P. Deligiannis, Thanasis Vergoulis, Serafeim Chatzopoulos, Christos Tryfonopoulos
The automatic extraction of topics is a standard technique for summarizing text corpora from various domains (e.g., news articles, transport or logistic reports, scientific publications) that has several applications. Since, in many cases, topics are subject to continuous change there is the need to monitor the evolution of a set of topics of interest, as the corresponding corpora are updated. The evolution of scientific topics, in particular, is of great interest for researchers, policy makers, fund managers, and other professionals/engineers in the research and academic community. In this work, we demonstrate a prototype that provides intuitive visualisations for the evolution of scientific topics providing insights about topic transformation, merging, and splitting during the recent years. Although the prototype works on top of a scientific text corpus, its implementation is generic and can be easily applied on texts from other domains, as well.
{"title":"Visualising Scientific Topic Evolution","authors":"P. Deligiannis, Thanasis Vergoulis, Serafeim Chatzopoulos, Christos Tryfonopoulos","doi":"10.1145/3442442.3451371","DOIUrl":"https://doi.org/10.1145/3442442.3451371","url":null,"abstract":"The automatic extraction of topics is a standard technique for summarizing text corpora from various domains (e.g., news articles, transport or logistic reports, scientific publications) that has several applications. Since, in many cases, topics are subject to continuous change there is the need to monitor the evolution of a set of topics of interest, as the corresponding corpora are updated. The evolution of scientific topics, in particular, is of great interest for researchers, policy makers, fund managers, and other professionals/engineers in the research and academic community. In this work, we demonstrate a prototype that provides intuitive visualisations for the evolution of scientific topics providing insights about topic transformation, merging, and splitting during the recent years. Although the prototype works on top of a scientific text corpus, its implementation is generic and can be easily applied on texts from other domains, as well.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132982650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nenad N. Petrović, Vlado Dimovski, Judita Peterlin, M. Meško, Vasja Roblek
This paper aims to give a systemic vision about the data-driven mobile applications in urban data management processes, which is essential to ensure a sustainable smart city ecosystem for what is needed to ensure diversification between stakeholders and data sources. The realization of sustainable data-driven smart solutions based on an urban data platform that will enable citizen wellbeing in the smart city is needed to develop data-driven applications. In this paper, we present five case study mobile applications developed using AppSheet and Google Apps Script technologies to prevent the spread of COVID-19 and provide support to (potentially) infected citizens. Several aspects relevant to coronavirus pandemic are considered: quick COVID-19 patient assessment based on user-provided symptoms integrated with contact tracing; volunteer help during quarantine; UAV-based COVID-19 outdoor safety surveillance; test scheduling and AR-based pharmacy shop assistant.
{"title":"Data-Driven Solutions in Smart Cities: The case of Covid-19","authors":"Nenad N. Petrović, Vlado Dimovski, Judita Peterlin, M. Meško, Vasja Roblek","doi":"10.1145/3442442.3453469","DOIUrl":"https://doi.org/10.1145/3442442.3453469","url":null,"abstract":"This paper aims to give a systemic vision about the data-driven mobile applications in urban data management processes, which is essential to ensure a sustainable smart city ecosystem for what is needed to ensure diversification between stakeholders and data sources. The realization of sustainable data-driven smart solutions based on an urban data platform that will enable citizen wellbeing in the smart city is needed to develop data-driven applications. In this paper, we present five case study mobile applications developed using AppSheet and Google Apps Script technologies to prevent the spread of COVID-19 and provide support to (potentially) infected citizens. Several aspects relevant to coronavirus pandemic are considered: quick COVID-19 patient assessment based on user-provided symptoms integrated with contact tracing; volunteer help during quarantine; UAV-based COVID-19 outdoor safety surveillance; test scheduling and AR-based pharmacy shop assistant.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"132 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132186906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the context of open science, good research data management (RDM), including data sharing and data reuse, has become a major goal of research policy. However, studies and monitors reveal that open science practices are not yet widely mainstream. Rewards and incentives have been suggested as a solution, to facilitate and accelerate the development of open and transparent RDM. Based on relevant literature, our paper provides a critical analysis of three main issues: what should be rewarded and incentivized, who should be rewarded, and what kind of rewards and incentives should be used? Concluding the analysis, we ask if it is really necessary and appropriate to consider RDM as an individual (behavioral) issue, as the main challenges are elsewhere, not personal, but technological, institutional and financial.
