computational.science April 2022

computational.science@lists.iccsa.org

4 participants
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[DEADLINE EXTENDED] 3 Years Marie Skłodowska-Curie ESR research grant - InnocyPES European project.
by Massimo Cafaro 26 Apr '22

26 Apr '22

A three years research grant is available within the InnocyPES European project. The position is related to ESR4, for research on Large Scale Data Management and Integration DEADLINE: extended to May 5, 2022 Ukranian Researchers are strongly encouraged to apply! Downloadable application forms etc are available: official Italian documents: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 unofficial English translation: http://sara.unisalento.it/~cafaro/page-3/ InnoCyPES announcement: https://innocypes.eu/index.php/2022/03/29/esr-4-large-scale-data-management… Foreign candidates are strongly encouraged to contact Prof. Cafaro by email (massimo.cafaro(a)unisalento.it) if they need help/support in order to prepare their application. A public selection procedure is called for a research grant for collaboration in research activities (hereinafter referred to as research grant), at the Department of Innovation Engineering of the University of Salento. The location, the duration, the amount, the scientific disciplinary sector, the scientific referent, the structure available to the winner and the program of the research grant are specified below: STRUCTURE Department of Engineering for Innovation, University of Salento, Lecce, Italy DURATION 3 years REMUNERATION The research grant will last 3 (three) years. The annual remuneration, gross of charges to be borne by the beneficiary and inclusive of contributions and social security charges to be borne by the University of Salento, consists of the following items: 1. Living allowance: Euro 40,966.56 (forty thousand nine hundred and sixty-six/56); 2. Mobility allowance: Euro 7.200,00 (seven thousand two hundred/00); 3. Family allowance: 3.1.Euro 0 (zero) per researcher without family obligations; 3.2.Euro 6,000.00 (six thousand/00) for researchers with family obligations (married or with a relationship recognised by Italian law or that of the country of origin or with dependent children). SCIENTIFIC SUPERVISOR Prof. Massimo Cafaro RESEARCH TITLE LARGE SCALE DATA MANAGEMENT AND INTEGRATION DESCRIPTION This is a three year “Marie Curie ETN Early Stage Researcher position” on the following topic. The sheer quantum of data being created and collected across jurisdictions requires a carefully planned and proactive approach to data management. The need for fusion and integration of multiple data sources characterized by fragmented data ownership is driving innovative approaches to large scale distributed data management and integration to avoid inconsistent and inaccurate data. The aim is to investigate, design and implement a fully decentralized solution to provide efficient management of dynamically updated information and support for distributed queries. One or more domain specific use cases shall be identified within the context of the project, considering both the current and future needs of some of the involved partners. These nicely fit into the research plan, owing to the need of surveying the user’s requirements to begin with; simultaneously, the uses cases can also be thought of as sources of advanced data management challenges. For admission to the selection is required the possession of all the requirements provided by law for access to public employment, the eligibility requirements provided by the Marie Sklodowska- Curie Action (https://ec.europa.eu/research/participants/data/ref/h2020/wp/2018-2020/main…) and the following requirements: a) Master of Science degree in Computer Science or Computer Engineering or equivalent qualification that formally allows access to a PhD program in Italy; b) an excellent academic career; c) must not have carried out more than 4 years of research activity after obtaining the degree mentioned in the previous point within the expected starting date of the contract (indicatively fixed at 01.03.2022); d) have a background relevant to the following areas but not limited to: distributed computing, databases, distributed data management, security and privacy; knowledge of C/C++ programming languages and time series archiving/analysis is a plus; e) must not have a Ph.D. as of the contract start date; f) must not have resided or carried out the main activity of study/work in Italy for more than 12 months during the 3 years preceding the date of the beginning of the contract; g) have an excellent knowledge of the English language, in any case sufficient to ensure the performance of the activity envisaged by the contract and daily interaction in the working environment; h) must not have criminal convictions or have pending criminal proceedings of particular gravity. Lack of even one of the above requirements will result in exclusion from the selection process at any time. Qualifications obtained abroad must normally be previously recognized in Italy in accordance with current legislation. The equivalence of qualifications obtained abroad that have not already been recognized in Italy will be evaluated by the Selection Committee solely for the purpose of admitting the candidate to this call for selection. The qualifications must be possessed on the date of expiry of the deadline established for the presentation of applications for admission to this selection. The University of Salento guarantees equality and equal opportunities between men and women for the allocation of the grants in question and the protection of confidentiality in the processing of personal data, according to the provisions in force. The position is officially advertised at the University of Unisalento: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 ******************************************************************************************* Prof. Massimo Cafaro, Ph.D. Associate Professor of Parallel Algorithms and Data Mining/Machine Learning Department of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro Web https://www.unisalento.it/people/massimo.cafaro E-mail massimo.cafaro(a)unisalento.it E-mail cafaro(a)ieee.org E-mail cafaro(a)acm.org INGV National Institute of Geophysics and Volcanology Via di Vigna Murata 605 Roma CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ******************************************************************************************* --

