Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Network Service Orchestration in Heterogeneous Domains for Next-Generation Mobile Networks

5G marks the beginning of a revolution in the mobile network ecosystem, transitioning to a network of services to satisfy the demands of new telecom players, the vertical industries. This revolution implies a redesign of the overall mobile network architecture to provide it with the automation and programmability capabilities to handle the envisioned heterogeneity, flexibility, and dynamicity. Software Defined Networking (SDN), Network Function Virtualization (NFV), and Network slicing are key enabling concepts to provide such capabilities. They are complementary, but they are still in its infancy and the synergies between them must be exploited to realise the mentioned vision. This thesis contributes to its development and integration in next generation mobile networks by designing an end-to-end (E2E) network service orchestration architecture. This proposed architecture, which is aligned with some guidelines and specifications provided by main standardization bodies, goes beyond current management and orchestration (MANO) platforms to fulfill network service lifetime requirements in heterogeneous multi-technology network infrastructures shared by concurrent instances of diverse network services. Other relevant contribution of this work is the multi-administrative domain capabilities of the proposed architecture, going beyond high-level considerations done in existing literature that prevent its effective realisation. The resulting architecture, workflows, and interfaces are validated and assessed through practical implementations in experimental infrastructures with network service definitions close to real vertical use cases (e.g., automotive and eHealth), which help bridging the gap between network providers and vertical industries stakeholders.
mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks

Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field.To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of wireless mobile networks.
Highly Sensitive Flexible Capacitive Pressure Sensor Based on Bionic Hybrid Microstructures

Flexible capacitance pressure sensors have shown excellent application potential in biomedical equipment, human-computer interaction, and robotics. Herein, we propose a flexible capacitive pressure sensor based on bionic hybrid microstructures (BHM) which consists of micro-cones by facile replication of the surface of Calathea zebrina leaf and mini-domes by vacuum adsorption from through-hole arrays, respectively. The sensor presents a high pressure sensitivity of 3.17 kPa-1, and wide detection range. These promising advantages of the pressure sensor enable continuous pulse signal monitoring and heart condition analyzing of human body. This work highlights the potential of the pressure sensor in a wide range of applications, including wearable devices, robots, and human-machine interaction.
Detecting and locating configuration errors in IP VPNs with Graph Neural Networks

Configuration verification of networks, particularly virtual private networks, is a complex task that is required before each update on a production environment, so that network providers can ensure network availability for their customers. In this paper, we propose a Graph Neural Network (GNN) based approach to detect and locate configuration errors in IP Virtual Private Networks (VPNs). Two GNN models are proposed, one that targets routing configuration errors between customer and provider edge routers, the other targets VPN routing errors between the different provider edge routers. The objective is to provide a tool that simplifies the process of verifying end-to-end VPN configurations. We trained both models with balanced datasets containing labeled configurations generated from examples based on IMS Networks’ deployed VPNs. Results show a high level of accuracy for different sizes of VPNs (3 to 40 sites) and for two types of architectures (full-mesh and hub-and-spoke).
Water Quality Prediction for Smart Aquaculture Using Hybrid Deep Learning Models

Water quality prediction (WQP) plays an essential role in water quality management for aquaculture to make aquaculture production profitable and sustainable. In this work, we propose hybrid deep learning (DL) models, convolutional neural network (CNN) with the long short-term memory (LSTM) and gated recurrent unit (GRU) for aquaculture WQP. CNN can effectively fetch the aquaculture water quality characteristics, whereas GRU and LSTM can learn long-term dependencies in the time series data. We conduct experiments using the two different water quality datasets and present an extensive study on the impact of hyperparameters on the performance of the proposed hybrid DL models. Furthermore, the performance of hybrid CNN-LSTM and CNN-GRU models are compared with different baseline LSTM, GRU and CNN DL models and also with attention-based LSTM and attention-based GRU DL models. The results show that the hybrid CNN-LSTM outperformed all other models in terms of prediction accuracy and computation time.
Algorithms for Processing the Information Signal of the Shock Sensor Car Security System

