Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking

In automated scientific fact-checking, machine learning models are trained to verify scientific claims given evidence. A major bottleneck of this task is the availability of large-scale training datasets on different domains, due to the required domain expertise for data annotation. However, multiple-choice question-answering datasets are readily available across many different domains, thanks to the modern online education and assessment systems. As one of the first steps towards addressing the fact-checking dataset scarcity problem in scientific domains, we propose a pipeline for automatically converting multiple-choice questions into fact-checking data, which we call Multi2Claim. By applying the proposed pipeline, we generated two large-scale datasets for scientific-fact-checking: Med-Fact and Gsci-Fact for the medical and general science domains, respectively. These two datasets are among the first examples of large-scale scientific-fact-checking datasets. We developed baseline models for the verdict prediction task using each dataset. Additionally, we demonstrated that the datasets could be used to improve performance measured by weighted F1 on existing fact-checking datasets such as SciFact, HEALTHVER, COVID-Fact, and CLIMATE-FEVER. In some cases, the improvement in performance was up to a 26% increase. The generated datasets are publicly available. © 2023 Association for Computational Linguistics.

Challenges in the construction of a tunnel under crossing a channel in Singapore

To increase the conveyance capacity to Western Singapore and to meet long-term water needs in a more cost-effective manner, four new transmission pipelines consisting of 2 numbers of 2200 mm diameter and 2 numbers of 1200mm diameter water pipes will be needed by 2024 to convey water from a Water Reclamation Plant to existing networks in the western region of Singapore. Out of the several possible routes studied, the most cost-effective and technically feasible route was selected by laying the proposed 1.6km-long pipelines that under crosses a channel via a 6m diameter subsea tunnel. This paper outlines the challenges the team faced throughout the project thus far. It also examines the difficulties such as the construction of a 56m-deep launching shaft near a highly sensitive 700mm diameter Gas Transmission Pipeline (GTP) and at a location with high groundwater;and manpower and supply disruptions caused by the COVID-19 pandemic situation. © 2023 The Author(s).

Streaming Big Data with Open-source: A Comparative Study and Architectural Recommendations

This study focusses on providing state-of-the-art infrastructure for data pipelines in e-Commerce sector, especially for online stores. With people going digital and also latest impact of Covid-19, daily e-Commerce companies are dealing with large amount of data (terabytes to petabytes). With growing Internet of Things, systems of computing devices which are interrelated. The inter-relation may be between mechanical and digital machines, objects or people. The interrelated objects will be provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Growth of big data poses several challenges and opportunities in every field of its usage. Realtime analysis of data and its inference gives a competitive edge over its partners in every business field especially in e-commerce. Recent advances in technology and tools have exposed new opportunities to get actionable insights from historical data like market data, customer demographics, along with real-time data. Advancement in distributed streaming technology makes it important to investigate existing streaming data pipeline capabilities in eCommerce sector with a focus on online stores. This study analyzes the published research works on streaming data pipelines in e-commerce sector also to facilitate e-commerce's variety of data streaming applications requirement. A state-of-the-art lambda architecture for streaming is proposed completely based on open-source technologies. Challenge in proprietary owned streaming platforms are vendor lock-in, limited ability to customize, cost, limited innovation & support. Proposed reference architecture will address many streaming use cases compared to its competitors, it has support of large open-source community in providing the inter-operability between streaming & related technologies like connectors, apart from providing better performance apart from other open-source based product advantages. © 2023 IEEE.