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CfP: 29th IEEE International Conference on High Performance Computing, Data & Analytics (HiPC 2022)
by Anand Panangadan 17 Apr '22

17 Apr '22

HiPC 2022 29th IEEE International Conference on High Performance Computing, Data & Analytics Dec. 18-21, 2022 Bengaluru, India Website: http://www.hipc.org CALL FOR PAPERS HiPC 2022 will be the 29th edition of the IEEE International Conference on High Performance Computing, Data, Analytics, and Data Science. HiPC serves as a forum to present current work by researchers from around the world as well as highlight activities in Asia in the areas of high performance computing and data science. The meeting focuses on all aspects of high performance computing systems, and data science and analytics, and their scientific, engineering, and commercial applications. Authors are invited to submit original unpublished research manuscripts that demonstrate current research in all areas of high performance computing, and data science and analytics, covering all traditional areas and emerging topics including from machine learning, big data analytics. Each submission should be submitted to one of the six tracks listed under the two broad themes of High Performance Computing and Data Science. Up to two best paper awards will be given for outstanding contributed papers. Depending on how the COVID-19 pandemic situation evolves, the presentation may be in person or in a virtual format. Authors of selected high-quality papers in HiPC 2022 will be invited to submit extended versions of their papers for possible publication in a special issue of the Journal of Parallel and Distributed Computing (JPDC). HIGH PERFORMANCE COMPUTING Topics for papers include, but are not limited to the topics given under the categories below. Algorithms: This track invites papers that describe original research on developing new parallel and distributed computing algorithms, and related advances. Examples of topics that are of interest include (but not limited to): - New parallel and distributed algorithms and design techniques; - Advances in enhancing algorithmic properties or providing guarantees (e.g., concurrency, data locality, communication-avoiding, asynchronous, hybrid CPU-GPU algorithms, fault tolerance, resilience,); - Algorithmic techniques for resource allocation and optimization (e.g., scheduling, load balancing, resource management); - Provably efficient parallel and distributed algorithms for advanced scientific computing and irregular applications (e.g., numerical linear algebra, graph algorithms, computational biology); - Classical and emerging computation models (e.g., parallel/distributed models, quantum computing, neuromorphic and other bioinspired models). Architecture: This track invites papers that describe original research on the design and evaluation of high performance computing architectures, and related advances. Examples of topics of interest include (but not limited to): - High performance processing architectures (e.g., reconfigurable, system-on-chip, many cores, vector processors); - Networks for high performance computing platforms (e.g., interconnect topologies, network-on-chip); - Memory, cache and storage architectures (e.g., 3D, photonic, Processing-In-Memory, NVRAM, burst buffers, parallel I/O); - Approaches to improve architectural properties (e.g., energy/power efficiency, reconfigurable, resilience/fault tolerance, security/privacy); - Emerging computational architectures (e.g., quantum computing, neuromorphic and other bioinspired architectures). Applications: This track invites papers that describe original research on the design and implementation of scalable and high performance applications for execution on parallel, distributed and accelerated platforms, and related advances. Examples of topics of interest include (but not limited to): - Shared and distributed memory parallel applications (e.g., scientific computing, simulation and visualization applications, graph and irregular applications, data-intensive applications, science/engineering/industry applications, emerging applications in IoT and life sciences, etc.); - Methods, algorithms, and optimizations for scaling applications on peta- and exa-scale platforms (e.g., co-design of hardware and software, heterogeneous and hybrid programming); - Hardware acceleration of parallel applications (e.g., GPUs, FPGA, vector processors, manycore); - Application benchmarks and workloads for parallel and distributed platforms. Systems Software: This track invites papers that describe original