Reviewerly/exHarmony huggingface dataset:
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study

Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard plights in training deep architectures such as vanishing gradients and overfitting, it also uniquely suffers from over-smoothing, information squashing, and so on, which limits their potential power for encoding the high-order neighbor structure in large-scale graphs. Although numerous efforts are proposed to address these limitations, such as various forms of skip connections, graph normalization, and random dropping, it is difficult to disentangle the advantages brought by a deep GNN architecture from those tricks" necessary to train such an architecture. Moreover, the lack of a standardized benchmark with fair and consistent experimental settings poses an almost insurmountable obstacle to gauging the effectiveness of new mechanisms. In view of those, we present the first fair and reproducible benchmark dedicated to assessing the tricks" of training deep GNNs. We categorize existing approaches, investigate their hyperparameter sensitivity, and unify the basic configuration. Comprehensive evaluations are then conducted on tens of representative graph datasets including the recent large-scale Open Graph Benchmark, with diverse deep GNN backbones. We demonstrate that an organic combo of initial connection, identity mapping, group, and batch normalization attains the new state-of-the-art results for deep GNNs on large datasets.
A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections

In clinical diagnostics and research involving histopathology, formalin-fixed paraffin-embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 h) can slow decision-making. In contrast, fresh frozen (FF) processing (<1 h) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images ("virtual FFPE") from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five board-certified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort.
Progressive Neural Image Compression With Nested Quantization And Latent Ordering

We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstream. In contrast to existing learned variable bitrate solutions which produce separate bitstreams for each quality, it enables easier rate-control and requires less storage. Leveraging the latent scaling based variable bitrate solution, we introduce nested quantization, a method that defines multiple quantization levels with nested quantization grids, and progressively refines all latents from the coarsest to the finest quantization level. To achieve finer progressiveness in between any two quantization levels, latent elements are incrementally refined with an importance ordering defined in the rate-distortion sense. To the best of our knowledge, PLONQ is the first learning-based progressive image coding scheme and it outperforms SPIHT, a well-known wavelet-based progressive image codec.
Online Causal Feature Selection for Streaming Features

Recently, causal feature selection (CFS) has attracted considerable attention due to its outstanding interpretability and predictability performance. Such a method primarily includes the Markov blanket (MB) discovery and feature selection based on Granger causality. Representatively, the max–min MB (MMMB) can mine an optimal feature subset, i.e., MB; however, it is unsuitable for streaming features. Online streaming feature selection (OSFS) via online process streaming features can determine parents and children (PC), a subset of MB; however, it cannot mine the MB of the target attribute ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> ), i.e., a given feature, thus resulting in insufficient prediction accuracy. The Granger selection method (GSM) establishes a causal matrix of all features by performing excessively time; however, it cannot achieve a high prediction accuracy and only forecasts fixed multivariate time series data. To address these issues, we proposed an online CFS for streaming features (OCFSSFs) that mine MB containing PC and spouse and adopt the interleaving PC and spouse learning method. Furthermore, it distinguishes between PC and spouse in real time and can identify children with parents online when identifying spouses. We experimentally evaluated the proposed algorithm on synthetic datasets using precision, recall, and distance. In addition, the algorithm was tested on real-world and time series datasets using classification precision, the number of selected features, and running time. The results validated the effectiveness of the proposed algorithm.
Revisiting performance metrics for prediction with rare outcomes

