Reviewerly/exHarmony huggingface dataset:
Citation Intent Classification Using Word Embedding

Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations. Deep analysis of citation may reveal interesting findings by utilizing deep neural network algorithms. The existing scholarly datasets are best suited for statistical approaches but lack citation context, intent, and section information. Furthermore, the datasets are too small to be used with deep learning approaches. For citation intent analysis, the datasets must have a citation context labeled with different citation intent classes. Most of the datasets either do not have labeled context sentences, or the sample is too small to be generalized. In this study, we critically investigated the available datasets for citation intent and proposed an automated citation intent technique to label the citation context with citation intent. Furthermore, we annotated ten million citation contexts with citation intent from Citation Context Dataset (C2D) dataset with the help of our proposed method. We applied Global Vectors (GloVe), Infersent, and Bidirectional Encoder Representations from Transformers (BERT) word embedding methods and compared their Precision, Recall, and F1 measures. It was found that BERT embedding performs significantly better, having an 89% Precision score. The labeled dataset, which is freely available for research purposes, will enhance the study of citation context analysis. Finally, It can be used as a benchmark dataset for finding the citation motivation and function from in-text citations.
Designing Online Platforms Supporting Emotions and Awareness

Due to the global pandemic (COVID-19) currently facing humanity, a new environment that promotes teaching–learning is now emerging. This environment that challenges traditional teaching practices created an opportunity for the technology industry to capitalize on by developing creative e-learning platforms that empowers the teaching–learning process, during this ‘emergency’ situation. E-learning scenario is an important element to be considered, as it offers a host of benefits, such as reducing costs, allowing for education on-demand, etc., to its adopters. However, the application of this could bring some challenges, as some of the existing online platforms are not conducive to support clear communication among academic staff. This paper describes a report identifying the main problems faced by teachers and students from different countries in Latin America, when using e-learning platforms in a lockdown scenario, reporting the importance to include aspects related with emotions and awareness.
Impact of contact tracing on COVID-19 mortality: An impact evaluation using surveillance data from Colombia

Background Contact tracing is a crucial part of the public health surveillance toolkit. However, it is labor-intensive and costly to carry it out. Some countries have faced challenges implementing contact tracing, and no impact evaluations using empirical data have assessed its impact on COVID-19 mortality. This study assesses the impact of contact tracing in a middle-income country, providing data to support the expansion and optimization of contact tracing strategies to improve infection control. Methods We obtained publicly available data on all confirmed COVID-19 cases in Colombia between March 2 and June 16, 2020. (N = 54,931 cases over 135 days of observation). As suggested by WHO guidelines, we proxied contact tracing performance as the proportion of cases identified through contact tracing out of all cases identified. We calculated the daily proportion of cases identified through contact tracing across 37 geographical units (32 departments and five districts). Further, we used a sequential log-log fixed-effects model to estimate the 21-days, 28-days, 42-days, and 56-days lagged impact of the proportion of cases identified through contact tracing on daily COVID-19 mortality. Both the proportion of cases identified through contact tracing and the daily number of COVID-19 deaths are smoothed using 7-day moving averages. Models control for the prevalence of active cases, second-degree polynomials, and mobility indices. Robustness checks to include supply-side variables were performed. Results We found that a 10 percent increase in the proportion of cases identified through contact tracing is related to COVID-19 mortality reductions between 0.8% and 3.4%. Our models explain between 47%-70% of the variance in mortality. Results are robust to changes of specification and inclusion of supply-side variables. Conclusion Contact tracing is instrumental in containing infectious diseases. Its prioritization as a surveillance strategy will substantially impact reducing deaths while minimizing the impact on the fragile economic systems of lower and middle-income countries. This study provides lessons for other LMIC.
Lags in the release, adoption, and propagation of npm vulnerability fixes

