Abstract
Besides the large amount of traffic that radio access and backhaul networks need to accommodate, the interest in low-latency communications is emerging. The goal is to reliably transmit high bitrates through the network under controlled delays, thus enabling human and machine-oriented communications. A critical aspect that must be addressed is the latency introduced by network queues. The literature has been focused on studying the queue size in wired networks, but wireless networks bring up additional challenges due to their dynamic characteristics. The problem is exacerbated in high dynamic networks, such as Flying Networks (FNs) composed of Unmanned Aerial Vehicles (UAVs), which have emerged to provide communications anywhere, anytime. The main contribution of this paper is a Proactive Queue Management (PQM) solution for FNs with controlled topology. PQM takes advantage of the knowledge of the future FN topologies and the offered traffic to define in advance the queue size of the communications nodes over time, in order to maximize the throughput with stochastic delay guarantees. The FN performance achieved using PQM is evaluated by means of ns-3 simulations, showing gains regarding aggregate throughput and average delay.

Abstract
Virtual power plants (VPPs) are defined as an aggregator of different types of energy resources and flexibility, coordinated by VPP owner through a smart control system. A correct establishment of a VPP will result in reduced electricity costs for the consumers within the VPP. One of the key aspect of VPP's success is the consumer engagement in order to manage their flexibilities effectively. Gamification is an efficient way of learning and engagement, which can efficiently change the behavior of consumers towards participating in programs provided by VPPs for energy cost reduction. In this paper, a gamification-based approach for consumer engagement is proposed and a methodology based on Fogg's behavior model and Kim's model on player types is developed to examine the suitability of available gamification applications for energy saving/efficiency in the context of a VPP. Seven gamification applications are analyzed and evaluated based on the developed methodology and the results are provided. © 2020 IEEE.

Abstract
Intelligent wheelchairs allow individuals to move more freely, safely, and facilitate users’ interaction with the wheelchair. This paper presents the results focused on the study and analysis of the state of the art related to topics as interaction, interfaces, intelligent wheelchairs and the analysis of the Intellweelsproject. The main goal is to create and implement a multimodal adaptive interface to be used as the control and interaction module of an intelligent wheelchair. Moreover, it will be important to have in mind the usability, by facilitating the control of a complex system, interactivity, by allowing the control using diverse kinds of input devices, and expansibility, by integrating easily with several intelligent external systems. This project features a complex input/output system with linked parameters simplified by a node system to create the input/output actions with automatic input recording and intuitive output association as well as a powerful, device-agnostic design, providing an easy way to extend the inputs, outputs and event the user interface. Results reveal a positive users’ feedback and a responsive way when using the multimodal interface in simulated environment. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
Network security encompasses distinct technologies and protocols, being behaviour based network Intrusion Detection Systems (IDS) a promising application to detect and identify zero-day attacks and vulnerabilities exploits. In order to overcome the weaknesses of signature-based IDS, behaviour-based IDS applies a wide set of machine learning technologies to learn the normal behaviour of the network, making it possible to detect malicious and not yet seen activities. The machine learning techniques that can be applied to IDS are vast, as are the methods to generate the datasets used for testing. This paper aims to evaluate CSE-CIC-IDS2018 dataset and benchmark a set of supervised bioinspired machine learning algorithms, namely CLONALG Artificial Immune System, Learning Vector Quantization (LVQ) and Back-Propagation Multi-Layer Perceptron (MLP). The results obtained were also compared with an ensemble strategy based on a majority voting algorithm. The results obtained show the appropriateness of using the dataset to test behaviour based network intrusion detection algorithms and the efficiency of MLP algorithm to detect zero-day attacks, when comparing with CLONALG and LVQ.

Abstract
With the appearance of Shared Autonomous Vehicles there will no longer be a driver responsible for maintaining the car interior and well-being of passengers. To counter this, it is imperative to have a system that is able to detect any abnormal behaviors, more specifically, violence between passengers. Traditional action recognition algorithms build models around known interactions but activities can be so diverse, that having a dataset that incorporates most use cases is unattainable. While action recognition models are normally trained on all the defined activities and directly output a score that classifies the likelihood of violence, video anomaly detection algorithms present themselves as an alternative approach to build a good discriminative model since usually only non-violent examples are needed. This work focuses on anomaly detection and action recognition algorithms trained, validated and tested on a subset of human behavior video sequences from Bosch's internal datasets. The anomaly detection network architecture defines how to properly reconstruct normal frame sequences so that during testing, each sequence can be classified as normal or abnormal based on its reconstruction error. With these errors, regularity scores are inferred showing the predicted regularity of each frame. The resulting framework is a viable addition to traditional action recognition algorithms since it can work as a tool for detecting unknown actions, strange/violent behaviors and aid in understanding the meaning of such human interactions.

Abstract
<p>Atmospheric new particle formation (NPF) generates secondary aerosol particles into the lower atmosphere via gas-to-particle phase transition. Secondary aerosol particles dominate the total particle number concentration and are an important source for cloud condensation nuclei <sup>[1]</sup>. NPF typically begins with clustering among gaseous molecules. Once the newly formed clusters attain a size larger than the critical cluster size (~1.5 nm), their growth to larger sizes is energetically favoured and eventually they become nanoparticles <sup>[2]</sup>. NPF is often observed with the participation of air ions <sup>[3]</sup> and sometimes is induced by ions <sup>[4]</sup>. Air ions are a constituent of atmospheric electricity. The presence of the Earth-atmosphere electric field poses an electrical force on air ions. The earth-atmosphere electric field exhibits variability at different time scales under fair-weather conditions <sup>[5]</sup>. It is therefore interesting to understand whether the Earth-atmosphere electric field influences atmospheric new particle formation.</p> <p>We analysed the Earth-atmosphere electric field together with the number size distribution data of air ions and aerosol particles under fair-weather conditions measured at Hyytiälä SMEAR II station in Southern Finland <sup>[6]</sup>. The electric field were measured by two Campbell CS 110 field mills in parallel. Air ion data were obtained with a Balance Scanning Mobility Analyser (BSMA) and a Neutral and Air Ion Spectrometer (NAIS), and aerosol particle data with a Differential Mobility Particle Sizer (DMPS). We used condensation Sinks (CS) derived from the DMPS measurement, air temperature, relative humidity, wind speed, global radiation as well as brightness derived from the global radiation measurement to assist the analysis. The measured earth-atmosphere electric field on NPF days was higher than on non-NPF days. We found that under low CS conditions, the electric field can enhance the formation of 1.7-3 nm air ions, but the concentration of 1.7-3 nm ions decreased with an increasing electric field under high CS conditions.</p> <p>References:</p> <p>[1]       Kerminen V.-M. et al., Environ. Res. Lett. <strong>2018</strong>, 13, 103003.</p> <p>[2]       Kulmala M. et al., Science <strong>2013</strong>, 339, 943-946.</p> <p>[3]       Manninen H. E. et al., Atmos. Chem. Phys. <strong>2010</strong>, 10, 7907-7927.</p> <p>[4]       Jokinen T. et al., Science Advances <strong>2018</strong>, 4, eaat9744.</p> <p>[5]       Bennett A. J., Harrison R. G., Journal of Physics: Conference Series <strong>2008</strong>, 142, 012046.</p> <p>[6]       Hari P., Kulmala M., Boreal Environ. Res. <strong>2005</strong>, 10, 315-322.</p>