Reviewerly/exHarmony huggingface dataset:
SCOR: A secure international informatics infrastructure to investigate COVID-19

Abstract Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.
Dynamic Multi-Role Adaptive Collaborative Ant Colony Optimization for Robot Path Planning

Aiming at the problems of poor diversity and slow convergence of ant colony algorithm, dynamic multi-role adaptive collaborative ant colony optimization (MRCACO) is proposed in this paper, and it applies to robot path planning. Firstly, an adaptive dynamic complementary algorithm is proposed to form a heterogeneous multi-colony together with ACS and MMAS, which complement each other in performance. Secondly, a multi-role adaptive cooperation mechanism is proposed to realize the exchange and sharing of information. The mechanism includes two strategies: one is an elite attribute learning strategy, which highlights the role of elite attribute and improves the comprehensive performance of ACS and MMAS; The second is the pheromone balancing strategy, which is executed when the algorithm is stagnant to make the algorithm jump out of the local optimal. Further, the effectiveness and superiority in the algorithm are demonstrated by the experimental analysis of multiple TSP instances. Finally, the algorithm presented in this paper is applied to the path planning of the robot, two different deadlock rollback strategies are proposed to solve the deadlock problem and improve the efficiency of the algorithm. The results of a practical application show that the algorithm is feasible to solve the path planning problem.
Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique

Highly scalable resource supply capacity of cloud computing has greatly improved the execution speed of workflow applications, however, traditional workflow scheduling algorithms which focus on the optimization of makespan (execution time) of workflows, become inappropriate for the design of large-scale workflow systems. Workflow scheduling in cloud computing is particularly a multiobjective optimization problem, in which many critical issues besides the execution time of workflows should be taken into account. Although many heuristics and meta-heuristics have been proposed to solve this problem, most of them cannot produce satisfactory cost-makespan tradeoffs and have a long time overhead. In this paper, we propose an efficient heuristic named CMSWC (Cost and Makespan Scheduling of Workflows in the Cloud) to solve the workflow scheduling problem, by simultaneously minimizing cost and makespan of workflows. CMSCW follows a two-phase list scheduling philosophy: ranking and mapping. Furthermore, CMSCW incorporates with three designs specifically for the multiobjective challenges: (i) The mapping phase is designed to avoid exploring useless resources for tasks, which significantly narrows down the search space. (ii) A new method is proposed to select non-dominated solutions, by combining the quick non-dominated sorting approach and Shift-Based Density Estimation (SDE) based crowding distance. (iii) Several elitist study strategies are designed to make solutions close to the true Pareto front as well as avoid trapping into local optimum. Extensive experiments on real-life workflows demonstrate that our approach can generate better cost-makespan tradeoff fronts than that of several state-of-the-art approaches.
Ultimate Limits for Multiple Quantum Channel Discrimination

Quantum hypothesis testing is a central task in the entire field of quantum information theory. Understanding its ultimate limits will give insight into a wide range of quantum protocols and applications, from sensing to communication. Although the limits of hypothesis testing between quantum states have been completely clarified by the pioneering works of Helstrom in the 1970s, the more difficult problem of hypothesis testing with quantum channels, i.e., channel discrimination, is less understood. This is mainly due to the complications coming from the use of input entanglement and the possibility of employing adaptive strategies. In this Letter, we establish a lower limit for the ultimate error probability affecting the discrimination of an arbitrary number of quantum channels. We also show that this lower bound is achievable when the channels have certain symmetries. As an example, we apply our results to the problem of channel position finding, where the goal is to identify the location of a target channel among multiple background channels. In this general setting, we find that the use of entanglement offers a great advantage over strategies without entanglement, with nontrivial implications for data readout, target detection, and quantum spectroscopy.
Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

In recent years, a significant boost in data availability for persistent data streams has been observed.These data streams are continually evolving, with the clusters frequently forming arbitrary shapes instead of regular shapes in the data space.This characteristic leads to an exponential increase in the processing time of traditional clustering algorithms for data streams.In this study, we propose a new online method, which is a density grid-based method for data stream clustering.The primary objectives of the density grid-based method are to reduce the number of distant function calls and to improve the cluster quality.The method is conducted entirely online and consists of two main phases.The first phase generates the Core Micro-Clusters (CMCs), and the second phase combines the CMCs into macro clusters.The grid-based method was utilized as an outlier buffer in order to handle multi-density data and noises.The method was tested on real and synthetic data streams employing different quality metrics and was compared with the popular method of clustering evolving data streams into arbitrary shapes.The proposed method was demonstrated to be an effective solution for reducing the number of calls to the distance function and improving the cluster quality.
Towards efficient unconstrained handwriting recognition using Dilated Temporal Convolution Network

