State-based targeted vaccination

AbstractVaccination has become one of the most prominent measures for preventing the spread of infectious diseases in modern times. However, mass vaccination of the population may not always be possible due to high costs, severe side effects, or shortage. Therefore, identifying individuals with a high potential of spreading the disease and targeted vaccination of these individuals is of high importance. While various strategies for identifying such individuals have been proposed in the network epidemiology literature, the vast majority of them rely solely on the network topology. In contrast, in this paper, we propose a novel targeted vaccination strategy that considers both the static network topology and the dynamic states of the network nodes over time. This allows our strategy to find the individuals with the highest potential to spread the disease at any given point in time. Extensive evaluation that we conducted over various real-world network topologies, network sizes, vaccination budgets, and parameters of the contagion model, demonstrates that the proposed strategy considerably outperforms existing state-of-the-art targeted vaccination strategies in reducing the spread of the disease. In particular, the proposed vaccination strategy further reduces the number of infected nodes by 23–99%, compared to a vaccination strategy based on Betweenness Centrality.

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Content Patterns in Topic-Based Overlapping Communities

The Scientific World JOURNAL ◽

10.1155/2014/105428 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Sebastián A. Ríos ◽

Ricardo Muñoz

Keyword(s):

Network Topology ◽

Online Social Networks ◽

Virtual Communities ◽

State Of The Art ◽

Graph Structure ◽

Textual Information ◽

Overlapping Communities ◽

Network Nodes ◽

Detection Approach ◽

Important Challenge

Understanding the underlying community structure is an important challenge in social network analysis. Most state-of-the-art algorithms only consider structural properties to detect disjoint subcommunities and do not include the fact that people can belong to more than one community and also ignore the information contained in posts that users have made. To tackle this problem, we developed a novel methodology to detect overlapping subcommunities in online social networks and a method to analyze the content patterns for each subcommunities using topic models. This paper presents our main contribution, a hybrid algorithm which combines two different overlapping sub-community detection approaches: the first one considers the graph structure of the network (topology-based subcommunities detection approach) and the second one takes the textual information of the network nodes into consideration (topic-based subcommunities detection approach). Additionally we provide a method to analyze and compare the content generated. Tests on real-world virtual communities show that our algorithm outperforms other methods.

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Performance Analysis of Edge Computing in IoT

Elektronika ir Elektrotechnika ◽

10.5755/j01.eie.26.1.23235 ◽

2020 ◽

Vol 26 (1) ◽

pp. 72-77

Author(s):

Julius Skirelis ◽

Dalius Navakauskas

Keyword(s):

Performance Analysis ◽

Network Topology ◽

System Performance ◽

State Of The Art ◽

Edge Computing ◽

Network Bandwidth ◽

Network Parameters ◽

Network Topologies ◽

Considerable Impact ◽

Art Research

Upcoming 5G technology and the demand for near real-time response services raise the need for optimizing current IoT solutions. The aim of this paper is to model and execute the performance analysis of network structures suitable for Edge Computing in IoT. The prior research into different topology and parameter sets have not provided sufficient clarity, on which parameters had a considerable impact on overall system performance; therefore, repetitive simulations were performed to express dispersion of alternating values, as well as determining its confidence intervals. The paper presents Edge Computing service simulation setup on known and newly derived network topologies with different complexity varying network bandwidth and network delay parameters. The experimental investigation has revealed that the IoT configuration network is more sensitive to network topology, while the Internet configuration is more sensitive to network parameters. The discussion on the results received debates possible causes of performance differences in network parameters and device configurations, the comparison to similar state-of-the-art research results has also been presented. Finally, conclusions with recommendations based on the results acquired have been provided.

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Vaccination strategies against COVID-19 and the diffusion of anti-vaccination views

Scientific Reports ◽

10.1038/s41598-021-85555-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rafael Prieto Curiel ◽

Humberto González Ramírez

Keyword(s):

Diffusion Model ◽

Large Population ◽

Vaccination Strategy ◽

Small World ◽

Years Of Life Lost ◽

Scale Free ◽

Vaccination Strategies ◽

Network Topologies ◽

Network Disruptions ◽

The Impact

AbstractMisinformation is usually adjusted to fit distinct narratives and propagates rapidly through social networks. False beliefs, once adopted, are rarely corrected. Amidst the COVID-19 crisis, pandemic-deniers and people who oppose wearing face masks or quarantine have already been a substantial aspect of the development of the pandemic. With the vaccine for COVID-19, different anti-vaccine narratives are being created and are probably being adopted by large population groups with critical consequences. Assuming full adherence to vaccine administration, we use a diffusion model to analyse epidemic spreading and the impact of different vaccination strategies, measured with the average years of life lost, in three network topologies (a proximity, a scale-free and a small-world network). Then, using a similar diffusion model, we consider the spread of anti-vaccine views in the network, which are adopted based on a persuasiveness parameter of anti-vaccine views. Results show that even if anti-vaccine narratives have a small persuasiveness, a large part of the population will be rapidly exposed to them. Assuming that all individuals are equally likely to adopt anti-vaccine views after being exposed, more central nodes in the network, which are more exposed to these views, are more likely to adopt them. Comparing years of life lost, anti-vaccine views could have a significant cost not only on those who share them, since the core social benefits of a limited vaccination strategy (reduction of susceptible hosts, network disruptions and slowing the spread of the disease) are substantially shortened.