research on the design, implementation, and evaluation of systems software for high performance computing platforms, and related advances. Examples of topics of interest include (but not limited to): - Scalable systems and software architectures for high-performance computing (e.g., middleware, operating systems, I/O services); - Techniques to enhance parallel performance (e.g., compiler/runtime optimization, learning from application traces, profiling); - Techniques to enhance parallel application development and productivity (e.g., Domain-Specific Languages, programming environments, performance/correctness checking and debugging); - Techniques to deal with uncertainties, hardware/software resilience, and fault tolerance; - Software for cloud, data center, and exascale platforms (e.g., middleware tools, schedulers, resource allocation, data migration, load balancing); - Software and programming paradigms for heterogeneous platforms (e.g., libraries for CPU/GPU, multi-GPU clusters, and other accelerator platforms). SCALABLE DATA SCIENCE Scalable Algorithms and Analytics: This track invites papers that describe original research on developing scalable algorithms for data analysis at scale, and related advances. Examples of topics of interest include (but not limited to): - New scalable algorithms for fundamental data analysis tasks (supervised, unsupervised learning, data (pre-)processing and pattern discovery); - Scalable algorithms that are designed to address the characteristics of different data sources and settings (e.g., graphs, social networks, sequences, data streams); - Scalable algorithms and techniques to reduce the complexity of large-scale data (e.g., streaming, sublinear data structures, summarization, compressive analytics); - Scalable algorithms that are designed to address requirements in different data-driven application domains (e.g., life sciences, business, agriculture, health sciences); - Scalable algorithms that ensure the transparency and fairness of the analysis; - Case studies, experimental studies, and benchmarks for scalable algorithms and analytics; - Scaling and accelerating machine learning, deep learning, natural language processing and computer vision applications. Scalable Systems and Software: This track invites papers that describe original research on developing scalable systems and software for handling data at scale and related advances. Examples of topics of interest include (but not limited to): - New parallel and distributed algorithms and design techniques; - Design of scalable system software to support various applications (e.g., recommendation systems, web search, crowdsourcing applications, streaming applications); - Scalable system software for various architectures (e.g., OpenPower, GPUs, FPGAs); - Architectures and systems software to support various operations in large data frameworks (e.g., storage, retrieval, automated workflows, data organization, visualization, visual analytics, human-in-the-loop); - Systems software for distributed data frameworks (e.g., distributed file system, data deduplication, virtualization, cloud services, resource optimization, scheduling); - Standards and protocols for enhancing various aspects of data analytics (e.g., open data standards, privacy-preserving, and secure schemes). Important dates - Submission site open: June 15, 2022 - Abstract submissions: July 4, 2022 AOE - Full Paper submissions: July 8, 2022 AOE - First-round Author notifications: September 12, 2022 - Submission of revised papers along with response to reviews: October 10, 2022 - Author notification for revised papers: November 1, 2022 - Camera-ready version: November 15, 2022 - Conference dates: December 18-21, 2022 General Co-chairs: - Chiranjib Sur, Shell, India - Neelima Bayyapu, Consultant, India Vice General Co-chairs: - Sanmukh Rao Kuppannagari, University of Southern California, USA - Vivek Yadav, IIIT-Bangalore, India- - Program Co–chairs: - High performance computing: Sathish Vadhiyar, Indian Institute of Science, India - Data science: Jun Wang, University of Central Florida, USA Steering committee chair: - Viktor K. Prasanna, University of Southern California, USA Program Vice-Chairs HPC TRACKS - Algorithms: Thomas Herault, University of Tennessee, USA - Applications: Yogish Sabharwal, IBM IRL, India - Architecture: Diana Goehringer, TU Dresden, Germany - System Software: Jyothi Vedurada, IIT, Hyderabad DATA SCIENCE TRACKS - Scalable Algorithms and Analytics: Zhishan Guo, University of Central Florida, USA - Scalable Systems and Software: Dan Huang, Sun Yat-Sen University, PRC