Recognition of cursive handwritten images has advanced well with recent recurrent architectures and attention mechanism. Most of the works focus on improving transcription performance in terms of Character Error Rate (CER) and Word Error Rate (WER). Existing models are too slow to train and test networks. Furthermore, recent studies have recommended models be not only efficient in terms of task performance but also environmentally friendly in terms of model carbon footprint. Reviewing the recent state-of-the-art models, it recommends considering model training and retraining time while designing. High training time increases costs not only in terms of resources but also in carbon footprint. This becomes challenging for handwriting recognition model with popular recurrent architectures. It is truly critical since line images usually have a very long width resulting in a longer sequence to decode. In this work, we present a fully convolution based deep network architecture for cursive handwriting recognition from line level images. The architecture is a combination of 2-D convolutions and 1-D dilated non causal convolutions with Connectionist Temporal Classification (CTC) output layer. This offers a high parallelism with a smaller number of parameters. We further demonstrate experiments with various re-scaling factors of the images and how it affects the performance of the proposed model. A data augmentation pipeline is further analyzed while model training. The experiments show our model, has comparable performance on CER and WER measures with recurrent architectures. A comparison is done with state-of-the-art models with different architectures based on Recurrent Neural Networks (RNN) and its variants. The analysis shows training performance and network details of three different dataset of English and French handwriting. This shows our model has fewer parameters and takes less training and testing time, making it suitable for low-resource and environment-friendly deployment.
Review of developments in person-centred healthcare

In recent years, there has been a shift in orientation towards person-centredness as part of a global move towards humanising and centralising the person within healthcare.Person-centredness, underpinned by robust philosophical and theoretical concepts, has an increasingly solid footprint in policy and practice, but research and education lag behind.This article considers the emergence of person-centredness, including person-centred care, and how it is positioned in healthcare policy around the world, while recognising a dominant philosophical positioning in Western philosophy, concepts and theories.Second, the evolution of person-centred healthcare over the past five years is reviewed.Published evidence of person-centred healthcare developments is drawn on, as well as information gathered from key stakeholders who engaged with the partner organisations in an Erasmus+ project to develop a European person-centred healthcare curriculum framework.Five themes are identified, which underpin the literature and stakeholder perspectives:• Policy development for transformation • Participatory strategies for public engagement • Healthcare integration and coordination strategies • Frameworks for practice • Process and outcome measurementThese themes reflect the World Health Organization's global perspective on people-centred and integrated healthcare, and give some indication of development priorities as person-centred healthcare systems continue to be developed.
Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness

Recent developments in sensor technologies enable physical activity recognition (PAR) as an essential tool for smart health monitoring and for fitness exercises. For efficient PAR, model representation and training are significant factors contributing to the ultimate success of recognition systems because model representation and accurate detection of body parts and physical activities cannot be distinguished if the system is not well trained. This paper provides a unified framework that explores multidimensional features with the help of a fusion of body part models and quadratic discriminant analysis which uses these features for markerless human pose estimation. Multilevel features are extracted as displacement parameters to work as spatiotemporal properties. These properties represent the respective positions of the body parts with respect to time. Finally, these features are processed by a maximum entropy Markov model as a recognition engine based on transition and emission probability values. Experimental results demonstrate that the proposed model produces more accurate results compared to the state-of-the-art methods for both body part detection and for physical activity recognition. The accuracy of the proposed method for body part detection is 90.91% on a University of Central Florida’s (UCF) sports action dataset and, for activity recognition on a UCF YouTube action dataset and an IM-DailyRGBEvents dataset, accuracy is 89.09% and 88.26% respectively.
Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation

We address whether neural models for Natural Language Inference (NLI) can learn the compositional interactions between lexical entailment and negation, using four methods: the behavioral evaluation methods of (1) challenge test sets and (2) systematic generalization tasks, and the structural evaluation methods of (3) probes and (4) interventions.To facilitate this holistic evaluation, we present Monotonicity NLI (MoNLI), a new naturalistic dataset focused on lexical entailment and negation.In our behavioral evaluations, we find that models trained on general-purpose NLI datasets fail systematically on MoNLI examples containing negation, but that MoNLI fine-tuning addresses this failure.In our structural evaluations, we look for evidence that our top-performing BERT-based model has learned to implement the monotonicity algorithm behind MoNLI.Probes yield evidence consistent with this conclusion, and our intervention experiments bolster this, showing that the causal dynamics of the model mirror the causal dynamics of this algorithm on subsets of MoNLI.This suggests that the BERT model at least partially embeds a theory of lexical entailment and negation at an algorithmic level.
Use of an Internet-of-Things Smart Home System for Healthy Aging in Older Adults in Residential Settings: Pilot Feasibility Study