TOBACCO USE IN STUDENTS OF THE THIRD FACULTY OF MEDICINE OF CHARLES UNIVERSITY IN THE CZECH REPUBLIC

Objectives: This study assessed trends in tobacco use in students of the Third Faculty of Medicine of Charles University in the Czech Republic between academic years 2012/13 and 2019/2020. Methods: Two cross-sectional surveys designed to obtain information on smoking history, smoking status, tobacco products use, and cessation were conducted among 382 students of the 6-year Master's Study Programme (General Medicine) and the 3-year Bachelor's Study Programme (Public Health) in 2012/2013;and among 580 students of General Medicine and of the Bachelor's Study Programmes (Public Health, Dental Hygiene and Nursing) in 2019/2020. Results: Regular/daily smoking was reported by 4.4 ± 2.4% (with 95% CI) of General Medicine students and 4.8 ± 4.1% of Public Health students in 2012/2013, and 1.3 ± 1.1% of General Medicine students and 14.4 ± 4.8% of students of bachelor studies in 2019/2020. The share of regular and occasional smokers was higher among junior students in both academic years (23.9 ± 5.1% and 20.1 ± 4.7%, respectively) compared to senior students (23.6 ± 9.8% and 9.6 ± 5.7%). Cigarettes were the most common products used in both academic years (67.0 ± 4.7% and 45.5 ± 4.0%). There was a significant increase in proportion of students using more tobacco products in the course of the time (from 12.1 ± 3.1% to 53.7 ± 4.1%). The proportion of students who quitted smoking has risen from 11.4 ± 3.2% to 16.1 ± 3.0%. On the contrary, the proportion of students who started smoking has dropped from 15.9 ± 3.7% to 2.9 ± 1.4%. The proportion of non-smokers has risen from 57.6 ± 5.0% to 65.3 ± 3.9%. Conclusions: The study revealed some positive trends concerning tobacco use in students (decline in regular smokers among students of General Medicine, senior students, cigarette smokers, water pipe smokers;rise in non-smokers), but also negative ones (rise in regular smokers among students of Public Health, students who used more tobacco products).

Drum Up SUPPORT: Systematic Analysis of Image-Schematic Conceptual Metaphors

Conceptual metaphors represent a cognitive mechanism to transfer knowledge structures from one onto another domain. Image-schematic conceptual metaphors (ISCMs) specialize on transferring sensorimotor experiences to domains. Natural language is believed to provide evidence of such metaphors. However, approaches to verify this hypothesis largely rely on top-down methods, gathering examples by way of introspection, or on manual corpus analyses. In order to contribute towards a method that is systematic and can be replicated, we propose to bring together existing processing steps in a pipeline to detect ISCMs, exemplified for the image schema SUPPORT in the COVID-19 domain. This pipeline consists of neural metaphor detection, dependency parsing to uncover construction patterns, clustering, and BERT-based frame annotation of dependent constructions to analyze ISCMs. © 2022 Association for Computational Linguistics.

Multi-National Topics Maps for Parliamentary Debate Analysis

In recent years, automated political text processing became an indispensable requirement for providing automatic access to political debate. During the Covid-19 worldwide pandemic, this need became visible not only in social sciences but also in public opinion. We provide a path to operationalize this need in a multi-lingual topic-oriented manner. Using a publicly available data set consisting of parliamentary speeches, we create a novel process pipeline to identify a good reference model and to link national topics to the cross-national topics. We use design science research to create this process pipeline as an artifact. © 2022 IEEE Computer Society. All rights reserved.

Tune Down the Misinformation, Please: Generating Corrective Messages for COVID-19 Misinformation

The ongoing COVID-19 pandemic drastically changed our lives in multiple aspects, one of which is the reliance on social media during quarantine, both for social interaction and information-seeking purposes. However, the wide dissemination of misinformation on social media has impacted public health negatively. Previous studies on COVID-19 misinformation mainly focused on exploration of impacts and explanation of motivations, with few exceptions. In this study, we propose an analytical pipeline that generates corrective messages toward COVID-19 misinformation in a semiautomatic fashion, and then evaluate it against a large amount of data. Both the automated and manual evaluation results suggest the efficiency of the proposed pipeline, which can be used in combination with human intelligence by individuals and public health organizations in fighting COVID-19 misinformation. © 2022 IEEE Computer Society. All rights reserved.

The Ukraine Crisis and Indo-Pacific Geopolitics

While the two statements available on the websites vary in length - the Russian statement runs into 5000 words,2 while the Chinese statement is crisper and is about 1200 words.3 Another difference between the two statements on the websites posts about the Putin-Xi meeting is that Russia calls out countries by names for being disruptors of peace in the international system, while China without mentioning countries' names, talks about disruptors to peace in the international system. [...]Russia states how the trilateral security partnership between Australia, the US and the UK (AUKUS) is a concerning development in international relations, how Japan's plans on the destroyed Fukushima nuclear plant are deeply concerning, how the U.S. plans in the Asia-Pacific and in the European regions are "risks to international and regional security", how Russia and China through the Brazil, Russia, India, China and South Africa (BRICS) grouping, aim at deepened strategic partnerships and how the Shanghai Cooperation Organization (SCO) aims at enhancing a "polycentric world order". On the eve of the 2013 state visit of Xi to Moscow, he remarked that the two sides were forging a "special relationship". Landmark contracts were signed in 2015 for the sale of Su-35 combat aircraft and S-400 air defense systems worth USD 5 billion.10 There have also been a series of transactions involving the transfer of helicopters, submarine technology and aircraft engines.

