Impact of decision-making system in social navigation

Facing human activity-aware navigation with a cognitive architecture raises several difficulties integrating the components and orchestrating behaviors and skills to perform social tasks. In a real-world scenario, the navigation system should not only consider individuals like obstacles. It is necessary to offer particular and dynamic people representation to enhance the HRI experience. The robot’s behaviors must be modified by humans, directly or indirectly. In this paper, we integrate our human representation framework in a cognitive architecture to allow that people who interact with the robot could modify its behavior, not only with the interaction but also with their culture or the social context. The human representation framework represents and distributes the proxemic zones’ information in a standard way, through a cost map. We have evaluated the influence of the decision-making system in human-aware navigation and how a local planner may be decisive in this navigation. The material developed during this research can be found in a public repository (https://github.com/IntelligentRoboticsLabs/social_navigation2_WAF) and instructions to facilitate the reproducibility of the results.

miND pipeline AWS EC2 installation and setup v2

miND is a NGS data analysis pipeline for smallRNA sequencing data. In this protocol, the pipeline is setup and run on an AWS EC2 instance with example data from a public repository. Please see the publication paper on F1000 for more details on the pipeline and how to use it.

Anymatrix: an extensible MATLAB matrix collection

Anymatrix is a MATLAB toolbox that provides an extensible collection of matrices with the ability to search the collection by matrix properties. Each matrix is implemented as a MATLAB function and the matrices are arranged in groups. Compared with previous collections, Anymatrix offers three novel features. First, it allows a user to share a collection of matrices by putting them in a group, annotating them with properties, and placing the group on a public repository, for example on GitHub; the group can then be incorporated into another user’s local Anymatrix installation. Second, it provides a tool to search for matrices by their properties, with Boolean expressions supported. Third, it provides organization into sets, which are subsets of matrices from the whole collection appended with notes, which facilitate reproducible experiments. Anymatrix comes with 146 built-in matrices organized into 7 groups with 49 recognized properties. The authors continue to extend the collection and welcome contributions from the community.

miND pipeline AWS EC2 installation and setup v1

miND is a NGS data analysis pipeline for smallRNA sequencing data. In this protocol, the pipeline is setup and run on an AWS EC2 instance with example data from a public repository. Please see the publication paper on F1000 for more details on the pipeline and how to use it.

pISA-tree - a data management framework for life science research projects using a standardised directory tree

We have developed pISA-tree, a straightforward and flexible data management solution for organisation of life science project-associated research data and metadata. It enables on-the-fly creation of enriched directory tree structure (project/Investigation/Study/Assay) via a series of sequential batch files in a standardised manner based on the ISA metadata framework. The system supports reproducible research and is in accordance with the Open Science initiative and FAIR principles. Compared with similar frameworks, it does not require any systems administration and maintenance as it can be run on a personal computer or network drive. It is complemented with two R packages, pisar and seekr, where the former facilitates integration of the pISA-tree datasets into bioinformatic pipelines and the latter enables synchronisation with the FAIRDOMHub public repository using the SEEK API. Source code and detailed documentation of pISA-tree and its supporting R packages are available from https://github.com/NIB-SI/pISA-tree.

A smart Warehouse 4.0 approach for the pallet management using machine vision and Internet of Things (IoT): A real industrial case study

Printing companies are commonly SMEs with high flow of materials, which management could be significantly improved through the digitalization. In this study we propose a smart Warehouse 4.0 solution by using QR code, open-source software tools for machine vision and conventional surveillance equipment. Although there have been concerns regarding the usage of QR in logistics, it has shown to be suitable for the particular use-case as pallets are static in the interwarehouse. The reliability of reading of QR codes was achieved by using multiple IP cameras, so that sub-optimal view angle or light reflection is compensated with alternative views. Since surveillance technology and machine vision are constantly evolving and becoming more affordable, we report that more attention needs to be invested into their adaptation to fit the needs and budgets of SMEs, which are the industrial cornerstone in the most developed countries. The demo of proposed solution is available on the public repository https://github.com/ArsoVukicevic/PalletManagement/.

COVID-19 L·OVE REPOSITORY IS HIGHLY COMPREHENSIVE AND CAN BE USED AS A SINGLE SOURCE FOR COVID-19 STUDIES

ABSTRACTObjectiveCOVID-19 Living OVerview of Evidence (COVID-19 L·OVE) is a public repository and classification platform for COVID-19 articles. The repository contains over 430,000 articles as of 20 September 2021 and intends to provide a one-stop shop for COVID-19 evidence. Considering that systematic reviews conduct high-quality searches, this study assesses the comprehensiveness and currency of the repository against the total number of studies in a representative sample of COVID-19 systematic reviews.MethodsOur sample was generated from all the studies included in the systematic reviews of COVID-19 published during April 2021. We estimated the comprehensiveness of COVID-19 L·OVE repository by determining how many of the individual studies in the sample were included in the COVID-19 L·OVE repository. We estimated the currency as the percentage of studies that were available in the COVID-19 L·OVE repository at the time the systematic reviews conducted their own search.ResultsWe identified 83 eligible systematic reviews that included 2132 studies. COVID-19 L·OVE had an overall comprehensiveness of 99.67% (2125/2132). The overall currency of the repository, that is, the proportion of articles that would have been obtained if the search of the reviews was conducted in COVID-19 L·OVE instead of searching the original sources, was 96.48% (2057/2132). Both the comprehensiveness and the currency were 100% for randomised trials (82/82).ConclusionThe COVID-19 L·OVE repository is highly comprehensive and current. Using this repository instead of traditional manual searches in multiple databases can save a great amount of work to people conducting systematic reviews and would improve the comprehensiveness and timeliness of evidence syntheses. This tool is particularly important for supporting living evidence synthesis processes

Lexibank: A public repository of standardized wordlists with computed phonological and lexical features

The past decades have seen substantial growth in digital data on the world's languages. At the same time, the demand for cross-linguistic datasets has been increasing, as witnessed by numerous studies devoted to diverse questions on human prehistory, cultural evolution, and human cognition. Unfortunately, the majority of published datasets lack standardization which makes their comparison difficult. Here, we present the first step to increase the comparability of cross-linguistic lexical data. We have designed workflows for the computer-assisted lifting of datasets to Cross-Linguistic Data Formats, a collection of standards that increase the FAIRness of linguistic data. We test the Lexibank workflow on a collection of 100 lexical datasets from which we derive an aggregated database of wordlists in unified phonetic transcriptions covering more than 2000 language varieties. We illustrate the benefits of our approach by showing how phonological and lexical features can be automatically inferred, complementing and expanding existing cross-linguistic datasets.

Sift-PULs: A public repository for specific functional polysaccharide utilization loci

Background Polysaccharide utilization loci (PULs) were bacterial gene clusters encoding genes responsible for polysaccharide utilization process. PUL studies are blooming in recent years but the biochemical characterization speed is relative slow. There is a growing demand for PUL database with function annotations. Results Using signature genes corresponding for specific polysaccharide, 10422 PULs specific for 6 polysaccharides (agar, alginate, pectin, carrageenan, chitin and β-manan) from various bacterial phyla were predicted. Then online website of specific functional polysaccharide utilization loci (Sift-PULs) was constructed. Sift-PULs provides a repository where users could browse, search and download interested PULs without registration. Conclusions The key advantage of Sift-PULs is to assign a function annotation of each PUL, which is not available in existing PUL databases. PUL's functional annotation lays a foundation for studying novel enzymes, new pathways, PUL evolution or bioengineering. The website is available on http://sift-puls.org