ABSTRACT
The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their "onboarding"period. Our results reveal that although new hires are gradually expanding networks over time, there still exists significant gaps between their network statistics and those of tenured employees even after the six-month onboarding phase. We also observe that heterogeneity exists among new employees in how their networks change over time, where employees whose job tasks do not necessarily require extensive and diverse connections could be at a disadvantaged position in this onboarding process. By investigating how web-based people recommendations in organizational knowledge base facilitate new employees naturally expand their networks, we also demonstrate the potential of web-based applications for addressing the aforementioned socialization challenges. Altogether, our findings provide insights on new employee network dynamics in remote and hybrid work environments, which may help guide organizational leaders and web application developers on quantifying and improving the socialization experiences of new employees in digital workplaces. © 2023 ACM.
ABSTRACT
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.
ABSTRACT
Accessing large-scale structured datasets such as WDC or CORD-191 is very challenging. Even if one topic (e.g. COVID-19 vaccine efficacy) is of interest, all topical tables in different sources/papers have hundreds of different schemas, depending on the authors, which significantly complicates both finding and querying them. Here we demonstrate a scalable Meta-profiler system, capable of constructing a structured standardized interface to a topic of interest in large-scale (semi-)structured datasets. This interface, that we call Meta-profile represents a multi-dimensional meta-data summary for a selected topic of interest, accumulating all differently structured representations of the topical tables in the dataset. Such Meta-profiles can be used as a rich visualization as well as a robust structural query interface simplifying access to large-scale (semi-)structured data for different user segments, such as data scientists and end users. © 2023 Owner/Author.
ABSTRACT
In the last 2 years, the SARS-CoV-2 (COVID-19) pandemic demonstrated that rapid response to outbreaks with readily effective treatments represents a primary health and societal priority. At the same time, we became conscious that technological resources are often not used in the most efficient manner. The LIGATE and REpurposing MEDIcines For All (REMEDI4ALL) projects started on the large-scale mobilization efforts of the EXaSCale smArt pLatform Against paThogEns (Exscalate4Cov) project with the aim to apply cutting-edge technologies in drug discovery, sustain the fight against future pandemics, and promote the everyday fight against rare diseases. In particular, the LIGATE project, using the drug-discovery platform Exscalate, intends to boost the virtual screening of drug campaigns at an extreme scale in terms of performance and streamline the drug-development process. The aim of the REMEDI4ALL project is to collect sciQ1entific expertise and innovative technology platforms for the repurposing of medicines to treat rare diseases or other pathologic conditions with no current therapy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The Omicron wave is the largest wave of COVID-19 pandemic to date, more than doubling any other in terms of cases and hospitalizations in the United States. In this paper, a large-scale agent-based model of policy interventions that could have been implemented to mitigate the Omicron wave is presented. The model takes into account the behaviors of individuals and their interactions with one another within a nationally representative population, as well as the efficacy of various interventions such as social distancing, mask wearing, testing, tracing, and vaccination. We use the model to simulate the impact of different policy scenarios and evaluate their potential effectiveness in controlling the spread of the virus. The results suggest the Omicron wave could have been substantially curtailed via a combination of interventions comparable in effectiveness to extreme and unpopular singular measures such as widespread closure of schools and workplaces, and highlight the importance of early and decisive action. © 2023 Wiley-VCH GmbH.
