Measuring the Network Vulnerability Based on Markov Criticality

Vulnerability assessment—a critical issue for networks—attempts to foresee unexpected destructive events or hostile attacks in the whole system. In this article, we consider a new Markov global connectivity metric—Kemeny constant, and take its derivative called Markov criticality to identify critical links. Markov criticality allows us to find links that are most influential on the derivative of Kemeny constant. Thus, we can utilize it to identity a critical link ( i , j ) from node i to node j , such that removing it leads to a minimization of networks’ global connectivity, i.e., the Kemeny constant. Furthermore, we also define a novel vulnerability index to measure the average speed by which we can disconnect a specified ratio of links with network decomposition. Our method is of high efficiency, which can be easily employed to calculate the Markov criticality in real-life networks. Comprehensive experiments on several synthetic and real-life networks have demonstrated our method’s better performance by comparing it with state-of-the-art baseline approaches.

Download Full-text

On-Shelf Utility Mining of Sequence Data

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3457570 ◽

2021 ◽

Vol 16 (2) ◽

pp. 1-31

Author(s):

Chunkai Zhang ◽

Zilin Du ◽

Yuting Yang ◽

Wensheng Gan ◽

Philip S. Yu

Keyword(s):

High Efficiency ◽

Sequence Data ◽

Real Life ◽

Search Space ◽

Upper Bounds ◽

Utility Mining ◽

Limited Memory ◽

Time Periods ◽

High Utility ◽

Synthetic Datasets

Utility mining has emerged as an important and interesting topic owing to its wide application and considerable popularity. However, conventional utility mining methods have a bias toward items that have longer on-shelf time as they have a greater chance to generate a high utility. To eliminate the bias, the problem of on-shelf utility mining (OSUM) is introduced. In this article, we focus on the task of OSUM of sequence data, where the sequential database is divided into several partitions according to time periods and items are associated with utilities and several on-shelf time periods. To address the problem, we propose two methods, OSUM of sequence data (OSUMS) and OSUMS + , to extract on-shelf high-utility sequential patterns. For further efficiency, we also design several strategies to reduce the search space and avoid redundant calculation with two upper bounds time prefix extension utility ( TPEU ) and time reduced sequence utility ( TRSU ). In addition, two novel data structures are developed for facilitating the calculation of upper bounds and utilities. Substantial experimental results on certain real and synthetic datasets show that the two methods outperform the state-of-the-art algorithm. In conclusion, OSUMS may consume a large amount of memory and is unsuitable for cases with limited memory, while OSUMS + has wider real-life applications owing to its high efficiency.

Download Full-text

Urban Vulnerability Assessment for Pandemic Surveillance—The COVID-19 Case in Bogotá, Colombia

Sustainability ◽

10.3390/su13063402 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3402

Author(s):

Jeisson Prieto ◽

Rafael Malagón ◽

Jonatan Gomez ◽

Elizabeth León

Keyword(s):

Vulnerability Assessment ◽

Disease Transmission ◽

Urban Areas ◽

A Priori ◽

Vulnerability Index ◽

Past Century ◽

Vulnerability Factors ◽

Early Assessment ◽

Global Citizens ◽

Urban Vulnerability

A pandemic devastates the lives of global citizens and causes significant economic, social, and political disruption. Evidence suggests that the likelihood of pandemics has increased over the past century because of increased global travel and integration, urbanization, and changes in land use with a profound affectation of society–nature metabolism. Further, evidence concerning the urban character of the pandemic has underlined the role of cities in disease transmission. An early assessment of the severity of infection and transmissibility can help quantify the pandemic potential and prioritize surveillance to control highly vulnerable urban areas in pandemics. In this paper, an Urban Vulnerability Assessment (UVA) methodology is proposed. UVA investigates various vulnerability factors related to pandemics to assess the vulnerability in urban areas. A vulnerability index is constructed by the aggregation of multiple vulnerability factors computed on each urban area (i.e., urban density, poverty index, informal labor, transmission routes). This methodology is useful in a-priori evaluation and development of policies and programs aimed at reducing disaster risk (DRR) at different scales (i.e., addressing urban vulnerability at national, regional, and provincial scales), under diverse scenarios of resources scarcity (i.e., short and long-term actions), and for different audiences (i.e., the general public, policy-makers, international organizations). The applicability of UVA is shown by the identification of high vulnerable areas based on publicly available data where surveillance should be prioritized in the COVID-19 pandemic in Bogotá, Colombia.