Abstract Security vulnerability in third-party dependencies is a growing concern not only for developers of the affected software, but for the risks it poses to an entire software ecosystem, e.g., Heartbleed vulnerability. Recent studies show that developers are slow to respond to the threat of vulnerability, sometimes taking four to eleven months to act. To ensure quick adoption and propagation of a release that contains the fix ( fixing release ), we conduct an empirical investigation to identify lags that may occur between the vulnerable release and its fixing release ( package-side fixing release ). Through a preliminary study of 231 package-side fixing release of npm projects on GitHub, we observe that a fixing release is rarely released on its own, with up to 85.72% of the bundled commits being unrelated to a fix. We then compare the package-side fixing release with changes on a client-side ( client-side fixing release ). Through an empirical study of the adoption and propagation tendencies of 1,290 package-side fixing releases that impact throughout a network of 1,553,325 releases of npm packages, we find that stale clients require additional migration effort, even if the package-side fixing release was quick (i.e., package-side fixing releasetypeSpatch). Furthermore, we show the influence of factors such as the branch that the package-side fixing release lands on and the severity of vulnerability on its propagation. In addition to these lags we identify and characterize, this paper lays the groundwork for future research on how to mitigate propagation lags in an ecosystem.
The role of cross-silo federated learning in facilitating data sharing in the agri-food sector

Data sharing remains a major hindering factor when it comes to adopting emerging AI technologies in general, but particularly in the agri-food sector. Protectiveness of data is natural in this setting: data is a precious commodity for data owners, which if used properly can provide them with useful insights on operations and processes leading to a competitive advantage. Unfortunately, novel AI technologies often require large amounts of training data in order to perform well, something that in many scenarios is unrealistic. However, recent machine learning advances, e.g. federated learning and privacy-preserving technologies, can offer a solution to this issue via providing the infrastructure and underpinning technologies needed to use data from various sources to train models without ever sharing the raw data themselves. In this paper, we propose a technical solution based on federated learning that uses decentralized data, (i.e. data that are not exchanged or shared but remain with the owners) to develop a cross-silo machine learning model that facilitates data sharing across supply chains. We focus our data sharing proposition on improving production optimization through soybean yield prediction, and provide potential use-cases that such methods can assist in other problem settings. Our results demonstrate that our approach not only performs better than each of the models trained on an individual data source, but also that data sharing in the agri-food sector can be enabled via alternatives to data exchange, whilst also helping to adopt emerging machine learning technologies to boost productivity.
Motivations and Limits for COVID-19 Policy Compliance in Germany and Switzerland

In contrast to neighboring countries, German and Swiss authorities refrained from general curfews during the first pandemic wave in spring 2020, calling for solidarity and personal responsibility instead. Using a qualitative methodology, this study aims to explore why people in Germany and Switzerland were motivated to comply with policy measures during the first wave of the coronavirus disease 2019 (COVID-19) pandemic, and what factors hindered or limited their motivation. While quantitative surveys can measure the level of compliance, or broadly ask what motives people had for compliance, we here strive to explain why and how these motives lead to compliance.This publication has been made possible by the joint work of the members of the "Solidarity in times of pandemics" (SolPan) research commons. Seventy-seven semi-structured qualitative interviews were conducted with members of the general public in Germany (n = 46) and the German-speaking part of Switzerland (n = 31) in April 2020. Interviews were transcribed and analyzed following a grounded theory approach.Three themes were identified that summarize factors contributing to compliant or noncompliant behavior. (1) Social cohesion was, on the one hand, an important motivator for compliance, but at the same time related to conflicting needs, illustrating the limits of compliance. (2) Consequences were considered on both the individual level (eg, consequences of individual infection) and societal level (eg, the societal and economic consequences of restrictions). (3) While for some participants following the rules was perceived as a matter of principle, others stressed the importance of making their own risk assessment, which was often associated with with a need for evidence on the effectiveness and reasons behind measures.A variety of motives contribute to COVID-19 related compliance. Authorities should seek to address these multi-faceted aspects to support motivation for compliance in a large proportion of the population.
A Formal Specification Smart-Contract Language for Legally Binding Decentralized Autonomous Organizations

Blockchain-and smart-contract technology enhance the effectiveness and automation of business processes.The rising interest in the development of decentralized autonomous organizations (DAO) shows that blockchain technology has the potential to reform business and society.A DAO is an organization wherein business rules are encoded in smart-contract programs that are executed when specified rules are met.The contractual-and business semantics are sine qua non for drafting a legally-binding smart contract in DAO collaborations.Several smart-contract languages (SCLs) exist, such as SPESC, or Symboleo to specify a legally-binding contract.However, their primary focus is on designing and developing smart contracts with the cooperation of IT-and non-IT users.Therefore, this paper fills a gap in the state of the art by specifying a smart-legal-contract markup language (SLCML) for legal-and business constructs to draft a legally-binding DAO.To achieve the paper objective, we first present a formal SCL ontology to describe the legal-and business semantics of a DAO.Secondly, we translate the SCL ontology into SLCML, for which we present the XML schema definition.We demonstrate and evaluate our SLCML language through the specification of a real life-inspired Sale-of-Goods contract.Finally, the SLCML use-case code is translated into Solidity to demonstrate its feasibility for blockchain platform implementations.
Grammarly as AI-powered English Writing Assistant: Students’ Alternative for Writing English