Machine learning algorithms are increasingly used in the clinical literature, claiming advantages over logistic regression. However, they are generally designed to maximize the area under the receiver operating characteristic curve. While area under the receiver operating characteristic curve and other measures of accuracy are commonly reported for evaluating binary prediction problems, these metrics can be misleading. We aim to give clinical and machine learning researchers a realistic medical example of the dangers of relying on a single measure of discriminatory performance to evaluate binary prediction questions. Prediction of medical complications after surgery is a frequent but challenging task because many post-surgery outcomes are rare. We predicted post-surgery mortality among patients in a clinical registry who received at least one aortic valve replacement. Estimation incorporated multiple evaluation metrics and algorithms typically regarded as performing well with rare outcomes, as well as an ensemble and a new extension of the lasso for multiple unordered treatments. Results demonstrated high accuracy for all algorithms with moderate measures of cross-validated area under the receiver operating characteristic curve. False positive rates were [Formula: see text]1%, however, true positive rates were [Formula: see text]7%, even when paired with a 100% positive predictive value, and graphical representations of calibration were poor. Similar results were seen in simulations, with the addition of high area under the receiver operating characteristic curve ([Formula: see text]90%) accompanying low true positive rates. Clinical studies should not primarily report only area under the receiver operating characteristic curve or accuracy.
StreamHover: Livestream Transcript Summarization and Annotation

With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.However, the problem is nontrivial due to the informal nature of spoken language.Further, there has been a shortage of annotated datasets that are necessary for transcript summarization.In this paper, we present StreamHover, a framework for annotating and summarizing livestream transcripts.With a total of over 500 hours of videos annotated with both extractive and abstractive summaries, our benchmark dataset is significantly larger than currently existing annotated corpora.We explore a neural extractive summarization model that leverages vector-quantized variational autoencoder to learn latent vector representations of spoken utterances and identify salient utterances from the transcripts to form summaries.We show that our model generalizes better and improves performance over strong baselines.The results of this study provide an avenue for future research to improve summarization solutions for efficient browsing of livestreams.
‘I Don’t Need a Goal’: Attitudes and Practices in Fitness Tracking beyond WEIRD User Groups

Fitness trackers have the potential for fostering sustained change and increasing well-being. However, the research community is yet to understand what design features and values need to be embodied in a fitness tracker for long-term engagement. While past work mainly focused on WEIRD (Western, Educated, Industrialised, Rich, Democratic) fitness trackers usersin North America and Western Europe, this paper investigates another perspective on fitness tracking. We conducted interviews with N = 37 fitness tracker users in the US, Europe and Egypt to identify the similarities and differences in attitudes and practices in fitness tracking. We found that fitness tracking involved a deeper social context in Egyptian communities and our findings suggest that Arabic users focused on physiological measurement, while non-Arab Western users appear to bewere more interested in goal achievement. We contribute design dimensions that can help build more inclusive tracker experiences. Our work highlights how future fitness trackers should support a customisable spectrum of design values to offer engaging experiences to a diverse and global audience.
An Empirical Study on Application of Word Embedding Techniques for Prediction of Software Defect Severity Level

Software defect severity level helps to indicate the impact of bugs on the execution of the software and how rapidly these bugs need to be addressed by the team.The working team is regularly analyzing the bugs report and prioritizing the defects.The manual prioritization of these defects based on the experience may be an inaccurate prediction of the severity that will delay in fixing of critical bugs.It is compulsory to automate the process of assigning an appropriate level of severity based on bug report results with an objective to fix critical bugs without any delay.This work aims to develop defect severity level prediction models that have the ability to assign severity level of defects based on bugs report.In this work, seven different word embedding techniques are applied to defect description to represent the word, not just as a number but as a vector in n-dimensional space in order to reduce the number of features.Since the predictive ability of the developed models depends on the vectors extracted from text as they are used as an input to the defect severity level prediction models.Further, three feature selection techniques have been applied to find the right set of relevant vectors.The effectiveness of these word embedding techniques and different sets of vectors are evaluated using eleven different classification techniques with Synthetic Minority Oversampling Technique (SMOTE) to overcome the class imbalance problem.The experimental results show that the word embedding, feature selection techniques and SMOTE have the ability to predict the severity level of the defect in a software.
Verification of University Student and Graduate Data using Blockchain Technology

