpretable and tell what are the important factors for the fraud and why the users are predicted as fraud. Experimentally, we conduct the prediction task on the users of Alipay, one of the largest third-party online and offline cashless payment platform serving more than 4 hundreds of million users in China. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the state-of-the-art methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization

Neural abstractive summarization models are prone to generate content inconsistent with the source document, i.e. unfaithful. Existing automatic metrics do not capture such mistakes effectively. We tackle the problem of evaluating faithfulness of a generated summary given its source document. We first collected human annotations of faithfulness for outputs from numerous models on two datasets. We find that current models exhibit a trade-off between abstractiveness and faithfulness: outputs with less word overlap with the source document are more likely to be unfaithful. Next, we propose an automatic question answering (QA) based metric for faithfulness, FEQA, which leverages recent advances in reading comprehension. Given question-answer pairs generated from the summary, a QA model extracts answers from the document; non-matched answers indicate unfaithful information in the summary. Among metrics based on word overlap, embedding similarity, and learned language understanding models, our QA-based metric has significantly higher correlation with human faithfulness scores, especially on highly abstractive summaries.
Error-Mitigated Digital Quantum Simulation

Variational algorithms may enable classically intractable simulations on near-future quantum computers. However, their potential is limited by hardware errors. It is therefore crucial to develop efficient ways to mitigate these errors. Here, we propose a stabilizerlike method which enables the detection of up to 60%-80% of depolarizing errors. Our method is suitable for near-term quantum hardware. Simulations show that our method can significantly benefit calculations subject to both stochastic and correlated noise, especially when combined with existing error mitigation techniques.
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network

Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs.In this paper, we introduce the topic entity graph, a local sub-graph of an entity, to represent entities with their contextual information in KG.From this view, the KB-alignment task can be formulated as a graph matching problem; and we further propose a graph-attention based solution, which first matches all entities in two topic entity graphs, and then jointly model the local matching information to derive a graphlevel matching vector.Experiments show that our model outperforms previous state-of-theart methods by a large margin.
Asking and Answering Questions to Evaluate the Factual Consistency of Summaries

Practical applications of abstractive summarization models are limited by frequent factual inconsistencies with respect to their input.Existing automatic evaluation metrics for summarization are largely insensitive to such errors.We propose QAGS, 1 an automatic evaluation protocol that is designed to identify factual inconsistencies in a generated summary.QAGS is based on the intuition that if we ask questions about a summary and its source, we will receive similar answers if the summary is factually consistent with the source.To evaluate QAGS, we collect human judgments of factual consistency on model-generated summaries for the CNN/DailyMail (Hermann et al., 2015) and XSUM (Narayan et al., 2018) summarization datasets.QAGS has substantially higher correlations with these judgments than other automatic evaluation metrics.Also, QAGS offers a natural form of interpretability: The answers and questions generated while computing QAGS indicate which tokens of a summary are inconsistent and why.We believe QAGS is a promising tool in automatically generating usable and factually consistent text.Code for QAGS will be available at https://github.com/W4ngatang/qags.
Deep Learning for Anomaly Detection: A Survey

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.
Characterizing large-scale quantum computers via cycle benchmarking

Abstract Quantum computers promise to solve certain problems more efficiently than their digital counterparts. A major challenge towards practically useful quantum computing is characterizing and reducing the various errors that accumulate during an algorithm running on large-scale processors. Current characterization techniques are unable to adequately account for the exponentially large set of potential errors, including cross-talk and other correlated noise sources. Here we develop cycle benchmarking, a rigorous and practically scalable protocol for characterizing local and global errors across multi-qubit quantum processors. We experimentally demonstrate its practicality by quantifying such errors in non-entangling and entangling operations on an ion-trap quantum computer with up to 10 qubits, and total process fidelities for multi-qubit entangling gates ranging from $$99.6(1)\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>99.6</mml:mn><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mi>%</mml:mi></mml:math> for 2 qubits to $$86(2)\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>86</mml:mn><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mi>%</mml:mi></mml:math> for 10 qubits. Furthermore, cycle benchmarking data validates that the error rate per single-qubit gate and per two-qubit coupling does not increase with increasing system size.
An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)