&lt;p class="AbstractText"&gt;The presence of ‘Grammarly’ as one of the online grammar checkers as the impact of technology development. This paper aims to reveal an overview of ‘Grammarly’ as an AI-powered English Writing Assistant for EFL students in Writing English. This research applies descriptive qualitative research. Based on the analysis, using Grammarly software shows the performance increased. Before using Grammarly, the performance of the test score is 34 out of 100. After using Grammarly, the performance text score is 77 out of 100. This score shows the quality of writing in this text increased. The performance can be increased based on Grammarly's suggestions in a Premium account. The researcher recommends the students to use Grammarly. Grammarly is a web tool to perform grammar checks well, starting from the spelling of words, sentence structure to standard grammar. Grammarly is free, so it is recommended for students who want to check various documents or articles in English. Grammarly helps check the grammatical rule, the spelling rule in English structure, also correct errors in writing such as punctuation and capitalization. Grammarly runs on an Artificial Intelligence (AI) system, which is built to analyze English sentences relying on a set of rules. Grammarly takes context when showing corrections or suggestions, and inform the students quickly but still precisely. For accuracy, two service options available both free and paid features. Of course, the Grammarly free version still has limitations and in-service features, unlike the paid version (premium) which has full advantages and benefits, many features, and complete.&lt;/p&gt;
Drawing inspiration from biological dendrites to empower artificial neural networks

This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adopt them in artificial neurons in order to exploit the respective benefits in machine learning.
Intelligent Sports Training System Based on Artificial Intelligence and Big Data

All activities in training fields are for the improvement of athletes’ competitive abilities. A sports training system is an organizational system to achieve common goals. Competitive ability is one of the main manifestations of the evolution of the training system. With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports-assisted training to eliminate traditional sports training that relied purely on experience. Pose estimation obtains the position, angle, and additional information about the human body in the image in a two-dimensional plane or three-dimensional space by establishing the mapping relationship between the human body features and the human body posture. This article demonstrates a golf-assisted training system to realize the transformation from an experience-based sports training method to a human motion analysis method, using artificial intelligence and big data. The swing posture parameters of the trainer and the coach are obtained using the posture estimation of a human body. Based on this information, an auxiliary training system is built. The two parameters of the joint angle trajectory and the posture similarity are used as auxiliary indicators to compare the trainers. The joint angle trajectory is analyzed, and the coach is guided based on the similarity of the posture.
18.8 Gbps real-time quantum random number generator with a photonic integrated chip

Quantum random number generators (QRNGs) can produce true random numbers. Yet, the two most important QRNG parameters highly desired for practical applications, i.e., speed and size, have to be compromised during implementations. Here, we present the fastest and miniaturized QRNG with a record real-time output rate as high as 18.8 Gbps by combining a photonic integrated chip and the technology of optimized randomness extraction. We assemble the photonic integrated circuit designed for vacuum state QRNG implementation, an InGaAs homodyne detector, and a high-bandwidth transimpedance amplifier into a single chip using hybrid packaging, which exhibits the excellent characteristics of integration and high-frequency response. With a sample rate of 2.5 GSa/s in a 10-bit analog-to-digital converter and subsequent paralleled postprocessing in a field programmable gate array, the QRNG outputs ultrafast random bitstreams via a fiber optic transceiver, whose real-time speed is validated in a personal computer.
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation

Models pretrained with self-supervised objectives on large text corpora achieve state-of-theart performance on English text summarization tasks.However, these models are typically fine-tuned on hundreds of thousands of data points, an infeasible requirement when applying summarization to new, niche domains.In this work, we introduce a novel and generalizable method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner.WikiTransfer fine-tunes pretrained models on pseudo-summaries, produced from generic Wikipedia data, which contain characteristics of the target dataset, such as the length and level of abstraction of the desired summaries.WikiTransfer models achieve state-ofthe-art, zero-shot abstractive summarization performance on the CNN-DailyMail dataset and demonstrate the effectiveness of our approach on three additional diverse datasets.These models are more robust to noisy data and also achieve better or comparable few-shot performance using 10 and 100 training examples when compared to few-shot transfer from other summarization datasets.To further boost performance, we employ data augmentation via round-trip translation as well as introduce a regularization term for improved few-shot transfer.To understand the role of dataset aspects in transfer performance and the quality of the resulting output summaries, we further study the effect of the components of our unsupervised fine-tuning data and analyze few-shot performance using both automatic and human evaluation.
Blockchain-Based Security Framework for a Critical Industry 4.0 Cyber-Physical System