A Meta-framework for Spatiotemporal Quantity Extraction from Text

News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic. © 2022 Association for Computational Linguistics.

Energy-based Domain Adaption with Active Learning for Emerging Misinformation Detection

Classifying whether collected information related to emerging topics and domains is fake/incorrect is not an easy task because we do not have enough labeled data in the domains. Given labeled data from source domains (e.g., gossip and health) and limited labeled data from a newly emerging target domain (e.g., COVID-19 and Ukraine war), simply applying knowledge learned from source domains to the target domain may not work well because of different data distribution. To solve the problem, in this paper, we propose an energy-based domain adaptation with active learning for early misinformation detection. Given three real world news datasets, we evaluate our proposed model against two baselines in both domain adaptation and the whole pipeline. Our model outperforms the baselines, improving at least 5% in the domain adaptation task and 10% in the whole pipeline, showing effectiveness of our proposed approach. © 2022 IEEE.

IR-ER- A Hybrid Pipeline for Classifying COVID-19 RNA Seq Data

Bioinformatics has numerous approaches for evaluating the similarities between RNA-seq data for disease classification. Processing RNA-sequencing (RNA-seq) data using clustering or classification approach is extremely challenging, although analysis of ribonucleic acid (RNA-Seq) helps understand differentially expressed genes and classify the patient in a risk-free method. In this study, we present a hybrid end-to-end pipeline for analyzing, processing, and classifying the RNA-Seq data with a major focus on the covid-19 data set. The pipeline has been developed in three phases initially the raw data is normalized. Then the normalized data is pushed to a colonization algorithm to remove the noise data. The optimized data set is passed to a Deep Learning (DL) classifier. Further, a comparative analysis is performed with state of art methods discussed in the literature. The results prove that our proposed hybrid pipeline achieved the best accuracy over other methods. Gene set enrichment analysis was also performed to analyze the genes that are informative towards COVID-19 identification. © 2023 ACM.

A Citizen Science Approach for Analyzing Social Media With Crowdsourcing

Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new approach for the analysis of social media posts, based on configurable automatic classification combined with Citizen Science methodologies. The process is facilitated by a set of flexible, automatic and open-source data processing tools called the Citizen Science Solution Kit. The kit provides a comprehensive set of tools that can be used and personalized in different situations, particularly during natural emergencies, starting from images and text contained in the posts. The tools can be employed by citizen scientists for filtering, classifying, and geolocating the content with a human-in-the-loop approach to support the data analyst, including feedback and suggestions on how to configure the automated tools, and techniques to gather inputs from citizens. Using flooding scenario as a guiding example, this paper illustrates the structure and functioning of the different tools proposed to support citizens scientists in their projects, and a methodological approach to their use. The process is then validated by discussing three case studies based on the Albania earthquake of 2019, the Covid-19 pandemic, and the Thailand floods of 2021. The results suggest that a flexible approach to tools composition and configuration can support a timely setup of an analysis project by citizen scientists, especially in case of emergencies in unexpected locations. © 2013 IEEE.

ECG-CCNet: Cardiovascular(Cardiac) and COVID19 Disease Classification Using Deep Convolutional Neural Network Learning Pipeline Approaches from Electrocardiography(ECG)- A Study

