301,244 research outputs found
Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems
No full textSupported by some of the major revolutionary technologies, such as Internet of Vehicles (IoVs), Edge Computing, and Machine Learning (ML), the traditional Vehicular Networks (VNs) are changing drastically and converging rapidly into one of the most complex, highly intelligent, and advanced networking systems, mostly known as Intelligent Transportation System (ITS). Recently, distributed ML techniques, such as Federated Learning (FL) have gained huge popularity mainly for their advantages in terms of intelligence sharing and privacy concerns. VNs are a natural contender for exploiting FL for solving challenging problems; however, their limited resources, dynamic nature, high speed, and reduced latency requirements often become the bottleneck. V2X communication technologies allow vehicular terminals (VTs) to share their valuable local environment parameters and become aware of their surroundings. Such information can be utilized to build a more sustainable and affordable FL platform for serving VTs. Gaining from recently introduced 3D architectures, integrating terrestrial and aerial edge computing layers, we present here a distributed FL platform able to distribute the FL process on a 3D fashion while reducing the overall communication cost for providing vehicular services. The framework is defined as a constrained optimization problem for reducing the overall FL process cost through a proper network selection between various nodes. We have modeled the FL network selection problem as a sequential decision-making process through a Markov Decision Process (MDP) with time-dependent state transition probabilities. A computation-efficient value iteration algorithm is adapted for solving the MDP. Comparison with various benchmark methods shows the overall improvement in terms of latency, energy, and FL performance
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Collaborative Reinforcement Learning for Multi-Service Internet of Vehicles
No full textInternet of Vehicles (IoV) is a recently introduced paradigm aiming at extending the Internet of Things (IoT) toward the vehicular scenario in order to cope with its specific requirements. Nowadays, there are several types of vehicles, with different characteristics, requested services, and delivered data types. In order to efficiently manage such heterogeneity, Edge Computing facilities are often deployed in the urban environment, usually co-located with the Roadside Units (RSUs), for creating what is referenced as Vehicular Edge Computing (VEC). In this paper, we consider a joint network selection and computation offloading optimization problem in multi-service VEC environments, aiming at minimizing the overall latency and the consumed energy in an IoV scenario. Two novel collaborative Q-learning based approaches are proposed, where Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication paradigms are exploited, respectively. In the first approach, we define a collaborative Q-learning method in which, through V2I communications, several vehicles participate in the training process of a centralized Q-agent. In the second approach, by exploiting the V2V communications, each vehicle is made aware of the surrounding environment and the potential offloading neighbors, leading to better decisions in terms of network selection and offloading. In addition to the tabular method, an advanced deep learning-based approach is also used for the action value estimation, allowing to handle more complex vehicular scenarios. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy with respect to some benchmark solutions
Some Europeans are more equal than others
No full textThe position of Roma migrants in the EU presents an anomaly which challenges the foundations of European Union law. As Union citizens, European migrants are entitled to freedom of movement and residence in Member States. Yet the rights intended to secure this position have been routinely and selectively denied to Roma migrants, leading to forced evictions and collective expulsions without regard t o European Law. As has been evidenced in the UK, Roma arrivals are viewed with particularly acute suspicion; a response which reflects their double stigmatization as both immigrant and Roma. At the same time, Roma migration from new Member States has expo sed a contradiction inherent in the citizenship project which strikes at the heart of the Union ’s human rights credentials. The degree of exclusion and inequality faced by Europe’s largest minority in all Member States is the most pressing internal human rights issue facing the EU. Yet the European institutions continue to lack a coherent response and defined strategy. The current European framework demanding National Action Plans is commendable in that it prevents individual states from abdicating responsibility for the situation of their Roma citizens. Nevertheless, the absence of clear targets, Roma engagement and European leadership, suggest that this strategy is doomed to failure offering little more than a distraction. In a Union predicated on, inter alia, the rule of law, respect for human rights and the protection of minorities, this detached position undermines the legitimacy of the entire citizenship project
A network operator-biased approach for multi-service network function placement in a 5G network slicing architecture
No full textThe 5G communication standard is characterized by an increased softwarization, allowing a higher flexibility able to cope with different requirements and services. In particular, Network Function Virtualization (NFV) is a recently introduced technology that enables a software implementation of different network functions exploiting virtualization techniques, hence, enabling their flexible
deployment upon system requirements. Boosted by NFV, the concept of network slicing is gaining great attention in 5G networks. The idea is that physical communication and computing resources are sliced in multiple end-to-end logical networks, each one tailored to best support a specific service. The advantages of NFV, in the network slicing context, are even more evident in distributed computing environments, such as the edge-to-cloud continuum, recently introduced for enabling a flexible deployment of multiple functions. In particular, thanks to the introduction of cloud-native technologies, based on the usage of containerization and microservice technologies, the virtual network functions (VNFs) deployment and their orchestration is an easy operation, allowing the on-the-fly network configuration. Gaining from the NFV, Network Slicing and Edge-to-Cloud continuum paradigms, we propose a new network function allocation problem for multi-service 5G networks, able to deploy network functions on a distributed
computing environment depending on the service requests. The proposed approach jointly considers Radio Access Network (RAN)
and Core Network (CN) functions and, dierently from other approaches, introduces an option able to bias the function placement
depending on the service requirements, allowing a fast-and-easy operator-side deployment of the network functions. We propose to
solve the problem through a Genetic Algorithm able to approach the optimal solution but with reduced complexity and execution
time. The performance is compared with two other heuristic algorithms and with an exhaustive search algorithm, introduced as
benchmarks, showing the benefits of the selected solution in terms of performance, flexibility and complexity
The Benefits of Being Economics Professor A (and not Z)
No full textAlphabetic name ordering on multi-authored academic papers, which is the convention in the economics discipline and various other disciplines, is to the advantage of people whose last name initials are placed early in the alphabet. As it turns out, Professor A, who has been a first author more often than Professor Z, will have published more articles and experienced afaster growth rate over the course of her career as a result of reputation and visibility. Moreover, authors know that name ordering matters and indeed take ordering seriously: Several characteristics of an author group composition determine the decision to deviate from the default alphabetic name order to a significant extent.performance measurement, incentives, economists, name ordering
Analysis and Performance Evaluation of Transfer Learning Algorithms for 6G Wireless Networks
No full textThe development of the 5G network and the transition to 6G has given rise to multiple challenges for ensuring high-quality and reliable network services. One of these main challenges is the emergent intelligent defined networks (IDN), designed to provide highly efficient connectivity, by merging artificial intelligence (AI) and networking concepts, to ensure distributed intelligence over the entire network. To this end, it will be necessary to develop and implement proper machine learning (ML) algorithms that take into account this new distributed nature of the network to represent increasingly dynamic, adaptable, scalable, and efficient systems. To be able to cope with more stringent service requirements, it is necessary to renew the ML approaches to make them more efficient and faster. Distributed learning (DL) approaches are shown to be effective in enabling the possibility of deploying intelligent nodes in a distributed network. Among several DL approaches, transfer learning (TL) is a valid technique to achieve the new objectives required by emerging networks. Through TL, it is possible to reuse ML models to solve new problems without having to recreate a learning model from scratch. TL, combined with distributed network scenarios, turns out to be one of the key technologies for the advent of this new era of distributed intelligence. The goal of this paper is to analyze TL performance in different networking scenarios through a proper MATLAB implementation
Opportunity cost analysis of android smartphones' permissions
No full textThis thesis provides the opportunity cost for reading androids permission model. We investigate the opportunity cost for users and an example nation (United States), if people would actually read these permission screens during installation time.While the Federal Trade Commission and reserachers try to protect users’ privacy and to improve their comfort level with mobile applications, users still remain unaware of these changes. Users are given a choice to overview the permissions an app would use and have to make an on the spot decision to accept these and move forward with the installation. In this research we project the time required by an average user if they were to read the permissions and compute the monetary value of that time in different situations. An average user may spend half an hour in overviewing permission screens bearing maximum opportunity cost of 3 based on whether it was read at work or leisure. Other than this, if the users decide to read the details of these permissions as well, they will spend more time and hence bear more cost. Reading permissions with details would require users to spend two and half hours annually with a maximum cost of 13. An entire nation (United States) would have to invest a minimum of 6 billion, in reading permissions.M.S.Includes bibliographical referencesby Swapnil Sarod
High-throughput FPGA QC-LDPC decoder architecture for 5G wireless
No full textWireless data traffic is expected to increase by a 1000 fold by the year 2020 with more than 50 billion devices connected to these wireless networks with peak data rates upto 10 Gb/s. The next generation of wireless cellular technology (being collectively termed as 5G) is slated to operate in the mm-wave (30-300GHz) spectrum which comes with challenges such as, reliance on line of sight (LOS) communication, short range of communication, increased shadowing and, rapid fading in time. This will necessitate additional signal processing techniques such as large antenna arrays and beamsteering which will further reduce the processing budget available to the channel coding system. In an effort ort to design and develop a channel coding solution suitable to such systems, in this thesis we propose strategies to achieve a high-throughput FPGA-based decoder architecture for a QC-LDPC code based on circulant-1 identity matrix construction. We present a novel representation of the parity-check matrix (PCM) providing a multifold throughput gain. Splitting of the node processing algorithm enables us to achieve pipelining of blocks and hence layers. By partitioning the PCM into not only layers but superlayers, we derive an upper bound on the pipelining depth with respect to the size of the superlayer for the compact representation. To validate the architecture, a decoder for the IEEE 802.11n (2012) QC-LDPC is implemented on the Xilinx Kintex-7 FPGA with the help of the FPGA IP compiler available in the NI LabVIEW Communication System Design Suite (CSDS). It off ers an automated and systematic compilation flow. An optimized hardware implementation from the decoder algorithm was generated in approximately 3 minutes, achieving an overall throughput of 608Mb/s (at 260MHz). With little or no modi fications, the proposed decoder architecture caters to a wide range of circulant-1 identity matrix construction based QC-LDPC codes widely accepted in several communication and data storage standards.M.S.Includes bibliographical referencesby Swapnil Mhask
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