{"title":"Rewarding Research Data Management","authors":"Joachim Schöpfel, Otmane Azeroual","doi":"10.1145/3442442.3451367","DOIUrl":"https://doi.org/10.1145/3442442.3451367","url":null,"abstract":"In the context of open science, good research data management (RDM), including data sharing and data reuse, has become a major goal of research policy. However, studies and monitors reveal that open science practices are not yet widely mainstream. Rewards and incentives have been suggested as a solution, to facilitate and accelerate the development of open and transparent RDM. Based on relevant literature, our paper provides a critical analysis of three main issues: what should be rewarded and incentivized, who should be rewarded, and what kind of rewards and incentives should be used? Concluding the analysis, we ask if it is really necessary and appropriate to consider RDM as an individual (behavioral) issue, as the main challenges are elsewhere, not personal, but technological, institutional and financial.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133532728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We audit the presence of domain-level source diversity bias in video search results. Using a virtual agent-based approach, we compare outputs of four Western and one non-Western search engines for English and Russian queries. Our findings highlight that source diversity varies substantially depending on the language with English queries returning more diverse outputs. We also find disproportionately high presence of a single platform, YouTube, in top search outputs for all Western search engines except Google. At the same time, we observe that Youtube’s major competitors such as Vimeo or Dailymotion do not appear in the sampled Google’s video search results. This finding suggests that Google might be downgrading the results from the main competitors of Google-owned Youtube and highlights the necessity for further studies focusing on the presence of own-content bias in Google’s search results.
{"title":"Auditing Source Diversity Bias in Video Search Results Using Virtual Agents","authors":"Aleksandra Urman, M. Makhortykh, R. Ulloa","doi":"10.1145/3442442.3452306","DOIUrl":"https://doi.org/10.1145/3442442.3452306","url":null,"abstract":"We audit the presence of domain-level source diversity bias in video search results. Using a virtual agent-based approach, we compare outputs of four Western and one non-Western search engines for English and Russian queries. Our findings highlight that source diversity varies substantially depending on the language with English queries returning more diverse outputs. We also find disproportionately high presence of a single platform, YouTube, in top search outputs for all Western search engines except Google. At the same time, we observe that Youtube’s major competitors such as Vimeo or Dailymotion do not appear in the sampled Google’s video search results. This finding suggests that Google might be downgrading the results from the main competitors of Google-owned Youtube and highlights the necessity for further studies focusing on the presence of own-content bias in Google’s search results.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131289511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most language use is driven by specific communicative goals in interactive setups, where often visual perception goes hand in hand with language processing. I will discuss some recent projects by my research group related to modelling language generation in socially and visually grounded contexts, arguing that such models can help us to better understand the cognitive processes underpinning these abilities in humans and contribute to more human-like conversational agents.
{"title":"Grounding Language in Visual and Conversational Contexts","authors":"R. Fernández","doi":"10.1145/3442442.3451898","DOIUrl":"https://doi.org/10.1145/3442442.3451898","url":null,"abstract":"Most language use is driven by specific communicative goals in interactive setups, where often visual perception goes hand in hand with language processing. I will discuss some recent projects by my research group related to modelling language generation in socially and visually grounded contexts, arguing that such models can help us to better understand the cognitive processes underpinning these abilities in humans and contribute to more human-like conversational agents.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115775915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes the method that we submitted to the FinSim-2 task on learning similarities for the financial domain. This task aims to automatically classify the Financial domain terms into the most relevant hypernym (or top-level) concept in an external ontology. This paper shows the result of experiments using the Catboost, Attention-LSTM, BERT, RoBERTa to develop an automatic finance domain classifier via word ontology and embedding. The experiment result demonstrates that each model could be an effective method to tackle the FinSim-2 task, respectively.
