Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Millimeter-Wave Network Performance and Mobility Management Overhead in Urban Cellular Deployments with Realistic Pedestrian Traffic and Blockages

Millimeter-wave (mm-wave) frequencies are seen as the new frontier in enabling high capacity for 5G-and-beyond cellular networks. However, spatio-temporal variations of the mm-wave channel impose significant challenges for high rate error-free communication, due to increased propagation loss and blockage susceptibility. In this paper, we analyze the end-to-end mobile user performance and the extent of mobility management overhead in the presence of dynamic pedestrian blockages. We use site-specific propagation data obtained via high resolution ray-tracing and realistic pedestrian traffic to implement site-specific user mobility and pedestrian blockage models, and integrate these in an extensive ns-3 framework to study the end-to-end mobile user performance. Our results show that the number of mm-wave mobility management events -- beam tracking, beam steering and handover -- strongly depends on the pedestrian walk nature and number of blockage events, i.e. the primary mm-wave serving link can be blocked for up to 36% of a walk duration. These frequent short-term pedestrian blockages can lead to TCP throughput degradation of up to 35% and TCP round-trip-time spikes of over 1 s, compared to the achievable network performance when pedestrian blockages are not considered, assuming mobility management with up-to-date CSI. We also show that stale CSI -- arising from delay in periodic beam training procedures -- results in more frequent and sub-optimal mobility management decisions and an additional TCP throughput degradation of up to 30%, compared to the achievable TCP throughput assuming mobility management with up-to-date CSI.
Light Field Near-eye Display Resolution Enhancement Using a Staggered Microlens Array

We propose a method of improving the resolution of near-eye light field displays (LFDs) using a staggered microlens array (MLA). The staggered matrix is formed by interlacing multiple identical sparse MLAs with the same microlens periodicity. The sparse MLAs are rectilinearly shifted relative to each other such that the distance between neighboring microlenses is not one fixed value. We demonstrate that by introducing this irregular shift, the staggered MLA-based LFD can support a higher spatial display resolution.
Classifying Incomplete Vectors using Decision Trees

: An attempt is made to address the problem of classifying incomplete vectors using decision trees. The essence of the approach is the proposal that in supervised learning classification of incomplete vectors can be improved in probabilistic terms. This approach, which is based on the a priori probability of each value determined from the instances at that node of the tree that has specified values, first exploits the total probability and Bayes’ theorems and then the probit and logit model probabilities. The proposed approach (developed in three versions) is evaluated using 21 machine learning datasets from its effect or tolerance of incomplete test data. Experimental results are reported, showing the effectiveness of the proposed approach in comparison with multiple imputation and fractioning of instances strategy.
The Importance of Place in Creative Activity - a Proposal for an Integral Approach

Abstract The subject of the discussion in this paper is place in the sense of physical space and its role in creative activity. In the text, we emphasize that a physical place is implicitly defined as creative by the people who conduct or have conducted their creative activity there. We emphasize that it promotes a creative lifestyle and poses a challenge for education. We are looking for distinguishing features of creative places and spaces. In the paper, we also present a proposal to analyse creative places using Wilber’s (2007) integral theory and theory of affordance. Starting from the concepts of Florida (2010), Amabile (2019), Oldenburg (1989), and Palasma (2013), we list the conditions that a place should meet to promote creative activity. In the conclusions, we point to the role of creative affordances in education.
TAGWAR: An Annotated Corpus for Sequence Tagging of War Incidents

Sequence tagging of free text constitutes an important task in natural language processing (NLP). In this work, we focus on the problem of automatic sequence tagging of news articles reporting on wars. In this context, tags correspond to details surrounding war incidents where a large number of casualties is observed, such as the location of the incident, its date, the cause of death, the actor responsible for the incident, and the number of casualties of different types (civilians, non-civilians, women and children). To this end, we begin by building TAGWAR, a manually sequence tagged dataset consisting of 804 news articles around the Syrian war, and use this dataset to train and test three state-of-the-art, deep learning based, sequence tagging models: BERT, BiLSTM, and a plain Conditional Random Field (CRF) model, with BERT delivering the best performance. Our approach incorporates an element of input sensitivity analysis where we attempt modeling exclusively at the level of articles’ titles, versus titles and first paragraph, and finally versus full text. TAGWAR is publicly available at: https://doi.org/10.5281/zenodo.3766682.
Directional-IPeR: Enhanced Direction and Interest Aware Peoplerank for Opportunistic Mobile Social Networks

