PODC 2020 Review

2021 ◽  
Vol 51 (4) ◽  
pp. 75-81
Ahad Mirza Baig ◽  
Alkida Balliu ◽  
Peter Davies ◽  
Michal Dory

Rachid Guerraoui was the rst keynote speaker, and he got things o to a great start by discussing the broad relevance of the research done in our community relative to both industry and academia. He rst argued that, in some sense, the fact that distributed computing is so pervasive nowadays could end up sti ing progress in our community by inducing people to work on marginal problems, and becoming isolated. His rst suggestion was to try to understand and incorporate new ideas coming from applied elds into our research, and argued that this has been historically very successful. He illustrated this point via the distributed payment problem, which appears in the context of blockchains, in particular Bitcoin, but then turned out to be very theoretically interesting; furthermore, the theoretical understanding of the problem inspired new practical protocols. He then went further to discuss new directions in distributed computing, such as the COVID tracing problem, and new challenges in Byzantine-resilient distributed machine learning. Another source of innovation Rachid suggested was hardware innovations, which he illustrated with work studying the impact of RDMA-based primitives on fundamental problems in distributed computing. The talk concluded with a very lively discussion.

2019 ◽  
Vol 214 ◽  
pp. 04025
Thomas Maier ◽  
Thomas Beermann ◽  
Günter Duckeck ◽  
Mario Lassnig ◽  
Federica Legger ◽  

For high-throughput computing the efficient use of distributed computing resources relies on an evenly distributed workload, which in turn requires wide availability of input data that is used in physics analysis. In ATLAS, the dynamic data placement agent C3PO was implemented in the ATLAS distributed data management system Rucio which identifies popular data and creates additional, transient replicas to make data more widely and more reliably available. This proceedings presents studies on the performance of C3PO and the impact it has on throughput rates of distributed computing in ATLAS. Furthermore, results of a study on popularity prediction using machine learning techniques are presented.

2020 ◽  
Vol 12 (4) ◽  
pp. 121-129
Francis de Véricourt ◽  
Georgia Perakis

In this short paper, we discuss the impact of data analytics in services and delineate future research directions for the field. After illustrating how data analytics are transforming different service sectors, we consider the provision of data analysis as a service in its own right. We discuss how the very nature of data and certain features of the machine learning method give rise to new issues and pitfalls for the management of these services, which delineates as many future research directions. We also discuss the coproduction of services by humans and machines and call for more research on responsible data analytics services to tackle some of the most pressing ethical issues in our societies.

2021 ◽  
Vol 2022 (1) ◽  
pp. 274-290
Dmitrii Usynin ◽  
Daniel Rueckert ◽  
Jonathan Passerat-Palmbach ◽  
Georgios Kaissis

Abstract In this study, we aim to bridge the gap between the theoretical understanding of attacks against collaborative machine learning workflows and their practical ramifications by considering the effects of model architecture, learning setting and hyperparameters on the resilience against attacks. We refer to such mitigations as model adaptation. Through extensive experimentation on both, benchmark and real-life datasets, we establish a more practical threat model for collaborative learning scenarios. In particular, we evaluate the impact of model adaptation by implementing a range of attacks belonging to the broader categories of model inversion and membership inference. Our experiments yield two noteworthy outcomes: they demonstrate the difficulty of actually conducting successful attacks under realistic settings when model adaptation is employed and they highlight the challenge inherent in successfully combining model adaptation and formal privacy-preserving techniques to retain the optimal balance between model utility and attack resilience.

2021 ◽  
Vol 118 (4) ◽  
pp. e2011216118
Steven T. Smith ◽  
Edward K. Kao ◽  
Erika D. Mackin ◽  
Danelle C. Shah ◽  
Olga Simek ◽  

The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IOs). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a network causal inference approach to quantify the impact of individual actors in spreading IO narratives. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007 to February 2020), over 50,000 accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the precision-recall (P-R) curve; maps out salient network communities; and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from US Congressional reports, investigative journalism, and IO datasets provided by Twitter.

2019 ◽  
Vol 214 ◽  
pp. 00001
Alessandra Forti ◽  
Latchezar Betev ◽  
Maarten Litmaath ◽  
Oxana Smirnova ◽  
Petya Vasileva ◽  

The 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP) took place in the National Palace of Culture, Sofia, Bulgaria from 9th to 13th of July 2018. 575 participants joined the plenary and the eight parallel sessions dedicated to: online computing; offline computing; distributed computing; data handling; software development; machine learning and physics analysis; clouds, virtualisation and containers; networks and facilities. The conference hosted 35 plenary presentations, 323 parallel presentations and 188 posters.

2018 ◽  
Vol 154 ◽  
pp. 03013
Endar Wihidayat ◽  
Ovide D.W. Ardi

Near Field Communication (NFC) as relatively new wireless communication technology pushes new challenges to application developers to make their applications easier to use and simpler to operate. This point of view known as usability element. Usability is one of the elements for creating good quality applications. This study aims to analyse the usability of mobile-based application embeds with NFC. We also try to evaluate usability in applications used by children. We developed an application called Receptionist which has a primary function as a communication tool between students, teachers and parents at a middle school. To know the impact of the NFC, the Receptionist input system is designed with two methods, via conventional navigation (using buttons) and via NFC. To understand the usability of each method, we do user testing and questioners on students. The results show, using the NFC there is a significant increase in usability attributes: efficiency, effectiveness, and learnability. On the other hand, there is decreases of user satisfaction comparing to conventional method. In general, this study demonstrates the potential of new input device technologies that can improve the usability of mobile-based applications.

Sangjae Lee ◽  
Joon Yeon Choeh

Abstract While electronic word-of-mouth (eWOM) variables, such as volume and valence have been posited in previous studies to consistently affect product sales, there is a lack of studies on the different contexts and outcomes that affect the importance of eWOM variables. In order to fill this gap, this study attempts to use the helpfulness of reviews and reviewers as moderators to predict box office revenue, comparing the prediction performances of business intelligence (BI) methods (random forest, decision trees using boosting, the k-nearest neighbor method, discriminant analysis) using eWOM between high and low review or reviewer helpfulness subsample in the Korean movie market scrawled from the Naver Movies website. The results of applying machine learning methods show that movies with more helpful reviews or those that are reviewed by more helpful reviewers show greater prediction performance, and review and reviewer helpfulness improve the prediction power of eWOM for box office revenue. The prediction performance will improve if the characteristics of eWOM are likely to be combined to contribute to box office revenue to a greater extent.

Nature Energy ◽  
2020 ◽  
Vol 5 (12) ◽  
pp. 1051-1052
Shiqi Ou ◽  
Xin He ◽  
Weiqi Ji ◽  
Wei Chen ◽  
Lang Sui ◽  

2020 ◽  
Vol 1500 ◽  
pp. 012131
Firdaus ◽  
Andre Herviant Juliano ◽  
Naufal Rachmatullah ◽  
Sarifah Putri Rafflesia ◽  
Dinna Yunika Hardiyanti ◽  

Jasleen Kaur Sethi ◽  
Mamta Mittal

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

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