Blockchain is a reliable and innovative technology that harnesses education and training through digital technologies. Nonetheless, it has been still an issue keeping track of student/graduate academic achievement and blockchain access rights management. Detailed information about academic performance within a certain period (semester) is not present in the official education documents. Furthermore, academic achievement documents issued by institutions are not secured against unauthorized changes due to the involvement of intermediaries. Therefore, verification of official educational documents has become a pressing issue owing to the recent development of digital technologies. However, effective tools to accelerate the verification are rare as the process takes time. This study provides a prototype of the UniverCert platform based on a consortium version of the decentralized, open-source Ethereum blockchain technology. The proposed platform is based on a globally distributed peer-to-peer network that allows educational institutions to partner with the blockchain network, track student data, verify academic performance, and share documents with other stakeholders. The UniverCert platform was developed on a consortium blockchain architecture to address the problems universities face in storing and securing student data. The system provides a solution to facilitate students’ registration, verification, and authenticity of educational documents.
Comparison of cloud-based ion trap and superconducting quantum computer architectures

Quantum computing represents a radical departure from conventional approaches to information processing, offering the potential for solving problems that can never be approached classically. While large-scale quantum computer hardware is still in development, several quantum computing systems have recently become available as commercial cloud services. The authors compare the performance of IBMQ-16-Melbourne, IBMQ-Vigo, and Rigetti Aspen-8 superconducting systems, and IonQ ion trap systems on several simple quantum circuits and algorithms and examine component performance in the context of each system's architecture.
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction

Stepping from sentence-level to documentlevel, the research on relation extraction (RE) confronts increasing text length and more complicated entity interactions.Consequently, it is more challenging to encode the key information sources-relevant contexts and entity types.However, existing methods only implicitly learn to model these critical information sources while being trained for RE.As a result, they suffer the problems of ineffective supervision and uninterpretable model predictions.In contrast, we propose to explicitly teach the model to capture relevant contexts and entity types by Supervising and Augmenting Intermediate Steps (SAIS) for RE.Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately so as to enhance interpretability.By assessing model uncertainty, SAIS further boosts the performance via evidence-based data augmentation and ensemble inference while reducing the computational cost.Eventually, SAIS delivers state-of-the-art RE results on three benchmarks (DocRED, CDR, and GDA) and outperforms the runner-up by 5.04% relatively in F1 score in evidence retrieval on DocRED. 1 Document: Carl Linnaeus the Younger[0] Carl Linnaeus the Younger, … was a Swedish naturalist.[1] He is known as Linnaeus filius … to distinguish from his famous father, the systematist Carl Linnaeus.[2] He was enrolled at the University of Uppsala at the age of nine … [3] In 1763, …, he succeeded his father as the head of Practical Medicine at Uppsala.
Opportunistic Multi-Modal User Authentication for Health-Tracking IoT Wearables

With the advancement of technologies, market wearables are becoming increasingly popular with a range of services, including providing access to bank accounts, accessing cars, monitoring patients remotely, among several others. However, often these wearables collect various sensitive personal information of a user with no to limited authentication, e.g., knowledge-based external authentication techniques, such as PINs. While most of these external authentication techniques suffer from multiple limitations, including recall burden, human errors, or biases, researchers have started using various physiological and behavioral data, such as gait and heart rate, collected by the wearables to authenticate a wearable user implicitly with a limited accuracy due to sensing and computing constraints of wearables. In this work, we explore the usefulness of blood oxygen saturation (SpO 2) values collected from the Oximeter device to distinguish a user from others. From a cohort of 25 subjects, we find that 92% of the cases SpO 2 can distinguish pairs of users. From detailed modeling and performance analysis, we observe that while SpO 2 alone can obtain an average accuracy of 0.69 and F 1 score of 0.69, the addition of heart rate (HR) can improve the average identification accuracy by 15% and F 1 score by 13%. These results show promise in using SpO 2 along with other biometrics to develop implicit continuous authentications for wearables.
Personal health records sharing scheme based on attribute based signcryption with data integrity verifiable