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Virtualization in High-Performance Cloud Computing (VHPC 2022) - Submission deadline extended
by Tommaso Cucinotta 14 Apr '22

14 Apr '22

[apologies for cross-postings] *The paper submission deadline for VHPC 2022 has been extended to April 26th (AoE). Please, register your abstract by April 19th.* The Workshop on Virtualization in High-Performance Cloud Computing (VHPC) <vhpc.org/> is an international forum bringing together researchers and industrial practitioners facing the challenges posed by virtualization in HPC/Cloud scenarios, in order to foster discussion, collaboration, mutual exchange of knowledge and experience, enabling research to ultimately provide novel solutions for virtualized computing systems of tomorrow. The 17th edition of VHPC will be held on June 2nd, jointly with the ISC High-Performance 2022 <https://www.isc-hpc.com/> conference and exhibition in Hamburg (Germany), and will feature two excellent *industrial **_/keynote speakers/_* * “rtla: finding the sources of OS noise on Linux”, Daniel Bristot De Oliveira, Senior Principal Software Engineer in the real-time kernel team at Red Hat * “DynamoDB: NoSQL database services for predictable HPC workloads”, Akshat Vig, Principal Software Engineer at Amazon Web Services (AWS) In addition to the general research topics mentioned below, VHPC'22 encourages particularly contributions on the following _/focus topics/_: * Container Platforms (Kubernetes, Docker, Singularity, Shifter, rkt, …) for Scientific Workflows * Composable Lightweight Applications and Unikernel Frameworks * Latency Control and Data/Container Placement in Heterogeneous HPC Virtualized Environments * Energy-efficiency and Service Orchestration in Virtualized Cloud & HPC Infrastructures *Workshop Overview* Containers and virtualization technologies constitute key enabling factors for flexible resource management in modern data centers, and particularly in cloud environments. Cloud providers need to manage complex and heterogeneous infrastructures in a seamless fashion to support the highly dynamic and diverse workloads and applications customers deploy. Similarly, HPC environments have been increasingly adopting techniques that enable flexible management of vast computing and networking resources, close to marginal provisioning cost, which is unprecedented in the history of scientific and commercial computing. More recently, Function as a Service (Faas) and Serverless computing, leveraging on lightweight virtualizaton and containerization solutions, widens the spectrum of applications that can be deployed in a cloud environment, especially in an HPC context. Here, HPC-provided services can become accessible to distributed workloads outside of large cluster environments. Various virtualization-containerization technologies contribute to the overall picture in different ways: machine virtualization, with its capability to enable consolidation of multiple underutilized servers with heterogeneous software and operating systems (OSes), and its capability to live-migrate a fully operating virtual machine (VM) with a very short downtime, enables novel and dynamic ways to manage physical servers; OS-level virtualization (i.e., containerization), with its capability to isolate multiple user-space environments and to allow for their coexistence within the same OS kernel, promises to provide many of the advantages of machine virtualization with bare-metal responsiveness and performance; lastly, unikernels provide for many virtualization benefits with a minimized OS/library surface. I/O Virtualization in turn allows physical network interfaces to take traffic from multiple VMs or containers; network virtualization, with its capability to create logical network overlays that are independent of the underlying physical topology is