Reviewerly/exHarmony huggingface dataset:
Assessing the implementation of digital innovations in response to COVID-19 to address public health key functions: A scoping review of academic and non-academic literature (Preprint)

Background Digital technologies have been central to efforts to respond to the COVID-19 pandemic. In this context, a range of literature has reported on developments regarding the implementation of new digital technologies for COVID-19–related surveillance, prevention, and control. Objective In this study, scoping reviews of academic and nonacademic literature were undertaken to obtain an overview of the evidence regarding digital innovations implemented to address key public health functions in the context of the COVID-19 pandemic. This study aimed to expand on the work of existing reviews by drawing on additional data sources (including nonacademic sources) by considering literature published over a longer time frame and analyzing data in terms of the number of unique digital innovations. Methods We conducted a scoping review of the academic literature published between January 1, 2020, and September 15, 2020, supplemented by a further scoping review of selected nonacademic literature published between January 1, 2020, and October 13, 2020. Both reviews followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. Results A total of 226 academic articles and 406 nonacademic articles were included. The included articles provided evidence of 561 (academic literature) and 497 (nonacademic literature) unique digital innovations. The most common implementation settings for digital innovations were the United States, China, India, and the United Kingdom. Technologies most commonly used by digital innovations were those belonging to the high-level technology group of integrated and ubiquitous fixed and mobile networks. The key public health functions most commonly addressed by digital innovations were communication and collaboration and surveillance and monitoring. Conclusions Digital innovations implemented in response to the COVID-19 pandemic have been wide ranging in terms of their implementation settings, the digital technologies used, and the public health functions addressed. However, evidence gathered through this study also points to a range of barriers that have affected the successful implementation of digital technologies for public health functions. It is also evident that many digital innovations implemented in response to the COVID-19 pandemic are yet to be formally evaluated or assessed.
Fast multi-qubit global-entangling gates without individual addressing of trapped ions

We propose and study ways speeding up of the entangling operations in the trapped ions system with high fidelity. First, we find a scheme to increase the speed of a two-qubit gate without the limitation of trap frequency, which was considered as the fundamental limit. Second, we study the fast gate scheme for entangling more than two qubits simultaneously. We apply the method of applying multiple frequency components on laser beams for the gate operations. In particular, in order to avoid infinite terms from the coupling to carrier transition, we focus on the phase-insensitive gate scheme here. We carefully study the effect of large excitation of motional mode beyond the limit of Lamb-Dicke approximation by including up to second order terms of the Lamb-Dicke parameter. We study the speed limit of multi-qubit global entangling gates without individual addressing requirements. Furthermore, our gates can be made insensitive to the fluctuation of initial motional phases which are difficult to stabilise in the phase-insensitive gate scheme.
Constrained In-network Computing with Low Congestion in Datacenter Networks

Distributed computing has become a common practice nowadays, where recent focus has been given to the usage of smart networking devices with in-network computing capabilities. State-of-the-art switches with near-line rate computing and aggregation capabilities enable acceleration and improved performance for various modern applications like big data analytics and large-scale distributed and federated machine learning.In this work, we formulate and study the theoretical algorithmic foundations of such approaches, and focus on how to deploy and use constrained in-network computing capabilities within the data center. We focus our attention on reducing the network congestion, i.e., the most congested link in the network, while supporting the given workload(s). We present an efficient optimal algorithm for tree-like network topologies and show that our solution provides as much as an x13 improvement over common alternative approaches. In particular, our results show that having merely a small fraction of network devices that support in-network aggregation can significantly reduce the network congestion, both for single and multiple workloads.
An immune plasma algorithm based approach for UCAV path planning

Immune Plasma algorithm for short IPA is one of the most recent meta-heuristics and differs when the speciality of the process tried to be modeled is considered. The competitive performance of the IPA on different numerical optimization or engineering problems gave inspiration for the studies related with the specializing or porting IPA to more complex real world problems. In this study, IPA was ported in order to solve unmanned combat aerial vehicle (UCAV) path planning problem and its capabilities were investigated in detail by assigning different values to the population size, number of donors-receivers and using challenging battlefield scenarios. The results found by the IPA were also compared with the results obtained by a set of well-known meta-heuristic algorithms. The comparative studies informed that IPA is capable of finding more robust path or paths for the UCAV operating on a battlefield and outperforms other executed algorithms for almost all the test scenarios.
Strong Equivalence of Logic Programs with Counting