{"title":"aiai at the FinSim-2 task: Finance Domain Terms Automatic Classification Via Word Ontology and Embedding","authors":"Ke Tian, Hua Chen","doi":"10.1145/3442442.3451388","DOIUrl":"https://doi.org/10.1145/3442442.3451388","url":null,"abstract":"This paper describes the method that we submitted to the FinSim-2 task on learning similarities for the financial domain. This task aims to automatically classify the Financial domain terms into the most relevant hypernym (or top-level) concept in an external ontology. This paper shows the result of experiments using the Catboost, Attention-LSTM, BERT, RoBERTa to develop an automatic finance domain classifier via word ontology and embedding. The experiment result demonstrates that each model could be an effective method to tackle the FinSim-2 task, respectively.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114858646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Capturing and exploiting a content’s semantic is a key success factor for Web search. To this end, it is crucial to - ideally automatically - extract the core semantics of the data being processed and link this information with some formal representation, such as an ontology. By intertwining both, search becomes semantic by simultaneously allowing end-users a structured access to the data via the underlying ontology. Connecting both, we introduce the SEMANNOREX framework in order to provide semantically enriched access to a news corpus from Websites and Wikinews.
{"title":"Semantic Search via Entity-Types: The SEMANNOREX Framework","authors":"Amit Kumar, Govind, M. Spaniol","doi":"10.1145/3442442.3458607","DOIUrl":"https://doi.org/10.1145/3442442.3458607","url":null,"abstract":"Capturing and exploiting a content’s semantic is a key success factor for Web search. To this end, it is crucial to - ideally automatically - extract the core semantics of the data being processed and link this information with some formal representation, such as an ontology. By intertwining both, search becomes semantic by simultaneously allowing end-users a structured access to the data via the underlying ontology. Connecting both, we introduce the SEMANNOREX framework in order to provide semantically enriched access to a news corpus from Websites and Wikinews.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124314757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuang Peng, Minghui Yang, Fudong Wang, Xiangyang Li, Zujie Wen, Lei Liu
In the insurance industry, the assessor’s role is essential and requires significant efforts conversing with the claimant. This is a highly professional process that involves many complex operations to make a final insurance report. In order to save the cost, the previous offline insurance assessment procedure is gradually moved online. However, for the junior assessor often lacking in practical experience, it is not easy to quickly handle such an online procedure, yet this is important as the insurance company decides how much compensation the claimant should receive based on the assessor’s feedback. In this paper, we present an insurance assistant that applies NLP technologies to help junior insurance assessors do their job better. The insurance assistant recommends appropriate inquiring policies and auto-completes the case report during the insurance assessment procedure. Here, we demonstrate the system via a short video 1.