Network infrastructures are being continuously challenged by virtue of increased demand, resource-hungry applications, and at times of crisis when people need to work from homes such as the current Covid-19 epidemic situation, where most of the countries applied partial or complete lockdown and most of the people worked from home. Opportunistic Mobile Social Networks (OMSN) prove to be a great candidate to support existing network infrastructures. However, OMSNs have copious challenges comprising frequent disconnections and long delays. In this research, we aim to enhance the performance of OMSNs including delivery ratio and delay. We build upon an interest-aware social forwarding algorithm, namely Interest Aware PeopleRank (IPeR) in two ways 1) By embracing directional forwarding (Directional-IPeR), and (2) By utilizing a combination of Directional forwarding and multi-hop forwarding (DMIPeR). Different interest distributions and users' densities are simulated using the Social-Aware Opportunistic Forwarding Simulator (SAROS). The results show that Directional-IPeR with a tolerance factor of 75% performed the best in terms of delay and delivery ratio compared to IPeR, and two other algorithms, namely MIPeR and DMIPeR. © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
Dimethyl sulfoxide as a strongly coordinating solvent: 3′,4′-dihydroxyflavone-Cu(II)-DMSO system case study

Abstract Dimethyl sulfoxide (DMSO) is an aprotic organic solvent widely used in laboratory practice due to its ability to dissolve both polar and nonpolar compounds. However, DMSO is also commonly known as a strongly coordinating solvent, especially towards transition metal containing complexes. In this study, estimation of the coordination ability of DMSO towards the Cu(II) ion was attempted, employing a model system composed of 3′,4′-dihydroxyflavone-Cu(II) complex in the presence of explicit DMSO molecules, using the density functional theory (DFT). Nature of the Cu-DMSO chemical interaction (i.e. Cu-O bonding) was studied within the framework of quantum theory of atoms in molecules (QTAIM). Impact of DMSO coordination on the charge and spin distribution at Cu(II) ion was inspected using Mulliken population and QTAIM analysis.
Non-Formal Education Institutions in the System of Civic Buildings in Ukraine

Abstract The article presents individual positions of the author’s research on the integration of non-formal education institutions in the system of civil buildings of Ukraine. The following scientific views on the phenomenon of non-formal education from related industries are analyzed: pedagogy, sociology, psychology, economics and a systematic approach to education as a phenomenon. The prognostic positions of the development of a typological network of non-formal education institutions in connection with the system of public buildings are described. The links are revealed – system objects of a combination of non-formal education institutions with other public buildings. The stages of the study of this problem are briefly described. The proposal on the creation of a universal educational cluster, as a characteristic of non-formal education institutions, is presented, and an example of the formation of the educational block of the center of scientific and technical creativity based on such a cluster is given.
Using Animation to Visualize Spatio-Temporal Varying COVID-19 Data

CoronaViz (http://coronaviz.umiacs.io) is a research prototype developed by us to enable the dynamic map visualization of COVID-19 related variables including the number of confirmed cases, active cases, recoveries, and deaths all on a daily basis from the Johns Hopkins University web site at ter.ps/coronajhu, by allowing the underlying spatial region and the spanned time interval to vary. Any combination of the variables can be viewed. subject to a possibility of clutter which is avoided by the use of concentric circles (termed geo-circles) whose radius values correspond to the variable values. The variable values are provided both on cumulative and day-by-day bases. The visualization enables spatial and temporal variation.
The Chemistry of sulphones and sulphoxides

General and Theoretical Structural Chemistry Stereochemistry, Conformation and Chiroptical Properties Thermochemistry Detection, Identification and Determination Mass Spectra Synthesis of R2SO2 Synthesis of R2SO Cyclic Sulphones and Sulphoxides Electronic Effects of SO and SO2 Groups Hydrogen Bonding and Complexing Properties of R2SO2 and R2SO Formation and Behaviour of a-Sulphonyl Carbanions Rearrangements Involving R2SO Rearrangements Involving R2SO2 Groups Synthetic Uses of Sulphones Asymmetric Syntheses using Sulphoxides Methionine Sulfoxide Photochemistry of R2SO and RSO2 Radiation Chemistry Reduction of R2SO and R2SO2 Oxidation of R2SO and R2SO2 Electrochemistry Electron Transfer Reactions of R2SO and R2SO2 Radical Chemistry of R2SO Radical Chemistry of R2SP2
Analysis of segregated witness implementation for increasing efficiency and security of the Bitcoin cryptocurrency

