Science Communication across STEM Disciplines:Who’s Keen, Who’s Not?

This is a program evaluation study of an interest group (IG) that advances public engagement by STEM researchers and provides opportunities to develop science communication skills. The study addresses these questions and observations: 1) Members of biomedical/biosciences disciplines are more common participants in the IG’s science communication activities than members from other STEM disciplines. 2) What are the characteristics of STEM disciplines that are more resistant/less amenable to participating in science communication activities?UT Librarie

Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection

In this paper, the future Fifth Generation (5G New Radio) radio communication system has been considered, coexisting and sharing the spectrum with the incumbent Fourth Generation (4G) Long-Term Evolution (LTE) system. The 4G signal presence is detected in order to allow for opportunistic and dynamic spectrum access of 5G users. This detection is based on known sensing methods, such as energy detection, however, it uses machine learning in the domains of space, time and frequency for sensing quality improvement. Simulation results for the considered methods: k-Nearest Neighbors and Random Forest show that these method significantly improves the detection probability

Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection

In this paper, the future Fifth Generation (5G New Radio) radio communication system has been considered, coexisting and sharing the spectrum with the incumbent Fourth Generation (4G) Long-Term Evolution (LTE) system. The 4G signal presence is detected in order to allow for opportunistic and dynamic spectrum access of 5G users. This detection is based on known sensing methods, such as energy detection, however, it uses machine learning in the domains of space, time and frequency for sensing quality improvement. Simulation results for the considered methods: k-Nearest Neighbors and Random Forest show that these method significantly improves the detection probability

Uberization of telecom networks for cost-efficient communication and computing

This paper discusses the uberization of telecommunication and computing network services. The Uber-like platform business model is discussed for application in future networks together with interesting analogies of communication and computing (2C) resource-sharing models. The economy of this sharing is discussed, and some recommendations for network uberization are provided.Comment: 7 pages, 4 figures, 1 tabl

„Slim, Smart and Happy?” Stigmatization of Obesity Among Preschool Children

Nowadays, the idea “the slimmer – the better” has gained such an enormous power in our culture that it significantly affects even the youngest members of modern society. For this reason, obesity is particularly traumatic for children and young people. It was decided to examine whether these important cultural changes also apply to the youngest members of today’s Polish society, moreover there is lack of studies of this problem in polish literature. Hence, the authors of this paper have focused on the question whether and how preschool children stigmatise obesity?The study was conducted on a group of 122 children with a three-silhouette technique, based on a method proposed by Tremblay et al. (2011). The results show that being classed as overweight means being perceived as lacking in physical and interpersonal attractiveness, so obesity appears to elicit a very strong negative stigma. The features of the slim body shape equal physical and interpersonal attractiveness (but only in the field of playing together)

MIMO beam selection in 5G using neural networks

In this paper, we consider the cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple-input-multiple-output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user equipment to reduce latency in beam/cell identification. Due to high path-loss in mmWave systems, the beam-forming technique is extensively used to increase Signal-to-Noise Ratio (SNR). When seeking to increase user discovery distance, a narrow beam must be formed. Thus, the number of possible beam orientations and consequently time needed for the discovery increases significantly when a random scanning approach is used. The idea presented here is to reduce latency by employing artificial intelligence (AI) or machine learning (ML) algorithms to guess the best beam orientation using context information from the Global Navigation Satellite System (GNSS), lidars, and cameras, and use the knowledge to swiftly initiate communication with the base station. To this end, here, we propose a simple neural network to predict beam orientation from GNSS and lidar data. Results show that using only GNSS data one can get acceptableperformance for practical applications. This finding can be useful for user devices with limited processing power

Secure Federated Learning for Cognitive Radio Sensing

This paper considers reliable and secure Spectrum Sensing (SS) based on Federated Learning (FL) in the Cognitive Radio (CR) environment. Motivation, architectures, and algorithms of FL in SS are discussed. Security and privacy threats on these algorithms are overviewed, along with possible countermeasures to such attacks. Some illustrative examples are also provided, with design recommendations for FL-based SS in future CRs.Comment: 7 pages, 6 figure