{"title":"Insurance Assistant: An Intelligent Platform for Video Insurance Assessment","authors":"Shuang Peng, Minghui Yang, Fudong Wang, Xiangyang Li, Zujie Wen, Lei Liu","doi":"10.1145/3442442.3458600","DOIUrl":"https://doi.org/10.1145/3442442.3458600","url":null,"abstract":"In the insurance industry, the assessor’s role is essential and requires significant efforts conversing with the claimant. This is a highly professional process that involves many complex operations to make a final insurance report. In order to save the cost, the previous offline insurance assessment procedure is gradually moved online. However, for the junior assessor often lacking in practical experience, it is not easy to quickly handle such an online procedure, yet this is important as the insurance company decides how much compensation the claimant should receive based on the assessor’s feedback. In this paper, we present an insurance assistant that applies NLP technologies to help junior insurance assessors do their job better. The insurance assistant recommends appropriate inquiring policies and auto-completes the case report during the insurance assessment procedure. Here, we demonstrate the system via a short video 1.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115828355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gertjan De Mulder, B. Meester, Pieter Heyvaert, Ruben Taelman, Anastasia Dimou, R. Verborgh
Data is scattered across service providers, heterogeneously structured in various formats. By lack of interoperability, data portability is hindered, and thus user control is inhibited. An interoperable data portability solution for transferring personal data is needed. We demo PROV4ITDaTa: a Web application, that allows users to transfer personal data into an interoperable format to their personal data store. PROV4ITDaTa leverages the open-source solutions RML.io, Comunica, and Solid: (i) the RML.io toolset to describe how to access data from service providers and generate interoperable datasets; (ii) Comunica to query these and more flexibly generate enriched datasets; and (iii) Solid Pods to store the generated data as Linked Data in personal data stores. As opposed to other (hard-coded) solutions, PROV4ITDaTa is fully transparent, where each component of the pipeline is fully configurable and automatically generates detailed provenance trails. Furthermore, transforming the personal data into RDF allows for an interopable solution. By maximizing the use of open-source tools and open standards, PROV4ITDaTa facilitates the shift towards a data ecosystem wherein users have control of their data, and providers can focus on their service instead of trying to adhere to interoperability requirements.
{"title":"PROV4ITDaTa: Transparent and direct transferof personal data to personal stores","authors":"Gertjan De Mulder, B. Meester, Pieter Heyvaert, Ruben Taelman, Anastasia Dimou, R. Verborgh","doi":"10.1145/3442442.3458608","DOIUrl":"https://doi.org/10.1145/3442442.3458608","url":null,"abstract":"Data is scattered across service providers, heterogeneously structured in various formats. By lack of interoperability, data portability is hindered, and thus user control is inhibited. An interoperable data portability solution for transferring personal data is needed. We demo PROV4ITDaTa: a Web application, that allows users to transfer personal data into an interoperable format to their personal data store. PROV4ITDaTa leverages the open-source solutions RML.io, Comunica, and Solid: (i) the RML.io toolset to describe how to access data from service providers and generate interoperable datasets; (ii) Comunica to query these and more flexibly generate enriched datasets; and (iii) Solid Pods to store the generated data as Linked Data in personal data stores. As opposed to other (hard-coded) solutions, PROV4ITDaTa is fully transparent, where each component of the pipeline is fully configurable and automatically generates detailed provenance trails. Furthermore, transforming the personal data into RDF allows for an interopable solution. By maximizing the use of open-source tools and open standards, PROV4ITDaTa facilitates the shift towards a data ecosystem wherein users have control of their data, and providers can focus on their service instead of trying to adhere to interoperability requirements.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117138677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances on deep learning technologies have made GPU clusters popular as training platforms. In this paper, we study reliability issues while focusing on training job failures from analyzing logs collected from running deep learning workloads on a large-scale GPU cluster in production. These failures are largely grouped into two categories, infrastructure and user, based on their sources, and reveal diverse reasons causing the failures. With insights obtained from the failure analysis, we suggest several different ways to improve the stability of shared GPU clusters designed for DL training and optimize user experience by reducing failure occurrences.
{"title":"Reliability of Large Scale GPU Clusters for Deep Learning Workloads","authors":"Junjie Qian, Taeyoon Kim, Myeongjae Jeon","doi":"10.1145/3442442.3452056","DOIUrl":"https://doi.org/10.1145/3442442.3452056","url":null,"abstract":"Recent advances on deep learning technologies have made GPU clusters popular as training platforms. In this paper, we study reliability issues while focusing on training job failures from analyzing logs collected from running deep learning workloads on a large-scale GPU cluster in production. These failures are largely grouped into two categories, infrastructure and user, based on their sources, and reveal diverse reasons causing the failures. With insights obtained from the failure analysis, we suggest several different ways to improve the stability of shared GPU clusters designed for DL training and optimize user experience by reducing failure occurrences.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127005154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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