furthermore enabling virtualization of HPC infrastructures. *Topics of Interest* The VHPC program committee solicits original, high-quality submissions related to virtualization across the entire software stack with a special focus on the intersection of HPC, containers-virtualization and cloud computing. Each topic encompasses aspects related to design/architecture, management, performance management, modeling and configuration/tooling: Design / Architecture: * Containers and OS-level virtualization (LXC, Docker, rkt, Singularity, Shifter) * Hypervisor support for heterogeneous resources (GPUs, co-processors, FPGAs, etc.) * Hypervisor extensions to mitigate side-channel attacks ([micro-]architectural timing attacks, privilege escalation) * VM & Container trust and security models * Multi-environment coupling, system software supporting in-situ analysis with HPC simulation * Cloud reliability, fault-tolerance and high-availability * Energy-efficient and power-aware virtualization * Containers inside VMs with hypervisor isolation * Virtualization support for emerging memory technologies * Lightweight/specialized operating systems in conjunction with virtual machines * Hypervisor support for heterogeneous resources (GPUs, co-processors, FPGAs, etc.) * Novel unikernels and use cases for virtualized HPC environments * ARM-based hypervisors, ARM virtualization extensions Management: * Container, VM and data management for HPC and cloud environments * HPC services integration, services to support HPC * Service and on-demand scheduling & resource management * Dedicated workload management with VMs or containers * Workflow coupling with VMs and containers * Unikernels and lightweight VM application management * Environments and tools for operating containerized environments (batch, orchestration) * Novel models for non-HPC workload provisioning on HPC resources Performance Measurements and Modeling: * Performance improvements for or driven by unikernels * Optimizations of virtual machine monitor platforms and hypervisors * Scalability analysis of VMs and/or containers at large scale * Performance measurement, modeling and monitoring of virtualized/cloud workloads * Virtualization in supercomputing environments, HPC clusters, HPC in the cloud * Energy-efficient deployment of high-performance, ultra-low latency and real-time workloads in cloud infrastructures * Modeling, control and isolation of end-to-end performance for parallel & distributed cloud/HPC applications Configuration / Tooling: * Tool support for unikernels: configuration/build environments, debuggers, profilers * Job scheduling/control/policy and container placement in virtualized environments * Measuring and controlling “OS/Virtualization noise” * Operating MPI in containers/VMs and Unikernels * GPU virtualization operationalization The workshop will be one day in length, composed of 20 min paper presentations, each followed by 10 min discussion sections, plus lightning talks that are limited to 5 minutes. Presentations may be accompanied by interactive demonstrations. For more information and detailed paper submission instructions, refer to the VHPC'22 webpage <https://vhpc.org/>: https://vhpc.org/ *Important Dates* * *Apr 19th, 2022 (extended)*: Abstract submission (opens /Feb 14th, 2022/) * *Apr 26th, 2022 (extended)*: Paper submission deadline (Springer LNCS) * *May 6th, 2022*: Acceptance notification * *Jun 2nd, 2022*: Workshop Day * *Jul 10th, 2022*: Camera-ready version due (post-workshop) *General Chairs* * Michael Alexander, BOKU Vienna, Austria * Anastassios Nanos, Nubificus Ltd., UK * Tommaso Cucinotta, Scuola Superiore Sant’Anna, Ital -- Tommaso Cucinotta, Associate Professor of Computer Engineering, PhD Head of the Real-Time Systems Laboratory (ReTiS) Scuola Superiore Sant'Anna, Pisa, Italy http://retis.sssup.it/~tommaso/eng/research.html