ABSTRACT The purpose of this paper is to present mechanisms and algorithms implemented for improving Bitcoin cryptocurrency efficiency and security and to examine the block propagation times from a selected period before and after SegWit was introduced. In this paper, Segregated Witness Implementation issues were verified based both on the simulation and real data from the Bitcoin network. Based on the block propagation times calculated in the simulator, as well as bitcoin network real data, the efficiency and safety of Bitcoin have been analysed and validated.
Improving Community Health and Wellbeing Through Multi-Functional Green Infrastructure in Cities Undergoing Densification

Abstract Evidence shows that maintaining a relationship with nature is essential for human health and wellbeing. This is of great importance when migration to urban areas is increasing globally and the need for nature as well as green and blue spaces as a source of recreation and relaxation is highly regarded for the health and wellbeing of local communities. Sustainable urban development and alternative design solutions to address urban compactness and densification are becoming increasingly important tools to counteract the adverse effects of urban sprawl. In the context of the highly compact bicultural capital city of Wellington, Aotearoa-New Zealand, this paper examines the effects of urban densification and compact city development in urban green spaces. It explores how architecture and landscape architecture can transform urban environments into desirable places to live and capitalise on the potentials of interstitial spaces, outdated zoning and changing land-use. To achieve that, it looks at green and blue infrastructure design solutions and opportunities that foster sustainable intensification and by offering new views for health and wellbeing that improve the social, cultural and environmental health of the city.
Knowledge-based Reliability Metrics for Social Media Accounts

: The growth of social media as an information medium without restrictive measures on the creation of new accounts led to the rise of malicious agents with the intend to diffuse unreliable information in the network, ultimately affecting the perception of users in important topics such as political and health issues. Although the problem is being tackled within the domain of bot detection, the impact of studies in this area is still limited due to 1) not all accounts that spread unreliable content are bots, 2) human-operated accounts are also responsible for the diffusion of unreliable information and 3) bot accounts are not always malicious (e.g. news aggregators). Also, most of these methods are based on supervised models that required annotated data and updates to maintain their performance through time. In this work, we build a framework and develop knowledge-based metrics to complement the current research in bot detection and characterize the impact and behavior of a Twitter account, independently of the way it is operated (human or bot). We proceed to analyze a sample of the accounts using the metrics proposed and evaluate the necessity of these metrics by comparing them with the scores from a bot detection system. The results show that the metrics can characterize different degrees of unreliable accounts, from unreliable bot accounts with a high number of followers to human-operated accounts that also spread unreliable content (but with less impact on the network). Furthermore, evaluating a sample of the accounts with a bot detection system shown that bots compose around 11% of the sample of unreliable accounts extracted and that the bot score is not correlated with the proposed metrics. In addition, the accounts that achieve the highest values in our metrics present different characteristics than the ones that achieve the highest bot score. This provides evidence on the usefulness of our metrics in the evaluation of unreliable accounts in social networks.
Tracing the Evolution of Approaches to Semantic Similarity Analysis

Capturing the essence of semantic similarity of words or concepts in order to quantify it and measure has been an inspiring challenge for the last decades. From corpus-based statistics to metrics based on structured knowledge bases, a plethora of methods has been proposed in several branches of Artificial Intelligence. Recently, with the advent of knowledge graphs, a renewed interest in similarity metrics can be observed. Choosing appropriate metrics that will work best in a given situation is not a trivial task. To help navigate through the semantic similarity algorithms and understand the characteristics of them, we have analyzed the fundamental proposals in this domain and the evolution of them over the years. In this paper, we present a review of the approaches to measuring semantic similarity of entities in knowledge bases. We organize the findings into a taxonomy and analyze the relations between and within the identified categories. To complement the research with a practical solution, we present a new tool that supports the literature review process with graph-based and temporal visualizations.
Exogenous Data for Load Forecasting: A Review

: Electrical power load forecasting deﬁnes strategies for utilities, power producers and individuals that participate in a smart grid. While it is well established in planning processes for production and utilities, the importance of accurate forecasting increases for individuals. The ongoing deregulation of the electricity market enables energy trading by individuals, requiring an accurate estimation of the production and consumption. Research on forecast for aggregated demand shows that including features for the forecast from sources, called exogenous, additional to the purely historical consumption data allows to obtain higher accuracy. In fact, their usage demonstrated to be able to explain the large variability observed in the power demand, taking into account the individual inﬂuences. Anyway, the inﬂuence of exogenous data is hardly investigated for individual forecasting, due to the minor prevalence of this analysis to date. This review shows the beneﬁt of exogenous data usage and the necessity of detailed research on the input features and their inﬂuence on detailed, individual level, forecasts of power demand. Eventually, this contribution is concluded by the presentation of open issues and research directions for electric smart communities that the authors would like to address.
Conventional and unconventional materials used in the production of brake pads – review