There has been an intense concern for security alternatives because of the recent rise of cyber attacks, mainly targeting critical systems such as industry, medical, or energy ecosystem. Though the latest industry infrastructures largely depend on AI-driven maintenance, the prediction based on corrupted data undoubtedly results in loss of life and capital. Admittedly, an inadequate data-protection mechanism can readily challenge the security and reliability of the network. The shortcomings of the conventional cloud or trusted certificate-driven techniques have motivated us to exhibit a unique Blockchain-based framework for a secure and efficient industry 4.0 system. The demonstrated framework obviates the long-established certificate authority after enhancing the consortium Blockchain that reduces the data processing delay, and increases cost-effective throughput. Nonetheless, the distributed industry 4.0 security model entails cooperative trust than depending on a single party, which in essence indulges the costs and threat of the single point of failure. Therefore, multi-signature technique of the proposed framework accomplishes the multi-party authentication, which confirms its applicability for the real-time and collaborative cyber-physical system.
A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis

Multimodal fusion is a core problem for multimodal sentiment analysis.Previous works usually treat all three modal features equally and implicitly explore the interactions between different modalities.In this paper, we break this kind of methods in two ways.Firstly, we observe that textual modality plays the most important role in multimodal sentiment analysis, and this can be seen from the previous works.Secondly, we observe that comparing to the textual modality, the other two kinds of nontextual modalities (visual and acoustic) can provide two kinds of semantics, shared and private semantics.The shared semantics from the other two modalities can obviously enhance the textual semantics and make the sentiment analysis model more robust, and the private semantics can be complementary to the textual semantics and meanwhile provide different views to improve the performance of sentiment analysis together with the shared semantics.Motivated by these two observations, we propose a text-centered shared-private framework (TCSP) for multimodal fusion, which consists of the cross-modal prediction and sentiment regression parts.Experiments on the MOSEI and MOSI datasets demonstrate the effectiveness of our shared-private framework, which outperforms all baselines.Furthermore, our approach provides a new way to utilize the unlabeled data for multimodal sentiment analysis.
Detecting Harmful Memes and Their Targets

Among the various modes of communication in social media, the use of Internet memes has emerged as a powerful means to convey political, psychological, and socio-cultural opinions.Although memes are typically humorous in nature, recent days have witnessed a proliferation of harmful memes targeted to abuse various social entities.As most harmful memes are highly satirical and abstruse without appropriate contexts, off-the-shelf multimodal models may not be adequate to understand their underlying semantics.In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.To this end, we present HarMeme, the first benchmark dataset, containing 3, 544 memes related to COVID-19.Each meme went through a rigorous two-stage annotation process.In the first stage, we labeled a meme as very harmful, partially harmful, or harmless; in the second stage, we further annotated the type of target(s) that each harmful meme points to: individual, organization, community, or society/general public/other.The evaluation results using ten unimodal and multimodal models highlight the importance of using multimodal signals for both tasks.We further discuss the limitations of these models and we argue that more research is needed to address these problems.
When Do You Need Billions of Words of Pretraining Data?

NLP is currently dominated by language models like RoBERTa which are pretrained on billions of words.But what exact knowledge or skills do Transformer LMs learn from large-scale pretraining that they cannot learn from less data?To explore this question, we adopt five styles of evaluation: classifier probing, information-theoretic probing, unsupervised relative acceptability judgments, unsupervised language model knowledge probing, and fine-tuning on NLU tasks.We then draw learning curves that track the growth of these different measures of model ability with respect to pretraining data volume using the MiniBERTas, a group of RoBERTa models pretrained on 1M, 10M, 100M and 1B words.We find that these LMs require only about 10M to 100M words to learn to reliably encode most syntactic and semantic features we test.They need a much larger quantity of data in order to acquire enough commonsense knowledge and other skills required to master typical downstream NLU tasks.The results suggest that, while the ability to encode linguistic features is almost certainly necessary for language understanding, it is likely that other, unidentified, forms of knowledge are the major drivers of recent improvements in language understanding among large pretrained models.