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[DEADLINE APPROACHING] InnoCyPES European Project - ESR4 position available
by Massimo Cafaro 10 Apr '22

10 Apr '22

A three years research grant is available within the InnocyPES European project. The position is related to ESR4, for research on Large Scale Data Management and Integration DEADLINE: April 19, 2022 Ukranian Researchers are strongly encouraged to apply! Downloadable application forms etc are available: official Italian documents: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 unofficial English translation: http://sara.unisalento.it/~cafaro/page-3/ InnoCyPES announcement: https://innocypes.eu/index.php/2022/03/29/esr-4-large-scale-data-management… Foreign candidates are strongly encouraged to contact Prof. Cafaro by email (massimo.cafaro(a)unisalento.it) if they need help/support in order to prepare their application. A public selection procedure is called for a research grant for collaboration in research activities (hereinafter referred to as research grant), at the Department of Innovation Engineering of the University of Salento. The location, the duration, the amount, the scientific disciplinary sector, the scientific referent, the structure available to the winner and the program of the research grant are specified below: STRUCTURE Department of Engineering for Innovation, University of Salento, Lecce, Italy DURATION 3 years REMUNERATION The research grant will last 3 (three) years. The annual remuneration, gross of charges to be borne by the beneficiary and inclusive of contributions and social security charges to be borne by the University of Salento, consists of the following items: 1. Living allowance: Euro 40,966.56 (forty thousand nine hundred and sixty-six/56); 2. Mobility allowance: Euro 7.200,00 (seven thousand two hundred/00); 3. Family allowance: 3.1.Euro 0 (zero) per researcher without family obligations; 3.2.Euro 6,000.00 (six thousand/00) for researchers with family obligations (married or with a relationship recognised by Italian law or that of the country of origin or with dependent children). SCIENTIFIC SUPERVISOR Prof. Massimo Cafaro RESEARCH TITLE LARGE SCALE DATA MANAGEMENT AND INTEGRATION DESCRIPTION This is a three year “Marie Curie ETN Early Stage Researcher position” on the following topic. The sheer quantum of data being created and collected across jurisdictions requires a carefully planned and proactive approach to data management. The need for fusion and integration of multiple data sources characterized by fragmented data ownership is driving innovative approaches to large scale distributed data management and integration to avoid inconsistent and inaccurate data. The aim is to investigate, design and implement a fully decentralized solution to provide efficient management of dynamically updated information and support for distributed queries. One or more domain specific use cases shall be identified within the context of the project, considering both the current and future needs of some of the involved partners. These nicely fit into the research plan, owing to the need of surveying the user’s requirements to begin with; simultaneously, the uses cases can also be thought of as sources of advanced data management challenges. For admission to the selection is required the possession of all the requirements provided by law for access to public employment, the eligibility requirements provided by the Marie Sklodowska- Curie Action (https://ec.europa.eu/research/participants/data/ref/h2020/wp/2018-2020/main…) and the following requirements: a) Master of Science degree in Computer Science or Computer Engineering or equivalent qualification that formally allows access to a PhD program in Italy; b) an excellent academic career; c) must not have carried out more than 4 years of research activity after obtaining the degree mentioned in the previous point within the expected starting date of the contract (indicatively fixed at 01.03.2022); d) have a background relevant to the following areas but not limited to: distributed computing, databases, distributed data management, security and privacy; knowledge of C/C++ programming languages and time series archiving/analysis is a plus; e) must not have a Ph.D. as of the contract start date; f) must not have resided or carried out the main activity of study/work in Italy for more than 12 months during the 3 years preceding the date of the beginning of the contract; g) have an excellent knowledge of the English language, in any case sufficient to ensure the performance of the activity envisaged by the contract and daily interaction in the working environment; h) must not have criminal convictions or have pending criminal proceedings of particular gravity. Lack of even one of the above requirements will result in exclusion from the selection process at any time. Qualifications obtained abroad must normally be previously recognized in Italy in accordance with current legislation. The equivalence of qualifications obtained abroad that have not already been recognized in Italy will be evaluated by the Selection Committee solely for the purpose of admitting the candidate to this call for selection. The qualifications must be possessed on the date of expiry of the deadline established for the presentation of applications for admission to this selection. The University of Salento guarantees equality and equal opportunities between men and women for the allocation of the grants in question and the protection of confidentiality in the processing of personal data, according to the provisions in force. The position is officially advertised at the University of Unisalento: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 ******************************************************************************************* Prof. Massimo Cafaro, Ph.D. Associate Professor of Parallel Algorithms and Data Mining/Machine Learning Department of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro Web https://www.unisalento.it/people/massimo.cafaro E-mail massimo.cafaro(a)unisalento.it E-mail cafaro(a)ieee.org E-mail cafaro(a)acm.org INGV National Institute of Geophysics and Volcanology Via di Vigna Murata 605 Roma CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ******************************************************************************************* --

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call for participation in online OpenMP study
by Eric Aubanel 08 Apr '22

08 Apr '22

Researchers at the University of New Brunswick are recruiting programmers with experience in parallel programming in C using OpenMP 4.0 directives to complete an online study. The goal of the research is to study the mental representations formed by parallel programmers. Understanding these representations is important for developing programming languages and tools that enhance and assist programmers in the comprehension process and other tasks. The study takes approximately 60 minutes and needs to be completed on a desktop or laptop computer (not a handheld device). The study can be accessed through the following link: https://unbfpsyc.ca1.qualtrics.com/jfe/form/SV_3QmkAJ1jqnPAFtr For more information please contact the principal investigator: Leah Bidlake (leah.bidlake(a)unb.ca). This project has been approved by the Research Ethics Board of the University of New Brunswick. Dr. Eric Aubanel, Professor Acting Assistant Dean (Graduate) Faculty of Computer Science, University of New Brunswick http://www.cs.unb.ca/profs/aubanel

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computational.science April 2022