Download Full-text

A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation

Metals ◽

10.3390/met11060870 ◽

2021 ◽

Vol 11 (6) ◽

pp. 870

Author(s):

Robby Neven ◽

Toon Goedemé

Keyword(s):

Process Parameters ◽

Visual Inspection ◽

Steel Surface ◽

Surface Defect ◽

State Of The Art ◽

Sheet Steel ◽

Real Life ◽

Single Task ◽

Severity Estimation ◽

Task Model

Automating sheet steel visual inspection can improve quality and reduce costs during its production. While many manufacturers still rely on manual or traditional inspection methods, deep learning-based approaches have proven their efficiency. In this paper, we go beyond the state-of-the-art in this domain by proposing a multi-task model that performs both pixel-based defect segmentation and severity estimation of the defects in one two-branch network. Additionally, we show how incorporation of the production process parameters improves the model’s performance. After manually constructing a real-life industrial dataset, we first implemented and trained two single-task models performing the defect segmentation and severity estimation tasks separately. Next, we compared this to a multi-task model that simultaneously performs the two tasks at hand. By combining the tasks into one model, both segmentation tasks improved by 2.5% and 3% mIoU, respectively. In the next step, we extended the multi-task model using sensor fusion with process parameters. We demonstrate that the incorporation of the process parameters resulted in a further mIoU increase of 6.8% and 2.9% for the defect segmentation and severity estimation tasks, respectively.

Download Full-text

New Multi-View Classification Method with Uncertain Data

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3458282 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-23

Author(s):

Bo Liu ◽

Haowen Zhong ◽

Yanshan Xiao

Keyword(s):

Learning Strategy ◽

State Of The Art ◽

Uncertain Data ◽

Real Life ◽

Support Vector ◽

Classification Methods ◽

Complementary Information ◽

Novel Approach ◽

Svm Model ◽

Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

Download Full-text

Drone Deep Reinforcement Learning: A Review

Electronics ◽

10.3390/electronics10090999 ◽

2021 ◽

Vol 10 (9) ◽

pp. 999

Author(s):

Ahmad Taher Azar ◽

Anis Koubaa ◽

Nada Ali Mohamed ◽

Habiba A. Ibrahim ◽

Zahra Fathy Ibrahim ◽

...

Keyword(s):

Reinforcement Learning ◽

State Of The Art ◽

Real Life ◽

Environment Monitoring ◽

Simulated Environments ◽

Infrastructure Inspection ◽

Remote Sensing Mapping ◽

And Control ◽

The Military ◽

Uav Navigation

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.

Download Full-text

Fast Accurate and Automatic Brushstroke Extraction

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3429742 ◽

2021 ◽

Vol 17 (2) ◽

pp. 1-24

Author(s):

Yunfei Fu ◽

Hongchuan Yu ◽

Chih-Kuo Yeh ◽

Tong-Yee Lee ◽

Jian J. Zhang

Keyword(s):

Neural Network ◽

Efficient Algorithm ◽

Deep Neural Network ◽

High Efficiency ◽

State Of The Art ◽

High Reliability ◽

The Other ◽

Manual Annotation ◽

Stroke Extraction ◽

Art Research

Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.

Download Full-text

Quasi-graphitic carbon shell-induced Cu confinement promotes electrocatalytic CO2 reduction toward C2+ products

Nature Communications ◽

10.1038/s41467-021-24105-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ji-Yong Kim ◽

Deokgi Hong ◽

Jae-Chan Lee ◽

Hyoung Gyun Kim ◽

Sungwoo Lee ◽

...

Keyword(s):

High Efficiency ◽

Co2 Reduction ◽

Value Added ◽

Material Design ◽

Catalyst System ◽

Critical Issue ◽

Reduction Reaction ◽

Catalyst Design ◽

Design Strategies ◽

Faradaic Efficiency

AbstractFor steady electroconversion to value-added chemical products with high efficiency, electrocatalyst reconstruction during electrochemical reactions is a critical issue in catalyst design strategies. Here, we report a reconstruction-immunized catalyst system in which Cu nanoparticles are protected by a quasi-graphitic C shell. This C shell epitaxially grew on Cu with quasi-graphitic bonding via a gas–solid reaction governed by the CO (g) - CO2 (g) - C (s) equilibrium. The quasi-graphitic C shell-coated Cu was stable during the CO2 reduction reaction and provided a platform for rational material design. C2+ product selectivity could be additionally improved by doping p-block elements. These elements modulated the electronic structure of the Cu surface and its binding properties, which can affect the intermediate binding and CO dimerization barrier. B-modified Cu attained a 68.1% Faradaic efficiency for C2H4 at −0.55 V (vs RHE) and a C2H4 cathodic power conversion efficiency of 44.0%. In the case of N-modified Cu, an improved C2+ selectivity of 82.3% at a partial current density of 329.2 mA/cm2 was acquired. Quasi-graphitic C shells, which enable surface stabilization and inner element doping, can realize stable CO2-to-C2H4 conversion over 180 h and allow practical application of electrocatalysts for renewable energy conversion.