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Efficient Directed Densest Subgraph Discovery

ACM SIGMOD Record ◽

10.1145/3471485.3471494 ◽

2021 ◽

Vol 50 (1) ◽

pp. 33-40

Author(s):

Chenhao Ma ◽

Yixiang Fang ◽

Reynold Cheng ◽

Laks V.S. Lakshmanan ◽

Wenjie Zhang ◽

...

Keyword(s):

Approximation Algorithms ◽

State Of The Art ◽

Exact Algorithms ◽

Large Datasets ◽

Dense Subgraph ◽

Extensive Evaluation ◽

Wide Range ◽

Edge Graph ◽

Community Mining ◽

Densest Subgraph

Given a directed graph G, the directed densest subgraph (DDS) problem refers to the finding of a subgraph from G, whose density is the highest among all the subgraphs of G. The DDS problem is fundamental to a wide range of applications, such as fraud detection, community mining, and graph compression. However, existing DDS solutions suffer from efficiency and scalability problems: on a threethousand- edge graph, it takes three days for one of the best exact algorithms to complete. In this paper, we develop an efficient and scalable DDS solution. We introduce the notion of [x, y]-core, which is a dense subgraph for G, and show that the densest subgraph can be accurately located through the [x, y]-core with theoretical guarantees. Based on the [x, y]-core, we develop both exact and approximation algorithms. We have performed an extensive evaluation of our approaches on eight real large datasets. The results show that our proposed solutions are up to six orders of magnitude faster than the state-of-the-art.

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Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques

Applied Sciences ◽

10.3390/app11031241 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1241

Author(s):

Sergio D. Saldarriaga-Zuluaga ◽

Jesús M. López-Lezama ◽

Nicolás Muñoz-Galeano

Keyword(s):

Unsupervised Learning ◽

Distributed Generation ◽

Network Topology ◽

International Electrotechnical Commission ◽

Number Of Clusters ◽

Learning Techniques ◽

Topology Changes ◽

Network Topologies ◽

Optimal Coordination ◽

Operational Modes

Microgrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper proposes an adaptive protection approach that takes advantage of multiple setting groups that are available in commercial DOCRs to account for network topology changes in microgrids. Because the number of possible topologies is greater than the available setting groups, unsupervised learning techniques are explored to classify network topologies into a number of clusters that is equal to the number of setting groups. Subsequently, optimal settings are calculated for every topology cluster. Every setting is saved in the DOCRs as a different setting group that would be activated when a corresponding topology takes place. Several tests are performed on a benchmark IEC (International Electrotechnical Commission) microgrid, evidencing the applicability of the proposed approach.

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Reconfigurable Binary Neural Network Accelerator with Adaptive Parallelism Scheme

Electronics ◽

10.3390/electronics10030230 ◽

2021 ◽

Vol 10 (3) ◽

pp. 230

Author(s):

Jaechan Cho ◽

Yongchul Jung ◽

Seongjoo Lee ◽

Yunho Jung

Keyword(s):

Neural Networks ◽

High Throughput ◽

Deep Neural Networks ◽

State Of The Art ◽

Throughput Performance ◽

Adaptive Parallelism ◽

Sensor Applications ◽

Binary Neural Network ◽

Target Layer ◽

Network Topologies

Binary neural networks (BNNs) have attracted significant interest for the implementation of deep neural networks (DNNs) on resource-constrained edge devices, and various BNN accelerator architectures have been proposed to achieve higher efficiency. BNN accelerators can be divided into two categories: streaming and layer accelerators. Although streaming accelerators designed for a specific BNN network topology provide high throughput, they are infeasible for various sensor applications in edge AI because of their complexity and inflexibility. In contrast, layer accelerators with reasonable resources can support various network topologies, but they operate with the same parallelism for all the layers of the BNN, which degrades throughput performance at certain layers. To overcome this problem, we propose a BNN accelerator with adaptive parallelism that offers high throughput performance in all layers. The proposed accelerator analyzes target layer parameters and operates with optimal parallelism using reasonable resources. In addition, this architecture is able to fully compute all types of BNN layers thanks to its reconfigurability, and it can achieve a higher area–speed efficiency than existing accelerators. In performance evaluation using state-of-the-art BNN topologies, the designed BNN accelerator achieved an area–speed efficiency 9.69 times higher than previous FPGA implementations and 24% higher than existing VLSI implementations for BNNs.