This paper aims to review Artificial neural networks, Multi-Layer Perceptron Neural network (MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for early diagnosis of breast cancer based on their accuracy in order to identify which method is better for the diagnosis of breast cell malignancies. Deep comparison of functioning of each network and its designing is performed and then analysis is done based on the accuracy of diagnosis and classification of breast malignancy by the network to decide which network outperforms the other. CNN is found to give slightly higher accuracy than MLP for diagnosis and detection of breast cancer. There still is the need to carefully analyse and perform a thorough research that uses both these methods on the same data set under same conditions in order identify the architecture that gives better accuracy.
Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction

We present a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses. This task, which is often applied to novel view synthesis, is recently revolution-ized by Neural Radiance Field (NeRF) for its state-of-the-art quality and fiexibility. However, NeRF and its variants require a lengthy training time ranging from hours to days for a single scene. In contrast, our approach achieves NeRF-comparable quality and converges rapidly from scratch in less than 15 minutes with a single GPU. We adopt a representation consisting of a density voxel grid for scene geometry and a feature voxel grid with a shallow network for complex view-dependent appearance. Modeling with explicit and discretized volume representations is not new, but we propose two simple yet non-trivial techniques that contribute to fast convergence speed and high-quality output. First, we introduce the post-activation interpolation on voxel density, which is capable of producing sharp surfaces in lower grid resolution. Second, direct voxel density optimization is prone to suboptimal geometry solutions, so we robustify the optimization process by imposing several priors. Finally, evaluation on five inward-facing benchmarks shows that our method matches, if not surpasses, NeRF's quality, yet it only takes about 15 minutes to train from scratch for a new scene. Code: https://github.com/sunset1995/DirectVoxGO.
Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak

Abstract The chronic nature of coronavirus disease (COVID-19) outbreak and lack of success in treatment and cure is creating an environment that is crucial for mental wellbeing. Presently, we extracted and classified sentiments and emotions from 141,208 headlines of global English news sources regarding the coronavirus disease (COVID-19). The headlines considered were those carrying keyword coronavirus between the time frame 15 Janaury, 2020 to 3 June, 2020 from top rated 25 English news sources. The headlines were classified into positive, negative and neutral sentiments after the calculation of text unbounded polarity at the sentence level score and incorporating the valence shifters. In addition, the National Research Council Canada (NRC) Word-Emotion Lexicon was used to calculate the presence of eight emotions at their emotional weight. The results reveal that the news headlines had high emotional scores with a negative polarity. More precisely, around 52% of the news headlines evoked negative sentiments and only 30% evoked positive sentiments while 18% were neutral. Fear, trust, anticipation, sadness, and anger were the main emotions evoked by the news headlines. Overall, the findings of this study can be weaved together into important implications for emotional wellbeing and economic perspective.
IL2M: Class Incremental Learning With Dual Memory

This paper presents a class incremental learning (IL) method which exploits fine tuning and a dual memory to reduce the negative effect of catastrophic forgetting in image recognition. First, we simplify the current fine tuning based approaches which use a combination of classification and distillation losses to compensate for the limited availability of past data. We find that the distillation term actually hurts performance when a memory is allowed. Then, we modify the usual class IL memory component. Similar to existing works, a first memory stores exemplar images of past classes. A second memory is introduced here to store past class statistics obtained when they were initially learned. The intuition here is that classes are best modeled when all their data are available and that their initial statistics are useful across different incremental states. A prediction bias towards newly learned classes appears during inference because the dataset is imbalanced in their favor. The challenge is to make predictions of new and past classes more comparable. To do this, scores of past classes are rectified by leveraging contents from both memories. The method has negligible added cost, both in terms of memory and of inference complexity. Experiments with three large public datasets show that the proposed approach is more effective than a range of competitive state-of-the-art methods.
WinoGrande: An Adversarial Winograd Schema Challenge at Scale