Download Full-text

Fabry Disease Therapy: State-of-the-Art and Current Challenges

International Journal of Molecular Sciences ◽

10.3390/ijms22010206 ◽

2020 ◽

Vol 22 (1) ◽

pp. 206

Author(s):

Olga Azevedo ◽

Miguel Fernandes Gago ◽

Gabriel Miltenberger-Miltenyi ◽

Nuno Sousa ◽

Damião Cunha

Keyword(s):

Fabry Disease ◽

State Of The Art ◽

Large Body ◽

Real Life ◽

Clinical Manifestations ◽

Substrate Reduction Therapy ◽

Lysosomal Storage ◽

New Therapies ◽

Enzyme Replacement

Fabry disease (FD) is a lysosomal storage disorder caused by mutations of the GLA gene that lead to a deficiency of the enzymatic activity of α-galactosidase A. Available therapies for FD include enzyme replacement therapy (ERT) (agalsidase alfa and agalsidase beta) and the chaperone migalastat. Despite the large body of literature published about ERT over the years, many issues remain unresolved, such as the optimal dose, the best timing to start therapy, and the clinical impact of anti-drug antibodies. Migalastat was recently approved for FD patients with amenable GLA mutations; however, recent studies have raised concerns that “in vitro” amenability may not always reflect “in vivo” amenability, and some findings on real-life studies have contrasted with the results of the pivotal clinical trials. Moreover, both FD specific therapies present limitations, and the attempt to correct the enzymatic deficiency, either by enzyme exogenous administration or enzyme stabilization with a chaperone, has not shown to be able to fully revert FD pathology and clinical manifestations. Therefore, several new therapies are under research, including new forms of ERT, substrate reduction therapy, mRNA therapy, and gene therapy. In this review, we provide an overview of the state-of-the-art on the currently approved and emerging new therapies for adult patients with FD.

Download Full-text

A Family of Efficient Sloshing Liquid Dampers for Suppression of Wind-Induced Instabilities

Journal of Vibration and Control ◽

10.1177/107754603030773 ◽

2003 ◽

Vol 9 (3-4) ◽

pp. 361-386 ◽

Cited By ~ 9

Author(s):

V. J. Modi ◽

A. Akinturk ◽

W. Tse

Keyword(s):

Energy Dissipation ◽

High Efficiency ◽

Damping Ratio ◽

Real Life ◽

Wind Tunnel Test ◽

Tall Buildings ◽

Two Phases ◽

Liquid Dampers ◽

Obstacle Geometry ◽

Floating Particles

Bluff structures in the form of tall buildings, smokestacks, control towers, bridges, etc., are susceptible to vortex resonance and galloping type of instabilities. One approach to vibration control of such systems is through energy dissipation using sloshing liquid dampers. In this paper we focus on enhancing the energy dissipation efficiency of a rectangular liquid damper through the introduction of two-dimensional obstacles as well as floating particles. The investigation has two phases. To begin with, a parametric free vibration study aimed at the optimization of the obstacle geometry is undertaken to arrive at configurations promising increased damping ratio and hence higher energy dissipation. The study is complemented by an extensive wind tunnel test program, which substantiates the effectiveness of this class of damper in suppressing both vortex resonance and galloping type of instabilities. Simplicity of design, ease of implementation, minimal maintenance, reliability as well as high efficiency make such liquid dampers quite attractive for real-life applications.

Download Full-text

SOSerbia: Android-Based Software Platform for Sending Emergency Messages

Complexity ◽

10.1155/2018/8283919 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Mihailo Jovanovic ◽

Ivan Babic ◽

Milan Cabarkapa ◽

Jelena Misic ◽

Sasa Mijalkovic ◽

...

Keyword(s):

Emergency Response ◽

State Of The Art ◽

Complete Solution ◽

Real Life ◽

Android Application ◽

Unique Combination ◽

Background Process ◽

Life Problems ◽

The Republic ◽

National Police

This paper presents Android-based SOS platform named SOSerbia for sending emergency messages by citizens in Serbia. The heart of the platform is SOS client Android application which is an easy and simple solution for sending SOS messages with unique combination of volume buttons. The proposed platform solves a lot of safety, security, and emergency problems for people who can be in dangerous situations. After a person presses a correct combination of buttons, a message with his or her location is sent to the operating center of the Serbian Police. The platform merges several appropriately combined advanced Android technologies into one complete solution. The proposed solution also uses the Google location API for getting user’s location and Media Player broadcast receiver for reading pressed buttons for volume. This logic can be also customized for any other mobile operating system. In other words, the proposed architecture can be also implemented in iOS or Windows OS. It should be noted that the proposed architecture is optimized for different mobile devices. It is also implemented with simple widget and background process based on location. The proposed platform is experimentally demonstrated as a part of emergency response center at the Ministry of Interior of the Republic of Serbia. This platform overcomes real-life problems that other state-of-the-art solutions introduce and can be applied and integrated easily in any national police and e-government systems.

Download Full-text