The Internet-of-Things (IoT) technologies can create smart residences that integrate technology within the home to enhance residents' safety as well as monitor their health and wellness. However, there has been little research on real-world testing of IoT smart home devices with older adults, and the feasibility and acceptance of such tools have not been systematically examined.This study aims to conduct a pilot study to investigate the feasibility of using IoT smart home devices in the actual residences of older adults to facilitate healthy aging.We conducted a 2-month feasibility study on community-dwelling older adults. Participants chose among different IoT devices to be installed and deployed within their homes. The IoT devices tested varied depending on the participant's preference: a door and window sensor, a multipurpose sensor (motion, temperature, luminosity, and humidity), a voice-operated smart speaker, and an internet protocol (IP) video camera.We recruited a total of 37 older adults for this study, with 35 (95%) successfully completing all procedures in the 2-month study. The average age of the sample was 78 (SD 9) years and primarily comprised women (29/37, 78%), those who were educated (31/37, 86%; bachelor's degree or higher), and those affected by chronic conditions (33/37, 89%). The most widely chosen devices among the participants were multipurpose sensors and smart speakers. An IP camera was a significantly unpopular choice among participants in both phases. The participant feedback suggests that perceived privacy concerns, perceived usefulness, and curiosity to technology were strong factors when considering which device to have installed in their home.Overall, our deployment results revealed that the use of IoT smart home devices is feasible in actual residences of older adults. These findings may inform the follow-up assessment of IoT technologies and their impact on health-related outcomes and advance our understanding of the role of IoT home-based monitoring technologies to promote successful aging-in-place for older adults. Future trials should consider older adults' preferences for the different types of smart home devices to be installed in real-world residential settings.
Comparison of Statistical and Machine Learning Techniques for Physical Layer Authentication

In this paper we consider authentication at the physical layer, in which the authenticator aims at distinguishing a legitimate supplicant from an attacker on the basis of the characteristics of a set of parallel wireless channels, which are affected by time-varying fading. Moreover, the attacker's channel has a spatial correlation with the supplicant's one. In this setting, we assess and compare the performance achieved by different approaches under different channel conditions. We first consider the use of two different statistical decision methods, and we prove that using a large number of references (in the form of channel estimates) affected by different levels of time-varying fading is not beneficial from a security point of view. We then consider classification methods based on machine learning. In order to face the worst case scenario of an authenticator provided with no forged messages during training, we consider one-class classifiers. When instead the training set includes some forged messages, we resort to more conventional binary classifiers, considering the cases in which such messages are either labelled or not. For the latter case, we exploit clustering algorithms to label the training set. The performance of both nearest neighbor (NN) and support vector machine (SVM) classification techniques is evaluated. Through numerical examples, we show that under the same probability of false alarm, one-class classification (OCC) algorithms achieve the lowest probability of missed detection when a small spatial correlation exists between the main channel and the adversary one, while statistical methods are advantageous when the spatial correlation between the two channels is large.
Losing Heads in the Lottery: Pruning Transformer Attention in Neural Machine Translation

The attention mechanism is the crucial component of the transformer architecture.Recent research shows that most attention heads are not confident in their decisions and can be pruned after training.However, removing them before training a model results in lower quality.In this paper, we apply the lottery ticket hypothesis to prune heads in the early stages of training, instead of doing so on a fully converged model.Our experiments on machine translation show that it is possible to remove up to three-quarters of all attention heads from a transformer-big model with an average -0.1 change in BLEU for Turkish→English.The pruned model is 1.5 times as fast at inference, albeit at the cost of longer training.The method is complementary to other approaches, such as teacher-student, with our English→German student losing 0.2 BLEU at 75% encoder attention sparsity.
Learning to Generate Clinically Coherent Chest X-Ray Reports

Automated radiology report generation has the potential to reduce the time clinicians spend manually reviewing radiographs and streamline clinical care.However, past work has shown that typical abstractive methods tend to produce fluent, but clinically incorrect radiology reports.In this work, we develop a radiology report generation model utilizing the transformer architecture that produces superior reports as measured by both standard language generation and clinical coherence metrics compared to competitive baselines.We then develop a method to differentiably extract clinical information from generated reports and utilize this differentiability to fine-tune our model to produce more clinically coherent reports. 1
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers. The documents come from judgment documents and the questions are annotated by law experts. The CJRC dataset can help researchers extract elements by reading comprehension technology. Element extraction is an important task in the legal field. However, it is difficult to predefine the element types completely due to the diversity of document types and causes of action. By contrast, machine reading comprehension technology can quickly extract elements by answering various questions from the long document. We build two strong baseline models based on BERT and BiDAF. The experimental results show that there is enough space for improvement compared to human annotators.
Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers.The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs.The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market.The focus of this paper is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids.Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies.Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information.The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark.The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks.