Network Function Virtualization (NFV) provides great flexibility in solving the problem of deploying service function chains (SFCs). The existing serial SFCs organize virtual network functions (VNFs) in a predefined order and execute the VNFs one by one, which can result in high SFC delay as the lengths of the SFCs increases. In order to address this problem, in this work, the SFC parallelization is

The Internet of Things (IoT), comprising a plethora of heterogeneous devices, is an enabling technology that can improve the quality of our daily lives, for instance by measuring parameters from the environment (e.g., humidity, temperature, weather, energy consumption, traffic, and others) or our bodies (e.g., health data). However, as with any technology, IoT has introduced a number of security and

Thus far, Multiview High-Efficiency Video Coding (MV-HEVC) can only use a central processing unit (CPU) to perform decompression on a personal computer (PC) or workstation. Because MV-HEVC is much more complex than High-Efficiency Video Coding (HEVC), decompressors need higher parallelism to decompress in real time. Therefore, this study presents a parallel method based on MV-HEVC. Inter-view complete

Abstract Frequent itemset mining is considered a popular tool to discover knowledge from transactional datasets. It also serves as the basis for association rule mining. Several algorithms have been proposed to find frequent patterns in which the apriori algorithm is considered as the earliest proposed. Apriori has two significant bottlenecks associated with it: first, repeated scanning of input dataset

The role mining process, i.e. computing minimal set of roles from a user-permission assignment relation, is rather incremental task. In a usual situation the set of roles is already established, and the existing permissions are evolved according to current needs. Computation of roles from scratch after each small change in the data is inefficient. Surprisingly, no incremental algorithm for role mining

This paper presents a 3D model aiming at investigating the out-of-plane collapse behavior of multi-leaf non-periodic masonry walls. These structural elements are widely employed in several constructions that are part of the European architectural heritage, but have been seldom addressed in the available literature due to their natural complexity and uniqueness. Specifically, the model here proposed

Cognitive radio is emerging as a technology to alleviate spectrum shortage and to improve spectrum utilization. In database-driven cognitive radio networks (CRNs), secondary users (SUs) must provide their locations to the database to enable channel allocation, which results in severe privacy violation. As SUs are usually energy-constrained or fast-moving, how to provide efficient channel allocation

As a new video coding technology, distributed compressive video sensing (DCVS) uses compressed sensing (CS) independent encoding and joint decoding. Since DCVS breaks through the constraint of traditional video coding, it is suitable for resource-constrained wireless multimedia sensor networks (WMSNs). However, two major issues related to DCVS in WMSNs need to be solved urgently: one is to balance

Over the last decade, IoT platforms have been developed into a global giant that grabs every aspect of our daily lives by advancing human life with its unaccountable smart services. Because of easy accessibility and fast-growing demand for smart devices and network, IoT is now facing more security challenges than ever before. There are existing security measures that can be applied to protect IoT.

During the past decades, due to the advances of the cloud computing technology with tens of thousands of processing machines, the intra data-center network traffic has increased tremendously. In data-center networks, the typical communication patterns of cloud computing technologies such as map-reduce require short latency of the request and response messages to achieve high service quality. Thus,

Radio frequency identification (RFID) technology, which obtained significant development in the past several decades, is widely applied in numerous industrial applications. One of the typical applications is to detect the missing tags, which can perform real-time detection of missing objects while each object is attached with a low-cost RFID tag in a large-scale warehouse. However, there actually may

Finding the best way to place the components of an application on a set of heterogeneous servers is a challenging task, especially if some components are associated with security requirements. To address security requirements, several security controls may be available, some of them software-based (e.g., encryption), others hardware-based (e.g., trusted execution environments). Security controls may

Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very costly in terms of query response time, resource consumption, and money in cloud deployments, especially when base data are stored across geographically distributed data

Credit scoring in online Peer-to-Peer (P2P) lending faces a huge challenge, which is the credit scoring models discard rejected applicants. This selective discarding leads to bias in the parameters of the models and ultimately affects the performance of credit evaluation. One approach for handling this problem is to adopt reject inference, which is a technique that infer the status of rejected samples

Docker container virtualization technology is being widely adopted in cloud computing environments because of its lightweight and efficiency. However, it requires adequate control and management via an orchestrator. As a result, cloud providers are adopting the open-access Kubernetes platform as the standard orchestrator of containerized applications. To ensure applications’ availability in Kubernetes

Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties, due to the use of robust formulations that are amenable to distributed design and computational methods. In this work, we propose a distributed stochastic model predictive control (DSMPC) scheme for dynamically coupled linear discrete-time systems

Floating offshore wind turbines allow wind energy to be harvested in deep waters. However, additional dynamics and structural loads may result when the floating platform is being excited by wind and waves. In this work, the conventional wind turbine controller is complemented with a novel linear feedforward controller based on wave measurements. The objective of the feedforward controller is to attenuate

Traditionally, inertia in power systems has been determined by considering all the rotating masses directly connected to the grid. During the last decade, the integration of renewable energy sources, mainly photovoltaic installations and wind power plants, has led to a significant dynamic characteristic change in power systems. This change is mainly due to the fact that most renewables have power electronics

Optical nonlinear Fourier transform-based communication systems require an accurate estimation of a signal's nonlinear spectrum, computed usually by piecewise approximation methods on the signal samples. We propose an algorithm, named successive eigenvalue removal, to improve the spectrum estimation of a multi-soliton pulse. It exploits a property of the Darboux transform that allows removing eigenvalues

We present a Model Predictive Control (MPC) strategy for unknown input-affine nonlinear dynamical systems. A non-parametric method is used to estimate the nonlinear dynamics from observed data. The estimated nonlinear dynamics are then linearized over time varying regions of the state space to construct an Affine Time Varying (ATV) model. Error bounds arising from the estimation and linearization procedure

Submodular maximization problems are a relevant model set for many real-world applications. Since these problems are generally NP-Hard, many methods have been developed to approximate the optimal solution in polynomial time. One such approach uses an agent-based greedy algorithm, where the goal is for each agent to choose an action from its action set such that the union of all actions chosen is as

This paper proposes a computational method to efficiently and quickly estimate stability regions of droop control slopes for modular multilevel converter (MMC)-based multiterminal dc (MTDC) systems. The proposed method is based on a general small-signal model consisting of a dc grid with arbitrary topology and MMCs with dq controllers. The general small-signal model developed by a systematic way can

This paper concentrates on the study of the decentralized fuzzy control method for a class of fractional-order interconnected systems with unknown control directions. To overcome the difficulties caused by the multiple unknown control directions in fractional-order systems, a novel fractional-order Nussbaum function technique is proposed. This technique is much more general than those of existing works

The integration of cyber technologies (computing and communication) with the physical world gives rise to complex systems referred to as Cyber Physical Systems (CPS), for example, manufacturing, transportation, smart grid, and water treatment. Many of those systems are part of the critical infrastructure and need to perform safely, reliably, and securely in real-time. CPS security is challenging as

Increased uptake of variable renewable generation and further electrification of energy demand necessitate efficient coordination of flexible demand resources to make most efficient use of power system assets. Flexible electrical loads are typically small, numerous, heterogeneous and owned by self-interested agents. Considering the multi-temporal nature of flexibility and the uncertainty involved,

In this paper we propose and compare methods for combining system identification (SYSID) and reinforcement learning (RL) in the context of data-driven model predictive control (MPC). Assuming a known model structure of the controlled system, and considering a parametric MPC, the proposed approach simultaneously: a) Learns the parameters of the MPC using RL in order to optimize performance, and b) fits

Inspired by the success of imitation and inverse reinforcement learning in replicating expert behavior through optimal control, we propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs). We consider the setting of a known nonlinear control affine dynamical system and assume that we have access to safe trajectories generated by an expert - a practical

This study addresses a new question regarding the security of the Kirchhoff-Law-Johnson-Noise (KLJN) scheme compromised by DC sources at Alice and Bob: What is the impact of these parasitic sources on active attacks, such as the man-in-the-middle (MITM) attack, or the current injection attack? The surprising answer is that the parasitic DC sources actually increase the security of the system because

Purpose: In this study, the recently emerged advances in Fuzzy Cognitive Maps (FCM) are investigated and employed, for achieving the automatic and non-invasive diagnosis of Coronary Artery Disease (CAD). Methods: A Computer-Aided Diagnostic model for the acceptable and non-invasive prediction of CAD using the State Space Advanced FCM (AFCM) approach is proposed. Also, a rule-based mechanism is incorporated

This paper addresses a phenomenon caused by resetting only one of the two states of a so-called second order "Constant in gain Lead in phase" (CgLp) element. CgLp is a recently introduced reset-based nonlinear element, bound to circumvent the well-known linear control limitation -- the waterbed effect. The ideal behaviour of such a filter in the frequency domain is unity gain while providing a phase

As an important technology in artificial intelligence Granular Computing (GrC) has emerged as a new multi-disciplinary paradigm and received much attention in recent years. Information granules forming an abstract and efficient characterization of large volumes of numeric data have been considered as the fundamental constructs of GrC. By generating prototypes and partition matrix, fuzzy clustering

Robotic algorithms typically depend on various parameters, the choice of which significantly affects the robot's performance. While an initial guess for the parameters may be obtained from dynamic models of the robot, parameters are usually tuned manually on the real system to achieve the best performance. Optimization algorithms, such as Bayesian optimization, have been used to automate this process

Long prediction horizons in Model Predictive Control (MPC) often prove to be efficient, however, this comes with increased computational cost. Recently, a Robust Model Predictive Control (RMPC) method has been proposed which exploits models of different granularity. The prediction over the control horizon is split into short-term predictions with a detailed model using MPC and long-term predictions

We consider a game-theoretic model where individuals compete over a shared failure-prone system or resource. We investigate the effectiveness of a taxation mechanism in controlling the utilization of the resource at the Nash equilibrium when the decision-makers have behavioral risk preferences, captured by prospect theory. We first observe that heterogeneous prospect-theoretic risk preferences can

Random attacks that jointly minimize the amount of information acquired by the operator about the state of the grid and the probability of attack detection are presented. The attacks minimize the information acquired by the operator by minimizing the mutual information between the observations and the state variables describing the grid. Simultaneously, the attacker aims to minimize the probability

This paper introduces a rescue robot named Sigma 3 which is developed for potential applications such as helping hands for humans where a human can not reach to have an assessment of the hazardous environment. Also, these kinds of robot can be controlled remotely with an adequate control system. The proposed methodology forces on two issues : 1. Novel mechanism design for measuring rotation, joints

Matrix Factorization (MF) based on Stochastic Gradient Descent (SGD) is a powerful machine learning technique to derive hidden features of objects from observations. In this article, we design a highly parallel architecture based on Field-Programmable Gate Array (FPGA) to accelerate the training process of the SGD-based MF algorithm. We identify the challenges for the acceleration and propose novel

One of the major advantages of cloud spot instances in cloud computing is to allow stakeholders to economically deploy their applications at much lower costs than that of other types of cloud instances. In exchange, spot instances are often exposed to revocations (i.e., terminations) by cloud providers. With spot instances becoming pervasive, terminations have become a part of the normal behavior of

This article presents AutoFFT, a template-based code generation framework that can automatically generate high-performance FFT kernels for all natural-number radices. AutoFFT is based on the Cooley-Tukey FFT algorithm, which exploits the symmetric and periodic properties of the DFT matrix, as the outer parallelization framework. Because butterflies are the core operations of the Cooley-Tukey algorithm

Terrorists use violence in pursuit of political goals. While terror often has severe consequences for victims, it remains an open question how terror attacks affect the general population. We study the behavioral response of citizens of cities affected by $7$ different terror attacks. We compare real-time mobile communication patterns in the first $24$ hours following a terror attack to the corresponding

Scholarly resources, just like any other resources on the web, are subject to reference rot as they frequently disappear or significantly change over time. Digital Object Identifiers (DOIs) are commonplace to persistently identify scholarly resources and have become the de facto standard for citing them. We investigate the notion of persistence of DOIs by analyzing their resolution on the web. We derive

With a view on graph clustering, we present a definition of vertex-to-vertex distance which is based on shared connectivity. We argue that vertices sharing more connections are closer to each other than vertices sharing fewer connections. Our thesis is centered on the widely accepted notion that strong clusters are formed by high levels of induced subgraph density, where subgraphs represent clusters

We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the

The directed network of telephone subscribers is considered in the article. It can be described as a dynamic network with vertices that correspond to the subscribers of the telephone network and emerging directional edges that correspond to the connections between the respective subscribers. The position of the edge and its direction is determined by the incoming and outgoing calls from the corresponding

A COVID-19 pandemic has already proven itself to be a global challenge. It proves how vulnerable humanity can be. It has also mobilized researchers from different sciences and different countries in the search for a way to fight this potentially fatal disease. In line with this, our study analyses the abstracts of papers related to COVID-19 and coronavirus-related-research using association rule text