The distribution of personal health records (PHRs) via a cloud server is a promising platform as it reduces the cost of data maintenance. Nevertheless, the cloud server is semi-trusted and can expose the patients’ PHRs to unauthorized third parties for financial gains or compromise the query result. Therefore, ensuring the integrity of the query results and privacy of PHRs as well as realizing fine-grained access control are critical key issues when PHRs are shared via cloud computing. Hence, we propose new personal health records sharing scheme with verifiable data integrity based on B+ tree data structure and attribute-based signcryption scheme to achieve data privacy, query result integrity, unforgeability, blind keyword search, and fine-grained access control.
Deep Reinforcement Learning on Autonomous Driving Policy With Auxiliary Critic Network

Deep reinforcement learning (DRL) is a machine learning method based on rewards, which can be extended to solve some complex and realistic decision-making problems. Autonomous driving needs to deal with a variety of complex and changeable traffic scenarios, so the application of DRL in autonomous driving presents a broad application prospect. In this article, an end-to-end autonomous driving policy learning method based on DRL is proposed. On the basis of proximal policy optimization (PPO), we combine a curiosity-driven method called recurrent neural network (RNN) to generate an intrinsic reward signal to encounter the agent to explore its environment, which improves the efficiency of exploration. We introduce an auxiliary critic network on the original actor–critic framework and choose the lower estimate which is predicted by the dual critic network when the network update to avoid the overestimation bias. We test our method on the lane- keeping task and overtaking task in the open racing car simulator (TORCS) driving simulator and compare with other DRL methods, experimental results show that our proposed method can improve the training efficiency and control performance in driving tasks.
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents

Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e., semantic entity), while the relations in-between are largely unexplored.In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task.Being different from the original dependency parsing model which recognizes dependency relations between words, we identify relations between groups of words with layout information instead.We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders.For the model training, we explore multi-task learning to combine entity labeling and relation extraction tasks; and for the evaluation, we conduct experiments on different datasets with filtering and augmentation.The results demonstrate that our proposed model achieves 65.96% F1 score on the FUNSD dataset.As for the realworld application, our model has been applied to the in-house customs data, achieving reliable performance in the production setting.
Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction

Sea subsurface temperature, an essential component of aquatic wildlife, underwater dynamics, and heat transfer with the sea surface, is affected by global warming in climate change. Existing research is commonly based on either physics-based numerical models or data-based models. Physical modeling and machine learning are traditionally considered as two unrelated fields for the sea subsurface temperature prediction task, with very different scientific paradigms (physics-driven and data-driven). However, we believe that both methods are complementary to each other. Physical modeling methods can offer the potential for extrapolation beyond observational conditions, while data-driven methods are flexible in adapting to data and are capable of detecting unexpected patterns. The combination of both approaches is very attractive and offers potential performance improvement. In this article, we propose a novel framework based on a generative adversarial network (GAN) combined with a numerical model to predict sea subsurface temperature. First, a GAN-based model is used to learn the simplified physics between the surface temperature and the target subsurface temperature in the numerical model. Then, observation data are used to calibrate the GAN-based model parameters to obtain a better prediction. We evaluate the proposed framework by predicting daily sea subsurface temperature in the South China Sea. Extensive experiments demonstrate the effectiveness of the proposed framework compared to existing state-of-the-art methods.
Measuring and characterizing generalization in deep reinforcement learning

Abstract Deep reinforcement learning (RL) methods have achieved remarkable performance on challenging control tasks. Observations of the resulting behavior give the impression that the agent has constructed a generalized representation that supports insightful action decisions. We re‐examine what is meant by generalization in RL, and propose several definitions based on an agent's performance in on‐policy, off‐policy, and unreachable states. We propose a set of practical methods for evaluating agents with these definitions of generalization. We demonstrate these techniques on a common benchmark task for deep RL, and we show that the learned networks make poor decisions for states that differ only slightly from on‐policy states, even though those states are not selected adversarially. We focus our analyses on the deep Q‐networks (DQNs) that kicked off the modern era of deep RL. Taken together, these results call into question the extent to which DQNs learn generalized representations, and suggest that more experimentation and analysis is necessary before claims of representation learning can be supported.