The Winograd Schema Challenge (WSC) (Levesque, Davis, and Morgenstern 2011), a benchmark for commonsense reasoning, is a set of 273 expert-crafted pronoun resolution problems originally designed to be unsolvable for statistical models that rely on selectional preferences or word associations. However, recent advances in neural language models have already reached around 90% accuracy on variants of WSC. This raises an important question whether these models have truly acquired robust commonsense capabilities or whether they rely on spurious biases in the datasets that lead to an overestimation of the true capabilities of machine commonsense.To investigate this question, we introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations. The best state-of-the-art methods on WinoGrande achieve 59.4 – 79.1%, which are ∼15-35% (absolute) below human performance of 94.0%, depending on the amount of the training data allowed (2% – 100% respectively).Furthermore, we establish new state-of-the-art results on five related benchmarks — WSC (→ 90.1%), DPR (→ 93.1%), COPA(→ 90.6%), KnowRef (→ 85.6%), and Winogender (→ 97.1%). These results have dual implications: on one hand, they demonstrate the effectiveness of WinoGrande when used as a resource for transfer learning. On the other hand, they raise a concern that we are likely to be overestimating the true capabilities of machine commonsense across all these benchmarks. We emphasize the importance of algorithmic bias reduction in existing and future benchmarks to mitigate such overestimation.
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard and achieved tremendous success. However, due to the intrinsic locality of convolution operations, U-Net generally demonstrates limitations in explicitly modeling long-range dependency. Transformers, designed for sequence-to-sequence prediction, have emerged as alternative architectures with innate global self-attention mechanisms, but can result in limited localization abilities due to insufficient low-level details. In this paper, we propose TransUNet, which merits both Transformers and U-Net, as a strong alternative for medical image segmentation. On one hand, the Transformer encodes tokenized image patches from a convolution neural network (CNN) feature map as the input sequence for extracting global contexts. On the other hand, the decoder upsamples the encoded features which are then combined with the high-resolution CNN feature maps to enable precise localization. We argue that Transformers can serve as strong encoders for medical image segmentation tasks, with the combination of U-Net to enhance finer details by recovering localized spatial information. TransUNet achieves superior performances to various competing methods on different medical applications including multi-organ segmentation and cardiac segmentation. Code and models are available at https://github.com/Beckschen/TransUNet.
Machine Learning for Health Services Researchers

BackgroundMachine learning is increasingly used to predict healthcare outcomes, including cost, utilization, and quality.ObjectiveWe provide a high-level overview of machine learning for healthcare outcomes researchers and decision makers.MethodsWe introduce key concepts for understanding the application of machine learning methods to healthcare outcomes research. We first describe current standards to rigorously learn an estimator, which is an algorithm developed through machine learning to predict a particular outcome. We include steps for data preparation, estimator family selection, parameter learning, regularization, and evaluation. We then compare 3 of the most common machine learning methods: (1) decision tree methods that can be useful for identifying how different subpopulations experience different risks for an outcome; (2) deep learning methods that can identify complex nonlinear patterns or interactions between variables predictive of an outcome; and (3) ensemble methods that can improve predictive performance by combining multiple machine learning methods.ResultsWe demonstrate the application of common machine methods to a simulated insurance claims dataset. We specifically include statistical code in R and Python for the development and evaluation of estimators for predicting which patients are at heightened risk for hospitalization from ambulatory care-sensitive conditions.ConclusionsOutcomes researchers should be aware of key standards for rigorously evaluating an estimator developed through machine learning approaches. Although multiple methods use machine learning concepts, different approaches are best suited for different research problems.
Do ImageNet Classifiers Generalize to ImageNet

We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly harder images than those found in the original test sets.
Analysis of Convolutional Neural Network based Image Classification Techniques

With the rapid urbanization and people moving from rural areas to urban time has become a very huge commodity. As a result of this change in people's lifestyles, there is a growing need for speed and efficiency. In the supermarket industry, item identification and billing are generally done manually, which takes a lot of time and effort. The lack of a bar code on the fruit products slows down the processing time. Before beginning the billing process, the seller may need to weigh the items in order to update the barcode, or the biller may need to input the item's name manually. This doubles the effort and also consumes a significant amount of time. As a result, several convolutional neural network-based classifiers are proposed to identify the fruits by visualizing via the camera for establishing a quick billing procedure in order to overcome this difficulty. The best model among the suggested models is capable of classifying pictures with start-of-art accuracy, which is superior than that of previously published studies.