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Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection

Journal Of Big Data ◽

10.1186/s40537-020-00391-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Asmaa El Hannani ◽

Rahhal Errattahi ◽

Fatima Zahra Salmam ◽

Thomas Hain ◽

Hassan Ouahmane

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Error Detection ◽

State Of The Art ◽

Rapid Development ◽

Unified Framework ◽

Human Machine Interaction ◽

Detection Analysis ◽

Extensive Evaluation ◽

Effectiveness And Efficiency

AbstractSpeech based human-machine interaction and natural language understanding applications have seen a rapid development and wide adoption over the last few decades. This has led to a proliferation of studies that investigate Error detection and classification in Automatic Speech Recognition (ASR) systems. However, different data sets and evaluation protocols are used, making direct comparisons of the proposed approaches (e.g. features and models) difficult. In this paper we perform an extensive evaluation of the effectiveness and efficiency of state-of-the-art approaches in a unified framework for both errors detection and errors type classification. We make three primary contributions throughout this paper: (1) we have compared our Variant Recurrent Neural Network (V-RNN) model with three other state-of-the-art neural based models, and have shown that the V-RNN model is the most effective classifier for ASR error detection in term of accuracy and speed, (2) we have compared four features’ settings, corresponding to different categories of predictor features and have shown that the generic features are particularly suitable for real-time ASR error detection applications, and (3) we have looked at the post generalization ability of our error detection framework and performed a detailed post detection analysis in order to perceive the recognition errors that are difficult to detect.

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Spreading predictability in complex networks

Scientific Reports ◽

10.1038/s41598-021-93611-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Na Zhao ◽

Jian Wang ◽

Yong Yu ◽

Jun-Yan Zhao ◽

Duan-Bing Chen

Keyword(s):

Infectious Diseases ◽

Prediction Model ◽

Complex Networks ◽

State Of The Art ◽

Experimental Results ◽

Vaccination Strategies ◽

Macro Scale ◽

Good Consistency ◽

The Future ◽

Rumor Control

AbstractMany state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this report, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future, not just the macro scale of infection. Experimental results on synthetic and real networks demonstrate that the infected individuals predicted by the model have good consistency with the actual infected ones based on simulations.

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On Directed Densest Subgraph Discovery

ACM Transactions on Database Systems ◽

10.1145/3483940 ◽

2021 ◽

Vol 46 (4) ◽

pp. 1-45

Author(s):

Chenhao Ma ◽

Yixiang Fang ◽

Reynold Cheng ◽

Laks V. S. Lakshmanan ◽

Wenjie Zhang ◽

...

Keyword(s):

State Of The Art ◽

Directed Graphs ◽

Exact Algorithms ◽

Large Datasets ◽

Dense Subgraph ◽

Extensive Evaluation ◽

Wide Range ◽

Edge Graph ◽

Community Mining ◽

Densest Subgraph

Given a directed graph G , the directed densest subgraph (DDS) problem refers to the finding of a subgraph from G , whose density is the highest among all the subgraphs of G . The DDS problem is fundamental to a wide range of applications, such as fraud detection, community mining, and graph compression. However, existing DDS solutions suffer from efficiency and scalability problems: on a 3,000-edge graph, it takes three days for one of the best exact algorithms to complete. In this article, we develop an efficient and scalable DDS solution. We introduce the notion of [ x , y ]-core, which is a dense subgraph for G , and show that the densest subgraph can be accurately located through the [ x , y ]-core with theoretical guarantees. Based on the [ x , y ]-core, we develop exact and approximation algorithms. We further study the problems of maintaining the DDS over dynamic directed graphs and finding the weighted DDS on weighted directed graphs, and we develop efficient non-trivial algorithms to solve these two problems by extending our DDS algorithms. We have performed an extensive evaluation of our approaches on 15 real large datasets. The results show that our proposed solutions are up to six orders of magnitude faster than the state-of-the-art.

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An Effective Fairness Scheme for Named Data Networking

Electronics ◽

10.3390/electronics9050749 ◽

2020 ◽

Vol 9 (5) ◽

pp. 749

Author(s):

Hammad Zafar ◽

Ziaul Haq Abbas ◽

Ghulam Abbas ◽

Fazal Muhammad ◽

Muhammad Tufail ◽

...

Keyword(s):

Interest Rates ◽

Future Internet ◽

High Volume ◽

Named Data Networking ◽

Network Resources ◽

Network Nodes ◽

Network Routers ◽

Rate Shaping ◽

Network Topologies ◽

Data Networking

Named data networking (NDN) is a revolutionary approach to cater for modern and future Internet usage trends. The advancements in web services, social networks and cloud computing have shifted Internet utilization towards information delivery. Information-centric networking (ICN) enables content-awareness in the network layer and adopts name-based routing through the NDN architecture. Data delivery in NDN is receiver-driven pull-based and governed by requests (interests) sent out by the receiver. The ever-increasing share of high-volume media streams traversing the Internet due to the popularity and availability of video-streaming services can put a strain on network resources and lead to congestion. Since most congestion control techniques proposed for NDN are receiver-based and rely on the users to adjust their interest rates, a fairness scheme needs to be implemented at the intermediate network nodes to ensure that “rogue” users do not monopolize the available network resources. This paper proposes a fairness-based active queue management at network routers which performs per-flow interest rate shaping in order to ensure fair allocation of resources. Different congestion scenarios for both single path and multipath network topologies have been simulated to test the effectiveness of the proposed fairness scheme. Performance of the scheme is evaluated using Jain’s fairness index as a fairness metric.

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