Network Capacity Bound for Personalized PageRank in Multimodal Networks

In a former paper the concept of Bipartite PageRank was introduced and a theorem on the limit of authority flowing between nodes for personalized PageRank has been generalized. In this paper we want to extend those results to multimodal networks. In particular we introduce a hypergraph type that may be used for describing multimodal network where a hyperlink connects nodes from each of the modalities. We introduce a generalisation of PageRank for such graphs and define the respective random walk model that can be used for computations. we finally state and prove theorems on the limit of outflow of authority for cases where individual modalities have identical and distinct damping factors.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1702.0373

The CAMALIOT project

This invited presentation was given at an information event about the European Space Agency’s (ESA) Navigation Innovation and Support Programme (NAVISP) hosted by the Austrian Agency for the Promotion of Science (FFG) in preparation for the ESA Ministerial Conference 2022. The presentation was about the CAMALIOT project, which is currently funded through NAVISP and by FFG, outlining the initial results and what the next steps in the project are. In particular, information about the CAMALIOT crowdsourcing campaign (being run by IIASA) was provided as well as the status of the CAMALIOT machine learning infrastructure and the science uses cases in the project

A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of observations from both GPS and Galileo constellations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations

The advantages and disadvantages of remote working from the perspective of young employees

This paper explores how remote work is perceived by young employees. On the basis of literature review, pilot study was undertaken (sample Olson, 1983, DeSanctis, 1984, Bailey, and Kurland, 2002; Madsen, 2011; Grant, Wallace, and Spurgeon, 2013). The results of the pilot study undertaken in Poland confirmed literature findings. It turned out that the most important for young remote workers are: flexible working hours and saving time on commuting to work. The main disadvantages of remote work include: difficulty in separating home affairs from the professional ones, social isolation and greater organizational requirements. The results of pilot study proved that other benefits and drawbacks are irrelevant. An interesting phenomenon is that in the age of social networking and extensive communication tools the second disadvantage of remote working for young remote workers is the risk of social isolation

Approaches to “Cold-Start” in recommender systems

The paper explores the possibilities of handling cold start problems for recommenders associated with document-map based search engines

Cyclic Bayesian Network : Markov Process Approach

The paper proposes a new interpretation of the concept of cyclic Bayesian Networks, based on stationary Markov processes over feature vector state transitions

An algorithm for finding most likely explanations in valuation based systems

A method for finding a number of best explanations in so-called valuation based system is presented. Roughly speaking, the method allows to sort (decreasingly or increasingly) a function of many variables without explicit computation of values of this function. The only condition is that the function be decomposable, i.e. can be expressed as a combination of a number of low-dimensional functions called components. Two cases are considered: the combination operator has an inverse and a more elaborated case when the combination operator has no inverse