Abstract In answer set programming, two groups of rules are considered strongly equivalent if they have the same meaning in any context. In some cases, strong equivalence of programs in the input language of the grounder gringo can be established by deriving rules of each program from rules of the other. The possibility of such proofs has been demonstrated for a subset of that language that includes comparisons, arithmetic operations, and simple choice rules, but not aggregates. This method is extended here to a class of programs in which some uses of the #count aggregate are allowed.
Sentence Simplification Capabilities of Transfer-Based Models

According to the official adult literacy report conducted in 24 highly-developed countries, more than 50% adults, on average, can only understand basic vocabulary, short sentences, and basic syntactic constructions. Everyday information found in news articles is thus inaccessible to many people, impeding their social inclusion and informed decision-making. Systems for automatic sentence simplification aim to provide scalable solution to this problem. In this paper, we propose new state-of-the-art sentence simplification systems for English and Spanish, and specifications for expert evaluation that are in accordance with well-established easy-to-read guidelines. We conduct expert evaluation of our new systems and the previous state-of-the-art systems for English and Spanish, and discuss strengths and weaknesses of each of them. Finally, we draw conclusions about the capabilities of the state-of-the-art sentence simplification systems and give some directions for future research.
On Optimizing Top-K Metrics for Neural Ranking Models

Top-K metrics such as [email protected] are frequently used to evaluate ranking performance. The traditional tree-based models such as LambdaMART, which are based on Gradient Boosted Decision Trees (GBDT), are designed to optimize [email protected] using the LambdaRank losses. Recently, there is a good amount of research interest on neural ranking models for learning-to-rank tasks. These models are fundamentally different from the decision tree models and behave differently with respect to different loss functions. For example, the most popular ranking losses used in neural models are the Softmax loss and the GumbelApproxNDCG loss. These losses do not connect to top-K metrics such as [email protected] naturally. It remains a question on how to effectively optimize [email protected] for neural ranking models. In this paper, we follow the LambdaLoss framework and design novel and theoretically sound losses for [email protected] metrics, while the original LambdaLoss paper can only do so using an unsound heuristic. We study the new losses on the LETOR benchmark datasets and show that the new losses work better than other losses for neural ranking models.
Ultra-high resolution, multi-scale, context-aware approach for detection of small cancers on mammography

Abstract While detection of malignancies on mammography has received a boost with the use of Convolutional Neural Networks (CNN), detection of cancers of very small size remains challenging. This is however clinically significant as the purpose of mammography is early detection of cancer, making it imperative to pick them up when they are still very small. Mammography has the highest spatial resolution (image sizes as high as 3328 × 4096 pixels) out of all imaging modalities, a requirement that stems from the need to detect fine features of the smallest cancers on screening. However due to computational constraints, most state of the art CNNs work on reduced resolution images. Those that work on higher resolutions, compromise on global context and work at single scale. In this work, we show that resolution, scale and image-context are all important independent factors in detection of small masses. We thereby use a fully convolutional network, with the ability to take any input size. In addition, we incorporate a systematic multi-scale, multi-resolution approach, and encode image context, which we show are critical factors to detection of small masses. We show that this approach improves the detection of cancer, particularly for small masses in comparison to the baseline model. We perform a single institution multicentre study, and show the performance of the model on a diagnostic mammography dataset, a screening mammography dataset, as well as a curated dataset of small cancers &lt; 1 cm in size. We show that our approach improves the sensitivity from 61.53 to 87.18% at 0.3 False Positives per Image (FPI) on this small cancer dataset. Model and code are available from https://github.com/amangupt01/Small_Cancer_Detection
NILINKER: Attention-based approach to NIL Entity Linking

The existence of unlinkable (NIL) entities is a major hurdle affecting the performance of Named Entity Linking approaches, and, consequently, the performance of downstream models that depend on them. Existing approaches to deal with NIL entities focus mainly on clustering and prediction and are limited to general entities. However, other domains, such as the biomedical sciences, are also prone to the existence of NIL entities, given the growing nature of scientific literature. We propose NILINKER, a model that includes a candidate retrieval module for biomedical NIL entities and a neural network that leverages the attention mechanism to find the top-k relevant concepts from target Knowledge Bases (MEDIC, CTD-Chemicals, ChEBI, HP, CTD-Anatomy and Gene Ontology-Biological Process) that may partially represent a given NIL entity. We also make available a new evaluation dataset designated by EvaNIL, suitable for training and evaluating models focusing on the NIL entity linking task. This dataset contains 846,165 documents (abstracts and full-text biomedical articles), including 1,071,776 annotations, distributed by six different partitions: EvaNIL-MEDIC, EvaNIL-CTD-Chemicals, EvaNIL-ChEBI, EvaNIL-HP, EvaNIL-CTD-Anatomy and EvaNIL-Gene Ontology-Biological Process. NILINKER was integrated into a graph-based Named Entity Linking model (REEL) and the results of the experiments show that this approach is able to increase the performance of the Named Entity Linking model.
KEST: A graph-based keyphrase extraction technique for tweets summarization using Markov Decision Process