ABSTRACT
Since 2000s, Athens has been changed due to imponent urban transformations mainly provoked by the implementation of large-scale works in occasion of the 28th Olympic Games in 2004, then by the dramatic effects caused by the impact of the 2008 economic crisis and the imposition of austerity policies and, finally, by the unbalanced growth of the subsequent economic recovery that abruptly halted with the impact of the Covid-19 pandemic. The city has rapidly changed not only its urban configuration, but also the way it has been perceived and represented. During these years, it can be registered a diffuse interest among architects and artists for the use of collage to represent Athens. While during the previous century in Greece collage was mainly limited to the representation of rural and mythological landscape, since 2000s many collaged images embed for the first time many urban features of the contemporary metropolis. This paper aims to present how collage art had been adopted by architects to describe, analyze, critic, and imagine the city as an attempt to find a proper tool to deal effectively with the emerging urban issues that had arose with the new status of Athens as a metropolitan city. In particular, the association between collage and the city will be investigated in relation with the periods of growth (2000–2008), decay (2008–2015) and economic recovery (2015–2020). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In 2020, the COVID-19 pandemic spread globally, leading to countries imposing health restrictions on people, including wearing masks, to prevent the spread of the disease. Wearing a mask significantly decreases distinguishing ability due to its concealment of the main facial features. After the outbreak of the pandemic, the existing datasets became unsuitable because they did not contain images of people wearing masks. To address the shortage of large-scale masked faces datasets, a developed method was proposed to generate artificial masks and place them on the faces in the unmasked faces dataset to generate the masked faces dataset. Following the proposed method, masked faces are generated in two steps. First, the face is detected in the unmasked image, and then the detected face image is aligned. The second step is to overlay the mask on the cropped face images using the dlib-ml library. Depending on the proposed method, two datasets of masked faces called masked-dataset-1 and masked-dataset-2 were created. Promising results were obtained when they were evaluated using the Labeled Faces in the Wild (LFW) dataset, and two of the state-of-the-art facial recognition systems for evaluation are FaceNet and ArcFace, where the accuracy of using the two systems was 96.1 and 97, respectively with masked-dataset-1 and 87.6 and 88.9, respectively with masked-dataset-2. © 2022 IEEE.
ABSTRACT
During a large-scale epidemic, a local healthcare system can be overwhelmed by a large number of infected and non-infected patients. To serve the infected and non-infected patients well with limited medical resources, effective emergency medical service planning should be conducted before the epidemic. In this study, we propose a two-stage stochastic programming model, which integrally deploys various types of emergency healthcare facilities before an epidemic and serves infected and non-infected patients dynamically at the deployed healthcare facilities during the epidemic. With the service equity of infected patients and various practical requirements of emergency medical services being explicitly considered, our model minimizes a weighted sum of the expected operation cost and the equity cost. We develop two comparison models and conduct a case study on Chengdu, a Chinese city influenced by the COVID-19 epidemic, to show the effectiveness and benefits of our proposed model. Sensitivity analyses are conducted to generate managerial insights and suggestions. Our study not only extends the existing emergency supply planning models but also can facilitate better practices of emergency medical service planning for large-scale epidemics. © Operational Research Society 2023.
ABSTRACT
When the globe was hit by the vicious Covid 19 pandemic, multiple industries faced the virus's wrath and that included the agricultural warehouse industry. Consequently, many warehouses which had received large shipment stocks of agricultural products were never to be used again as it had reached its expiration date. This led to major losses for the agricultural warehouses as well as losses in crops for farmers and large scale agriculturists. The main objective of this paper is to build a model which utilises 3 heavy-weight algorithms (Seasonal Autoregressive Integrated Moving Average-SARIMA, Long short term memory-LSTM and Holt Winters) and predicts the agricultural needs of retailers and consumers based on previous data from different warehouses. Deploying this system will not help in the regulation of goods in warehouses but will also aid in maximizing the profits and minimizing the losses for warehouses. The algorithm with the least MAE(Mean Absolute Error) value will be considered for forecasting the sales of the aforementioned product. © 2022 IEEE.
ABSTRACT
Over the last two years, the COVID-19 pandemic has affected hundreds of millions of people around the world. As in many crises, people turn to social media platforms, like Twitter, to communicate and share information. Twitter datasets have been used over the years in many research studies to extract valuable information. Therefore, several large COVID-19 Twitter datasets have been released over the last two years. However, none of these datasets contains only Portuguese Tweets, despite the Portuguese Language being reported as one of the top five languages used on Twitter. In this paper, we present the first large-scale Portuguese COVID-19 Twitter dataset. The dataset contains over 19 million Tweets spanning 2020 and 2021, allowing the entire pandemic to be analyzed. We also conducted a sentiment analysis on the dataset and correlated the various spikes in Tweet count and sentiment scores to various news articles and government announcements in Portugal and Brazil. The dataset is available at: https://github.com/bioinformatics-ua/Portuguese-Covid19-Dataset © 2022 IEEE.
ABSTRACT
To prevent and curb viral outbreaks, such as COVID-19, it is important to increase vaccination coverage while resolving vaccine hesitancy and refusal. To understand why COVID-19 vaccination coverage had rapidly increased in Japan, we analyzed Twitter posts (tweets) to track the evolution of people's stance on vaccination and clarify the factors of why people decide to vaccinate. We collected all Japanese tweets related to vaccines over a five-month period and classified the vaccination stances of users who posted those tweets by using a deep neural network we designed. Examining diachronic changes in the users' stances on this large-scale vaccine dataset, we found that a certain number of neutral users changed to a pro-vaccine stance while very few changed to an anti-vaccine stance in Japan. Investigation of their information-sharing behaviors revealed what types of users and external sites were referred to when they changed their stances. These findings will help increase coverage of booster doses and future vaccinations. © 2022 IEEE.