Experimental studies demonstrate that COVID-19 illness affects the cardiovascular as well as the pulmonary / lung tract. The limits of existing COVID-19 diagnostic procedures have been revealed. In contrast, to present diagnoses, such as low-sensitivity conventional RT-PCR testing and costly healthcare scanning equipment, implementing additional approaches for COVID-19 illness assessment would be advantageous for COVID-19 epidemic management. Furthermore, problems generated by COVID-19 on the cardiovascular tract must be detected rapidly and precisely using ECG. Considering the numerous advantages of electrocardiogram (ECG) functionalities, the proposed study offers a novel pipeline termed ECG-CCNet for examining the feasibility of employing ECG pulses to diagnose COVID-19. This study is a two-phase transfer learning (TL) approach is suggested for the prognosis of COVID-19 disorder, which includes feature mining utilizing DCNNs models and ensemble pipelining using ECG tracing imageries generated from ECG signals of COVID-19 diseased sufferers relying on the anomalies induced by COVID-19 pathogen on cardiovascular structures. A complete classification performance of 93.5% accuracy, 87% recall, 87.03% F1-score, 95.66% specificity, 87.16% precision, and 95.33% AUC attained by abnormal heartbeats, COVID-19, myocardial, and normal/healthy classification. This experiment is considered a high possibility for speeding up the diagnostic and treatments of COVID-19 individuals, reducing practitioners' efforts, and improving epidemic containment by utilizing ECG data. © 2022 IEEE.

A Systematic Literature Review on Risk Analysis of Health and Safety in Oil Refinery and Onshore Pipeline Construction Projects

Oil industry construction is a very high risk from a safety and health perspective. Thousands of workers die while working in onshore oil refineries and pipeline projects worldwide, and despite many advancements in research and technology, fatal injuries are still happening. Construction products involving oil refineries and pipelines always need successful strategies in mitigating health and safety risks. After the recent Covid-19 pandemic, the industry became more conscious of increasing workers' safety on construction sites. The lack of a comprehensive literature review involving raking and prioritization of critical health and safety risk factors is the reason behind conducting a new secondary study. This study aimed to show the Systematic Literature Review (SLR) on risk analysis of health and safety issues construction workers face in onshore oil refineries and pipeline construction projects. The SLR methodology involved searching and reviewing the most relevant research papers from the perspective of safety risk factors and proven mitigation techniques. The SLR involves 30 research papers that are of high significance from 2011 to 2022. Fifteen health and safety risk factors are ranked according to arguments from previous studies, with falling from height at the top and scaffolding failure at the lowest position. The successful mitigation techniques are discussed in the existing literature, and the study provides positive theoretical and practical implications for the workers in oil refinery and pipeline construction projects. © 2022 IEEE.

Towards MOOCs for Lipreading: Using Synthetic Talking Heads to Train Humans in Lipreading at Scale

Many people with some form of hearing loss consider lipreading as their primary mode of day-to-day communication. However, finding resources to learn or improve one's lipreading skills can be challenging. This is further exacerbated in the COVID19 pandemic due to restrictions on direct interactions with peers and speech therapists. Today, online MOOCs platforms like Coursera and Udemy have become the most effective form of training for many types of skill development. However, online lipreading resources are scarce as creating such resources is an extensive process needing months of manual effort to record hired ac-tors. Because of the manual pipeline, such platforms are also limited in vocabulary, supported languages, accents, and speakers and have a high usage cost. In this work, we investigate the possibility of replacing real human talking videos with synthetically generated videos. Synthetic data can easily incorporate larger vocabularies, variations in accent, and even local languages and many speakers. We propose an end-to-end automated pipeline to develop such a platform using state-of-the-art talking head video generator networks, text-to-speech models, and computer vision techniques. We then perform an extensive human evaluation using carefully thought out lipreading exercises to validate the quality of our designed platform against the existing lipreading platforms. Our studies concretely point toward the potential of our approach in developing a large-scale lipreading MOOC platform that can impact millions of people with hearing loss. © 2023 IEEE.

LLOG Praline Tieback Execution: Fast-Paced Project Execution Delivered by Collaborative Project Management Techniques