Abstract Brakes are one of the most important components of vehicle. The brake system must be reliable and display unchanging action throughout its use, as it guards the health and life of many people. Properly matched friction pair, a disc and brake pad (in disc brakes), have a great impact on these factors. In most cases, the disc is made of grey cast iron. The brake pads are far more complex components. New technologies make it possible to develop materials with various compositions and different proportions, and connect them permanently in fully controllable processes. This elaboration shows that all these factors have a greater or lesser impact on the coefficient of friction, resistance to friction wear and high temperature, and brake pad’s operating life. This review collects the most important, the most interesting, and the most unconventional materials used in production of brake pads, and characterizes their impact on the tribological properties of pads.
Virtual Reality (VR) in Engineering Education and Training: A Bibliometric Analysis

Although Virtual Reality (VR) was first mentioned in the 60s, the research interest into the technology and its application are still gaining much attention globally. VR technology had evolved and had found niche application in many fields including entertainment, tourism, healthcare, manufacturing, education and more. A notable characteristic of the VR technology is that it seeks to immerse the user into the intended environment and narrative, allowing high user interaction and involvement. These qualities are very beneficial for engineering education. In the past, cost, hardware capabilities and availability, connectivity and other issues had hindered VR wider adoption and application. However, it is changing with the advent of more devices, the involvement of more consumer electronic players and the growth of digitization. This paper seeks to report on the research on virtual reality in engineering education over the past 26 years. The study conducts a bibliometric analysis to reflect the trend of VR in engineering education settings, thus identifying the possible emerging trends. An extensive literature search was conducted using the Scopus database and was analyzed using Vosviewer and Excel. There is an increasing trend of VR research related to the engineering education settings with an evident increase in the scope, the coverage and the citations figures. There is tremendous growth in the number of publications and citations in the recent past four years of the study. The most active author in the field is Sampaio, and the top universities where the publications are affiliated with are Purdue University and Clemson University. The majority of the publications were also found to be affiliated with the "ASEE Annual Conference and Exposition Conference Proceedings". The United States had the honor of being the country with the majority of publications from this study. There is a continuous shift of research interest observed through the publications keywords, and this provides an indication of the dynamic progression of research direction among researcher in the field of study. The findings of this study provide a good overview of the trend in research related to VR in engineering education and can serve as a guide to academics seeking to research or adapt the usage of VR in engineering settings.
An Open Dataset of Operational Mobile Networks

Mobile networks have become ubiquitous and the primary means to access the Internet, and the traffic they generate has rapidly increased over the last years. The technology and service diversity in mobile networks call for extensive and accurate measurements to ensure the proper functioning of the networks and rapidly spot impairments. However, the measurement of mobile networks is complicated by their scale, and, thus, expensive, especially due to the diversity of deployments, technologies, and web services. In this paper, we present and provide access to the largest open international mobile network dataset collected using the MONROE platform spanning six countries, 27 mobile network operators, and 120 measurement nodes. We use them to run measurements targeting several web services from January 2018 to December 2019, collecting millions of TCP and UDP flows using these commercial mobile networks. We illustrate the data collection platforms and describe some of the main experiments. Besides a high-level overview of the dataset, we provide two practical use cases. First, we show how our data can be used as a proxy for web service performance. Second, we study the content delivery infrastructure of Facebook.
Intelligent Safety Monitoring and Early Warning System for Construction Site

Faced with the complex environment and difficult construction of infrastructure projects, designing an intelligent safety monitoring and early warning system for construction sites can effectively detect existing violations and reduce the probability of accidents. Existing violation detection methods for construction sites mainly include hand-crafted feature extraction and deep neural network. However, the method of extracting features is usually difficult to design and the architecture of the deep learning-based method is simple, which might lead to poor detection performance in extreme cases (Insufficient light, small detection object, occlusion, etc.) and cannot be used in actual detection environment. Therefore, we improve the existing target detection algorithm by adding image preprocessing module, multi-scale feature fusion module, and repulsion loss term. We also use the KCF algorithm to continuously track targets to identify specific violations. On this basis, we develop an intelligent safety monitoring and early warning system to classify the detected violations and send the information to the responsible person in time, which significantly improves the management capacity at the construction site. Through a series of experiments, we compared the impact of different modules on detection accuracy. The results show that our model has a significant improvement compared to existing methods on our dataset, especially in harsh environments.