ABSTRACT
Hospitals across the globe have severe constraints in regard to ICU facilities, beds, and other life support systems. However, in certain situations including natural calamities, epidemics or pandemics, large-scale accidents, and so on, the requirement for ICU beds and resources immediately gets augmented. During such times, there exists an impending need for an optimum apportioning of ICU admissions and resources so that those patients who need critical care are given at the right point of time. The onslaught of COVID-19 pandemic has exuded a high probability of virus transmissions and subsequent complications in patients with co-morbidities and relevant medical issues, resulting in the exploration and investigation of models that could forecast the need for ICU admissions with a higher degree of accuracy. In this research study, a patient's pre-condition dataset will be used that is categorical in nature. Feature selection and extractions are implemented and the modified descriptors are provided as input to the model, for evaluating them based on the metrics namely F1-score, accuracy, specificity, and sensitivity. The prime objective is to build a predictive algorithm that will predict prior to the necessity of ICU admissions based on the patient's comorbidity/ precondition specifically for SARS COV2 infection. © 2022 IEEE.
ABSTRACT
Cloud computing is currently one of the prime choices in the computing infrastructure landscape. In addition to advantages such as the pay-per-use bill model and resource elasticity, there are technical benefits regarding heterogeneity and large-scale configuration. Alongside the classical need for performance, for example, time, space, and energy, there is an interest in the financial cost that might come from budget constraints. Based on scalability considerations and the pricing model of traditional public clouds, a reasonable optimization strategy output could be the most suitable configuration of virtual machines to run a specific workload. From the perspective of runtime and monetary cost optimizations, we provide the adaptation of a Hadoop applications execution cost model extracted from the literature aiming at Spark applications modeled with the MapReduce paradigm. We evaluate our optimizer model executing an improved version of the Diff Sequences Spark application to perform SARS-CoV-2 coronavirus pairwise sequence comparisons using the AWS EC2's virtual machine instances. The experimental results with our model outperformed 80% of the random resource selection scenarios. By only employing spot worker nodes exposed to revocation scenarios rather than on-demand workers, we obtained an average monetary cost reduction of 35.66% with a slight runtime increase of 3.36%. © 2023 John Wiley & Sons, Ltd.
ABSTRACT
In this research, through the survey and experiments, we examined whether the reflection of distance to sounds leads to an improvement in the sense of reality, and whether it is possible to enhance the sense of presence by reproducing the feeling of distance in environment that each of audience can easily build. The basic idea is from the needs of the times: the increase in online events due to the spread of COVID-19, and the loss of sense of distance and lack of realism when watching online events. From the survey results on ‘sense of reality' at the first stage of the research, it became clear that ‘sense of reality' is composed of multi kinds of sensory elements. Therefore, it is thought that, if sounds reflect sense of distance, it would lead to the enhancement of the ‘three-dimensional feeling' and the ‘sense of cause and effect', which are components of the ‘sense of presence', and thus this results in the enhancement of the sensory reality of "being there” when viewing a video. Also, the sound is attenuated by different kinds of causes, and in the large-scale venues where events take place, it is the case that the ‘sound attenuated by the distance from the sound source' reaches most of the seats. To reproduce a sense of distance, two kinds of experiments were conducted to confirm whether people recognize it and to check whether it is reproducible. From the results, it has been shown that the feeling of distance from the object can be perceived almost accurately by the audio information and the ‘distance perception' can be altered to some extent by the volume change. Recognizing distance leads to an improvement in the three-dimensional sense, which is one of components of the sense of reality, and it is thought that matching the feeling of distance perceived by sound and the feeling of distance perceived by the image can improve the ‘sense of cause and effect'. Therefore, based on the results of the experiment, it is considered possible to enhance the sense of presence by changing the sound volume in line with what is on the video. Even in a re-viewing environment that can be generally adjusted like when participating in an event online. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Post Covid-19 pandemic and the Ukrainian war are significantly impacting energy systems worldwide, faltering investments and threatening to throttle the expansion of primary clean energy technologies, even in the case of a well-structured and managed energy system, such as Norway. This unprecedented crisis requires deeper analyses and well-measured actions from the main actors in Norway's energy and climate sector. Hence, providing and highlighting needed interventions and improvements in the energy system is crucial. This study analyzes demand-side energy in Norway's households, industry, transport, and "other” sectors. LEAP model, a powerful energy system analysis tool, was used to conduct the analysis based on Baseline and Mitigation scenarios. The energy demand by sector and fuel type toward 2050 is forecasted, firstly by considering a set of parameters and key assumptions that impact the security of supply and secondly on the ambitious target of Norway's government in decreasing GHG emissions by 55% in 2030 and 90–95% by the year 2050 compared to 1990 levels. The mitigation scenario aims to diversify the overall national energy system and technological changes based on large-scale renewable energy sources (RES) integration. From the perspective of climate change issues, EV's include an attractive option for deep decarbonization, including other sustainable fuel sources such as H2, biofuel mixed with diesel, the use of excess heat deriving from industry to cover households' heating demand, and integration of large-scale heat pumps driven by RES during off-peak demand is applied. Energy demand projections are uncertain, and the main goal is to show how different scenario projections up to 2050 affect the whole of Norway's energy system, leading to a combined global warming potential (GWP) of around 7.30 MtCO2 in the mitigation scenario from 56.40 MtCO2 tones released in the baseline scenario, by reaching only 77.5% reduction referring to 1990 level. This study's findings show that the net-zero ambitions by the end of 2050 are impossible without the carbon tax application and carbon capture storage (CCS), especially in the oil and gas industry. © 2023 The Authors
ABSTRACT
A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning. © 2023 Owner/Author.
ABSTRACT
The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taking. So now these days, the purpose of the researcher is to detect these diseases with the help of Artificial Intelligence or Machine learning-based models using CT scan images and X-rays images. So the testing cost, time taken and the number of data required could be minimized. In this paper, transfer learning based on three fine-tuned models has been proposed for Covid detection. The performance of these proposed fine-tuned models has been also compared with other competing models to check the accuracy and other matrices. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic is having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional responses on a large scale. In this paper, we present the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500 short + 2,500 long texts). Our analyses suggest that emotional responses correlated with linguistic measures. Topic modeling further revealed that people in the UK worry about their family and the economic situation. Tweet-sized texts functioned as a call for solidarity, while longer texts shed light on worries and concerns. Using predictive modeling approaches, we were able to approximate the emotional responses of participants from text within 14% of their actual value. We encourage others to use the dataset and improve how we can use automated methods to learn about emotional responses and worries about an urgent problem. © ACL 2020.All right reserved.
ABSTRACT
Agile companies are not uniform. Consequently, agile transformations are conceived broadly, ranging from adopting agile methods and practices in software development teams or functions to building all-encompassing enterprise agility. Moreover, the targeted effects of agility may vary, and the success of transformations and the attainment of agility are measured in various ways. In this paper, based on a recent industrial survey study, we scrutinize holistically why companies want to transform, what types of agility they are aiming at, and how they gauge transformations. The survey data was collected during the COVID-19 pandemic in 2020. Most of the respondents were in large or very large companies in Finland and Sweden in diverse industry domains. The main findings indicate that there are many reasons for companies to transform both to improve external outcomes (fore mostly responsiveness) and to develop internal capabilities (adaptability, organizational learning). Companies seemed to have aims and goals with respect to all types of agility, including business agility. As the nature of transformations and the companies' aims and goals vary, the transformations follow various means and measures. As a conclusion, for the hybrid era, we advise companies to consider how agility has benefited during the pandemic era, how hybrid work possibly affects the goals for agile transformations and the different facets of agility, and how to sustain agility in hybrid work. © 2022 IEEE.
ABSTRACT
In energy saving operation of buildings, it is important to understand the energy consumption characteristics of university campuses in order to formulate specific energy saving plans. Due to COVID-19 expansion, it is assumed that infection prevention measures such as behavior change of students and ventilation are affecting the energy consumption characteristics. It is necessary to understand the energy consumption characteristics that have changed from the conventional ones. In this study, we analyzed energy consumption data on Meiji university campuses for the three years from 2019 to 2021. we clarify the energy consumption characteristics that have changed due to COVID-19 expansion. © 2023 Architectural Institute of Japan. All rights reserved.