Objectives/Scope: The Praline project was awarded and executed during the COVID-19 pandemic and demonstrated how the right project management principles created transparency and trust between Contractor and Company so that both parties worked openly during project execution to successfully execute the scope in the required timeframe. Methods, Procedures, Process: This project showcases how creating the right project environment fostered creative engineering, operational flexibility and collaborative decision making. A gate system was developed which enabled several solutions to run in parallel and focused on key dates for specific decisions which allowed for the final offshore scenario to be vetted. In addition, daily task orientated meetings were held with a small team to communicate updates and plans on specific topics between key stakeholders. All attendees were empowered to make decisions and progress tasks in the meeting. A lean and agile approach was deployed. Results, Observations, Conclusions: The project was able to overcome numerous challenges during a pandemic in a short timeframe by implementing a fast-paced decision-making process which was underpinned by high-level trust between LLOG and Subsea 7. The project shifted from a plan to install the entire pipeline, In-Line Sled (ILS) and Pipeline End Termination (PLET) in a single trip to using multiple assets and mobilisations. The asset changes were required to maintain LLOG's first oil requirements and meant that the design, installation engineering, and procurement activities needed to be condensed and yet still meet high quality and safety standards. The changes in installation assets impacted the structures designs due to the installation equipment and this had a knock-on impact to the procurement and fabrication activities. One example was that the ILS needed to be redesigned to change the mudmat foundation and use an existing foundation from another project. This scope was completed in less than four weeks from the implementation of the vessel change and could only have been done through the transparent and collaborative approach established on the project. © 2022, Offshore Technology Conference. All rights reserved.

Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text

Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure. In addition, the recent COVID-19 pandemic fueled a surge in the use of video conferencing tools. Since videos take up considerable bandwidth ( ∼ 100 Kbps to a few Mbps), improved video compression can have a substantial impact on network performance for live and pre-recorded content, providing broader access to multimedia content worldwide. We present a novel video compression pipeline, called Txt2Vid, which dramatically reduces data transmission rates by compressing webcam videos ('talking-head videos') to a text transcript. The text is transmitted and decoded into a realistic reconstruction of the original video using recent advances in deep learning based voice cloning and lip syncing models. Our generative pipeline achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience based on a subjective evaluation by users ( n=242 ) in an online study. The Txt2Vid framework opens up the potential for creating novel applications such as enabling audio-video communication during poor internet connectivity, or in remote terrains with limited bandwidth. The code for this work is available at https://github.com/tpulkit/txt2vid.git. © 1983-2012 IEEE.

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems;(ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis;(iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC;(iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences. © The Author(s) 2022.

Russian gas in Europe: pipeline and hubs price convergence analysis

The analysis in this paper was performed before the disastrous and unsolicited invasion of Russia to Ukraine. The paper aims to identify if the biggest Russian gas exporter Gazprom used market power to decouple its gas prices from European gas hub benchmarks. The empirical analysis is based on pairwise price convergence between the Russian pipeline and European gas hubs. The main finding shows that Gazprom takes advantage of its market position. The proposed model does not support the company's claims of pipeline price tightness to liquid European gas hubs, and rather proves fluctuating and unstable price convergence between pipelines and hubs from 2016 to March 2020, right before the COVID-19 pandemic. Notably, a robust and trendy-stable price convergence is observed between the Russian pipeline gas and Brent benchmark. Methodologically, the paper contributes with a modified convergence model compliant with gas market fundamentals and suggests a time-expanding concept missed in previous studies. Ongoing political and European gas market developments of 2022 (during the paper review) support the conclusions. © 2022 Taylor & Francis Group, LLC.

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective.

In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing, founded upon advanced communication, computation, and control infrastructure. Cybermanufacturing will unleash the potential of multi-modal manufacturing data, and provide a new perspective called computation service, as a part of service-oriented architecture (SOA), where on-demand computation requests throughout manufacturing operations are seamlessly satisfied by data analytics and machine learning. However, the complexity of information technology infrastructure leads to fundamental challenges in modeling and analysis under cybermanufacturing, ranging from information-poor datasets to a lack of reproducibility of analytical studies. Nevertheless, existing reviews have focused on the overall architecture of cybermanufacturing/SOA or its technical components (e.g., communication protocol), rather than the potential bottleneck of computation service with respect to modeling and analysis. In this paper, we review the fundamental challenges with respect to modeling and analysis in cybermanufacturing. Then, we introduce the existing efforts in computation pipeline recommendation, which aims at identifying an optimal sequence of method options for data analytics/machine learning without time-consuming trial-and-error. We envision computation pipeline recommendation as a promising research field to address the fundamental challenges in cybermanufacturing. We also expect that computation pipeline recommendation can be a driving force to flexible and resilient manufacturing operations in the post-COVID-19 industry.