Modern car security systems are complex, multiprocessor systems in which the main module interacts with peripheral devices via digital buses. One such device is a microprocessor-based shock sensor. The sensing element of the sensor is usually a MEMS accelerometer. To distinguish between weak and strong impacts on the car body, the sensors have two zones (two levels) of operation. Publications on shock sensors are mainly advertising in nature. This is due to production secrets and unwillingness to disclose signal processing algorithms. The paper presents experimental data on accelerations upon impact on a car body. An information signal processing algorithm is proposed that excludes false alarms and allows the implementation of a digital sensor with 16 adjustable sensitivity gradations and adaptation to periodic disturbing influences.
Slice admission control using overbooking for enhancing provider revenue in 5G Networks

Network Slicing (NS) provides a new business model to the Infrastructure Providers (InP), where multiple tenants request the InP to provide services to its customers. InP decides whether to accept the request or not to maximize the overall profit due to resource limitation. It is generally seen that tenants over-estimate their slices’ maximum requirements. In particular, for elastic slices that have flexible resource requirements, we use the concept of overbooking. Here, we accept slices above actual resource availability. We present a Slice Admission architecture where, a Slice Forecasting Agent (SFA) predicts the future resource (CPU, Memory, Bandwidth) usage of currently active elastic slices for next time window. This predicted information is used by an opportunistic overbooking heuristic, where the system allocates the required resources to each slice. After this, we address the admission control problem using a reinforcement learning (RL) approach that decides to accept/reject an incoming slice request. The performance of our proposed work is compared against three other heuristics (Basic, Prediction, Prediction-RL) that do not use overbooking. Data traces from the Materna data center network were used for prediction. The results show that the proposed work significantly outperforms the other mechanisms in terms of revenue gain and resource utilization.
Combining Methods of Mathematical Optimization and Artificial Intelligence for Autonomous UAV Mission Planning and Execution

In this thesis, different solutions for autonomous mission planning and execution aimed at Unmanned Aerial Vehicles (UAV) is investigated using methods of Artificial Intelligence (AI) and Mathematical Optimization. Different configurations of these solutions is then implemented as an Autonomous Mission Planning and Execution (AMPE) system in order to test the online performance of the complete system, and the different individual models that composes the system. Inspired by [26] the system is divided into modules handling the plan in different levels of refinement in a hierarchical manner. Where applicable, modules will be tested and compared when implemented by AI and Mathematical Optimization and the advantages and disadvantages will be discussed. The system should be able to plan with a certain amount of resources in mind, continuously update the plan using state feedback, and perform the plan by using the plans current actuator inputs. In order to keep clear goals for the the AMPE system, it is designed with a specific case in mind. Both the implemented capabilities and the discussion of results will focus on off-shore data gathering from sensors where the distances causes the required bandwidth impossible to achieve for direct communication. Several UAVs will cooperate in order to collect the data by creating a delay-tolerant communication network, and will seek to do so in some optimal manner. The different levels of the planning problem will be solved using Nonlinear Model Predictive Control (NMPC), Mixed Integer Linear Programming (MILP) and domain models for EUROPA. The tools ACADO, AMPL, EUROPA and T-REX will be used to implement the AMPE system with parallel, synchronous deliberation of the planning levels. Simulations will be performed which shows that using the MILP optimization with data flow constraints are note applicable for a real world system as it uses hours to solve a plan. However, by finding plans in a static mission, the EUROPA solvers will show that it is much more efficient and ways of combining EUROPA and MILP in the future will be discussed. Simulations is done to show re-planning capabilities when the mission is affected by poor flight dynamics, dynamic resource consumption and dynamically positioned nodes. In the end we will see how this system can be quite easily expanded so that the case of this thesis is only a specialization of a much broader domain of problems solvable by a more general system. A system where a mission problem would be defined as a top-level nddl domain model, and then solved automatically throughout the AMPE pipeline. The thesis will conclude with how a system based on purely Mathematical Optimization is ill-fitted for real-time demands, and how AI and Mathematical Optimization can be combined for autonomous mission planning and execution.
An Automated SMT-based Security Framework for Supporting Migrations in Cloud Composite Services