Multi-document summarization finds its application in many downstream information retrieval and natural language processing tasks. In the light of recent developments in social media data mining, Tweet summarization has emerged as a fundamental task of automatically detecting important keyphrases from a set of Tweets about current happenings. In the existing literature, the graph-based keyphrase extraction techniques are well-established unsupervised algorithms to capture summaries from dynamically evolving data. We argue that the traditional multi-tweet summarization technique may or may not capture user’s interest-specific keyphrases during tweet summarization. The nature of user-generated factual short-text is different from well-formed descriptive and perceptual long-text due to their repetitive nature. In this context, we introduce a simple yet effective interest-specific keyphrase extraction technique for tweet sumarization as KEST: K ey E xtraction for S ummarization of T weets using Markov Decision Process (MDP). In this research work, we generate a path as evolving chain of highly interconnected words from sub-components in graph of words. We evaluate the effectiveness of our computationally, inexpensive, graph-based, abstractive keyphrase extraction approach over two datasets which we make publicly available. • The patterns for word co-occurrence network evolved from social media data. • The Markov Decision Process over Graph of Words. • Propose transition and controlling parameters to approximate chain of words. • Keyphrase extraction by retaining lexical sequence of words.
Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa

Natural Language Processing (NLP)'s applied nature makes it necessary to select the most effective and robust models.However, just producing slightly higher performance is insufficient; we want to know whether this advantage will carry over to other data sets.Bootstrapped significance tests can indicate that ability.Computing the significance of performance differences has many levels of complexity, though.It can be tedious, especially when the experimental design has many conditions to compare and several runs of experiments.We present BooStSa, a tool that makes it easy to compute significance levels with the BOOtSTrap SAmpling procedure.BooStSa can evaluate models that predict not only standard hard labels but soft labels (i.e., probability distributions over different classes) as well.
IEESWPR: An Integrative Entity Enrichment Scheme for Socially Aware Web Page Recommendation

The World Wide Web is the most extensive knowledge repository and the vastest record structure that has ever existed in human history. A socially aware and semantically driven web page recommendation algorithm is therefore a compelling necessity. In this paper, an entity enrichment mechanism for recommendation of web pages has been proposed. The approach incorporates semantic frame matching and entities enriched by generation of Resource Description Framework along with the incorporation of background knowledge from the Linked Open Data cloud, social awareness is incorporated by including entities from the Twitter API. The dataset is classified using the XGBoosting algorithm, and the SemantoSim measure has been chosen to compute the semantic similarity under LION optimization algorithm which serves as the metaheuristics. The approach considers the user query, the current user clicks as well as web usage data of the user. The proposed methodology yields an accuracy of 95.42% which surpasses the existing techniques.
CharmSeeker: Automated Pipeline Configuration for Serverless Video Processing

Video processing plays an essential role in a wide range of cloud-based applications. It typically involves multiple pipelined stages, which well fits the latest fine-grained serverless computing paradigm if properly configured to match the cost and delay constraints of video. Existing configuration tools, however, are primarily developed for traditional virtual machine clusters with general workloads. This paper presents CharmSeeker, an automated configuration tuning tool for serverless video processing pipelines. We first carefully examine the key steps and the performance bottlenecks for video processing over modern serverless platforms. Then, we identify the configuration space for processing pipelines and leverage a carefully designed Sequential Bayesian Optimization search scheme to identify promising configurations. We further address the practical challenges toward integrating our solution into real-world systems and develop a prototype with AWS Lambda. Evaluation results show that CharmSeeker can find out the optimal or near-optimal configurations that improve the relative processing time up to 408.77%. It is also more robust and scalable to various video processing pipelines compared with state-of-the-art solutions.
Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers

To maximize the accuracy and increase the overall acceptance of text classifiers, we propose a framework for the efficient, inoperation moderation of classifiers' output.Our framework focuses on use cases in which F1-scores of modern Neural Networks classifiers (ca.90%) are still inapplicable in practice.We suggest a semi-automated approach that uses prediction uncertainties to pass unconfident, probably incorrect classifications to human moderators.To minimize the workload, we limit the human moderated data to the point where the accuracy gains saturate and further human effort does not lead to substantial improvements.A series of benchmarking experiments based on three different datasets and three state-of-the-art classifiers show that our framework can improve the classification F1scores by 5.1 to 11.2% (up to approx.98 to 99%), while reducing the moderation load up to 73.3% compared to a random moderation.
Arabic Quran Verses Authentication Using Deep Learning and Word Embeddings

Nowadays, with the developments witnessed by the Internet, algorithms have come to control all aspects of digital content. Due to its Arabic roots, it is ironic to find that Arabic Quranic content is still thirsty to benefit from computer linguistics, especially with the advent of artificial intelligence algorithms. The massive spread of Islamic-typed websites and applications has led to a widespread of digital Quranic content. Unfortunately, such content lacks censorship and can rarely match resourcefulness. It is quite difficult, especially for a non-native speaker of the Arabic language, to distinguish and authenticate the provided Quranic verses from the non-Quranic Arabic texts. Text processing techniques classified outside the field of Natural Language Processing (NLP) give less qualified results, especially with Arabic texts. To address this problem, we propose to explore Word Embeddings (WE) with Deep Learning (DL) techniques to identify Quranic verses in Arabic textual content. The proposed work is evaluated using twelve different word embeddings models with two popular classifiers for binary classification, namely: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The experimental results showed the superiority of the proposed approach over traditional methods in distinguishing between the Quranic verses and the Arabic text with an accuracy of 98.33%.
Optimization of K-Means Clustering with Modified Spiral Phenomena

The technique of distributing a set of natural entities into groups of related objects is called clustering. Trying to find a good search platform for data mining has incrementally become a key problem as most conventional search methodologies are still unable to contribute to the improvement of knowledge discovery. Spiral optimization (SO) seems to be an accurate functioning algorithm that uses natural processes including the spiralling pattern and pressurized to treasure an optimal result in a reasonable period of time. A novel SO is introduced in this paper to tackle the clustering dilemma. With the exception of the actual SO, that recursively spins the objects across the self-righteous centre, the developed system divides the population into many subgroups to maximize searching heterogeneity and thereby boost the result of cluster analysis. The procedure of k-means has also been utilized to improve the usefulness of the anticipated algorithm. To assess the proposed method’s accuracy, we introduce it to the problem of clustering and measure the findings through the optimization using spiral strategy and clustering using k-means algorithm. The outcomes illustrate that the suggested procedure is very optimistic.
Novel Clustering-Based Web Service Recommendation Framework

Normally web services are classified originate in on the quality of service, wherever the term quality is not absolute and it is a relative term. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, availability, etc. However, the limitation of these methods is that they are producing similar web services in recommendation lists some times. To address this research problem, the novel improved the Clustering-based web service recommendation method is proposed in this project. This approach is mainly dealing to produce diversity in the results of web service recommendation. In this method, functional interest, QoS preference, and diversity features are combined to produce the unique recommendation list of web services to end-users. To produce the unique recommendation results, we proposed a vary web service classify order that is clustering-based on web services' functional relevance such as non-useful pertinence, recorded client intrigue importance, potential client intrigue significance, etc.
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training

Robust Reinforcement Learning (RL) focuses on improving performances under model errors or adversarial attacks, which facilitates the real-life deployment of RL agents. Robust Adversarial Reinforcement Learning (RARL) is one of the most popular frameworks for robust RL. However, most of the existing literature models RARL as a zero-sum simultaneous game with Nash equilibrium as the solution concept, which could overlook the sequential nature of RL deployments, produce overly conservative agents, and induce training instability. In this paper, we introduce a novel hierarchical formulation of robust RL -- a general-sum Stackelberg game model called RRL-Stack -- to formalize the sequential nature and provide extra flexibility for robust training. We develop the Stackelberg Policy Gradient algorithm to solve RRL-Stack, leveraging the Stackelberg learning dynamics by considering the adversary's response. Our method generates challenging yet solvable adversarial environments which benefit RL agents' robust learning. Our algorithm demonstrates better training stability and robustness against different testing conditions in the single-agent robotics control and multi-agent highway merging tasks.