Multilayer and multiplex networks are becoming common network data sets in recent times. We consider the problem of identifying the common community structure for a special type of multilayer networks called multi-relational networks. We consider extensions of the spectral clustering methods for multi-relational networks and give theoretical guarantees that the spectral clustering methods recover community

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative

Stance detection entails ascertaining the position of a user towards a target, such as an entity, topic, or claim. Recent work that employs unsupervised classification has shown that performing stance detection on vocal Twitter users, who have many tweets on a target, can yield very high accuracy (+98%). However, such methods perform poorly or fail completely for less vocal users, who may have authored

Using a random 10% sample of tweets authored from 2019-09-01 through 2020-03-25, we analyze the dynamic behavior of words (1-grams) used on Twitter to describe the ongoing COVID-19 pandemic. Across 24 languages, we find two distinct dynamic regimes: One characterizing the rise and subsequent collapse in collective attention to the initial Coronavirus outbreak in late January, and a second that represents

Finding graph indices which are unbiased to network size and density is of high importance both within a given field and across fields for enhancing comparability of modern network science studies. The degree variance is an important metric for characterising network degree heterogeneity. Here, we provide an analytically valid normalisation of degree variance to replace previous normalisations which

This paper discusses the problem of estimating treatment allocation rules under network interference. I propose a method with several attractive features for applications: (i) it does not rely on the correct specification of a particular structural model; (ii) it exploits heterogeneity in treatment effects for targeting individuals; (iii) it accommodates arbitrary constraints on the policy function

The COVID-19 pandemic has not only had severe political, economic, and societal effects, it has also affected media and communication systems in unprecedented ways. While traditional journalistic media has tried to adapt to the rapidly evolving situation, alternative news media on the Internet have given the events their own ideological spin. Such voices have been criticized for furthering societal

Motivated by the challenge related to the COVID-19 epidemic and the seek for optimal containment strategies, we present a robustness analysis into an inter-cities mobility complex network. We abstract municipal initiatives as nodes' failures and the federal actions as targeted attacks. The geo(graphs) approach is applied to visualize the geographical graph and produce maps of topological indexes, such

Cloud GPU servers have become the de facto way for deep learning practitioners to train complex models on large-scale datasets. However, it is challenging to determine the appropriate cluster configuration---e.g., server type and number---for different training workloads while balancing the trade-offs in training time, cost, and model accuracy. Adding to the complexity is the potential to reduce the

Function-as-a-Service (FaaS) is one form of the serverless cloud computing paradigm and is defined through FaaS platforms (e.g., AWS Lambda) executing event-triggered code snippets (i.e., functions). Many studies that empirically evaluate the performance of such FaaS platforms have started to appear but we are currently lacking a comprehensive understanding of the overall domain. In our work, we survey

With the recent trend of "network softwarisation", enabled by emerging technologies such as Software-Defined Networking (SDN) and Network Function Virtualisation (NFV), system administrators of data centres and enterprise networks have started replacing dedicated hardware-based middleboxes with virtualised network functions running on servers and end hosts. This radical change has facilitated the provisioning

The 3rd Generation Partnership Project (3GPP) has defined based on the Long Term Evolution (LTE) enhanced Multicast Broadcast Multimedia Service (eMBMS) a set of new features to support the distribution of Terrestrial Broadcast services in Release 14. On the other hand, a new 5th Generation (5G) system architecture and radio access technology, 5G New Radio (NR), are being standardised from Release

We perform experiments on the wireless communication between a drone flying at different heights and a commercial 5G base station. An Android-based tool deployed on a 5G test platform is used to record radio link parameters in the up- and downlink. In the downlink, measurements show a throughput of 600 Mbit/s on average with peaks above 700 Mbit/s. The uplink has a much lower throughput, comparable

Diagnosing problems in Internet-scale services remains particularly difficult and costly for both content providers and ISPs. Because the Internet is decentralized, the cause of such problems might lie anywhere between an end-user's device and the service datacenters. Further, the set of possible problems and causes is not known in advance, making it impossible in practice to train a classifier with

Operational cellular networks rely on closed and inflexible infrastructure tightly controlled by a handful of vendors. As a consequence, configuration requires vendor support and lengthy manual operations, which prevent Telco Operators (TOs) from unlocking the full network potential and from being able to perform fine grained performance optimization, especially on a per-user basis. To address this