The growing maturity of orchestration languages is contributing to the elaboration of cloud composite services, whose resources may be deployed over different distributed infrastructures. These composite services are subject to changes over time, that are typically required to support cloud properties, such as scalability and rapid elasticity. In particular, the migration of their elementary resources may be triggered by performance constraints. However, changes induced by this migration may introduce vulnerabilities that may compromise the resources, or even the whole cloud service. In that context, we propose an automated SMT1-based security framework for supporting the migration of resources in cloud composite services, and preventing the occurrence of new configuration vulnerabilities. We formalize the underlying security automation based on SMT solving, in order to assess the migrated resources and select adequate counter-measures, considering both endogenous and exogenous security mechanisms. We then evaluate its benefits and limits through large series of experiments based on a proof-of-concept prototype implemented over the CVC4 commonly-used open-source solver. These experiments show a minimal overhead with regular operating systems deployed in cloud environments.
Gas Dispersion Simulator with Strong Fluctuations for Developing Gas Source Localization Systems

Here we report a gas dispersion simulator that can be used to develop and evaluate robotic systems for gas leak detection and gas source localization. The key contribution of this paper is a wind field generator capable of recreating highly fluctuating wind fields that we often encounter in open outdoor environments. Gas-source localization simulations can be done using artificially generated wind data that satisfy the statistical properties of natural wind. Results of the simulations are compared to simulation results obtained using wind data recorded in a real environment to show the effectiveness of the proposed wind generator.
Deep Learning-Assisted Traffic Prediction in Hybrid SDN/OSPF Backbone Networks

Deploying a real-world software defined network (SDN) requires instantaneous link traffic information. This has necessitated for the need of accurate real-time data analytics and traffic matrix (TM) prediction methods. So far, several frameworks have been developed to enable analysis and generation of valuable information from huge volumes of partial and noisy data. But, owing to the linear nature of network design, generally typified by manual control plane forwarding design, current frameworks are incapable of performing accurate traffic prediction over datasets in modern non-recurrent large-sized networks. To address this issue, deep learning (DL) methods have recently been proposed as a possible solution. But, deciding the most appropriate DL models to be employed for accurate TM prediction is still a challenge. This paper proposes an improved DL framework that utilizes different dimensionality feature reduction techniques to perform short-term TM prediction in SDN networks. The two dimensionality reduction techniques required to perform feature reduction for the DL model are correlation component analysis (CCA) and principal component analysis (PCA). Investigational results show that the proposed method can achieve more accurate forecast of link traffic in comparison to the traditional baseline machine learning frameworks.
Pikachu: Temporal Walk Based Dynamic Graph Embedding for Network Anomaly Detection

Enterprise networks evolve constantly over time. In addition to the network topology, the order of information flow is crucial to detect cyber-threats in a constantly evolving network. Majority of the existing technique uses static snapshot to learn from dynamic network. However, using static snapshots is not sufficient as it largely ignores highly granular temporal information and leads to information loss due to approximation of aggregation granularity. In this work, we propose PIKACHU, a sophisticated, unsupervised, temporal walk-based dynamic network embedding technique that can capture both network topology as well as highly granular temporal information. PIKACHU learns the appropriate and meaningful representation by preserving the temporal order of nodes. This is important information to detect Advanced Persistent Threat (APT) as temporal order helps to understand the lateral movement of the attacker. Experiments on two open-source datasets: LANL and OpTC datasets demonstrated the effectiveness in detecting network anomalies. PIKACHU achieves True Positive Rate (TPR) of 95.1% in LANL and 98.7% on OpTC dataset. Furthermore, in the LANL dataset, it achieves a 4.65% reduction in False Positive Rate (FPR) despite similar area under ROC curve (AUC). In the OpTC dataset 16% improvement in AUC was obtained in comparison to the other state-of-the-art approaches.
On Minimizing TCP Retransmission Delay in Softwarized Networks

Today’s Internet mainly relies on TCP protocol to ensure reliable communications between two endpoints. Unfortunately, this widely-used protocol may incur significant delay when transmitting lost packets. Indeed, with TCP, the source of the data detects lost packets using a timeout or duplicated acknowledgements before retransmitting them. As a result, the delay needed for a packet to reach the destination may become significant. This delay is estimated to be at least three times the end-to-end delay when the packet is lost once and could be even worse when the same packet is lost several times. As a matter of fact, this high delay cannot be tolerated by critical applications.To address this problem, in this paper, we focus on minimizing TCP retransmission delays and we introduce a novel network function called Transport Assistant that could be deployed within the network in order to cache, detect and retransmit lost packets. Thanks to this function, there is no need to wait for the source to detect and retransmit lost packets as the TA ensures packet retransmission from the network itself and thereby minimize retransmission delays. Through extensive experiments, we show that the TA allows to outperform the standard TCP by minimizing the average packet transmission time, the flow completion time, the packet loss and the number of retransmitted packets from the source.
Development of an Algorithm for the Program to Recognize Defects on the Surface of Hot-Rolled Metal

The problem of recognizing defects on the surface of hot-rolled metal is quite old, but technologies have only recently reached a sufficient level for the automation of this process [1]–[3]. One of the most suitable methods is the application of convolutional neural networks [4]–[7]. As the qualitative dataset for network training we selected the NEU (Northeastern University) surface defect database, which is a dataset of the most identified cases of hot rolled defects. Also, these data were supplemented with images of a clean hot-rolled surface. An algorithm for recognizing defects was developed. Camera parameters for machine vision were calculated.
Comparative Causal Analysis of Network Log Data in Two Large ISPs

Towards a collaborative analysis of log data obtained from multiple networks, we first need to clarify what kind of information is available as transferable knowledge between different networks. However, we cannot directly compare net-work log data from different sources because the data largely depends on the network architecture and equipment. In this paper, we focus on relational information among network log events that follow standardized network protocols regardless of network environment. We propose a comparative analysis approach relying on causality between log time-series. In this approach, we classify log messages into anonymized log time-series with log templates, reduce the number of log time-series to decrease processing time, and apply causal discovery with the PC algorithm. To decrease the processing time of causal analysis, we propose a new preprocessing method that reduces the number of log time-series without any domain knowledge (i.e., available in any ISPs). We compare log data obtained from two nation-wide ISPs to demonstrate the effectiveness of the causal approach in comparative analysis.
SmartNIC-based Load Management and Network Health Monitoring for Time Sensitive Applications

Time sensitive network applications, for example in Intra-Vehicular Networks, aim to give predictable end-to-end latency guarantees. As a consequence, processing resources of involved host systems remain partially unused, because they are reserved for rare worst cases. This circumstance provides the opportunity to reduce dimensioning overheads by managing the load on the nodes flexibly within the network. In our proposed approach, a SmartNIC involving an FPGA-based load balancer achieves dynamic routing of flows whilst preserving end-to-end latency guarantees. A flow-oriented online network measurement component continuously supervises network traffic with regards to compliance to flow specifications and constraints such as bounded one-way delay, absence of packet loss, and jitter. We use the supervisor to enhance forwarding decisions on the data plane. Initial evaluation yields a saving potential of around 30 %. We showcase quick dynamic reconfiguration of the FPGA when triggered by real-time measurement of the one-way delay using realistic automotive network traffic.
Ambient Assistive Living (AAL) Technology for Dementia and Aging in Place: An inclusive approach to knowledge acquisition for the design community

The growing concern for safety, care and wellbeing of older people ad increase in age related health issues such as dementia, has generated a great interest in AAL technologies as a means to support the elderly and caregiver needs in daily living tasks, extend aging at home, and maintaining social inclusion for as long as possible. As technology becomes more ubiquitous in home environments, new design challenges will arise for the design/build community. Literature search revealed that there is now North American Guideline for AAL technology available. This study required an integrated design process utilizing in-person and online surveys which included interdisciplinary contributions from healthcare experts. building automation experts. interior designers and architects in the development of an educational structure fro a proposed North American AAL Technology Guideline. Research indicated that the educational structure would need to consider the developmental continuum in learning for professionals as they move from novice to expert in practice. Keywords: aging, ambient assisted living (AAL) technology, dementia, home automation, innovation models, Maslow's Hierarchy of Needs, professional developmental continuum
Ammoniumsupply rate in£uences archaeal and bacterial ammonia oxidizers inawetland soil vertical pro¢le

Ammoniumsupply rate in£uences archaeal and bacterial ammonia oxidizers inawetland soil vertical pro¢le Špela Höfferle, Graeme W. Nicol, Levin Pal, Janez Hacin, James I. Prosser & Ines Mandić-Mulec Department of Food Science and Technology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia; and Institute of Biological and Environmental Sciences, University of Aberdeen, Scotland, UK