scholarly journals Vpassport: A Digital Architecture to Support Social Restart during the SARS-CoV-2 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3945
Author(s):  
Guendalina Capece ◽  
Paolo Bazzica
Keyword(s):  
Public Transportation ◽  
Large Scale ◽  
Social Activities ◽  
Digital Architecture ◽  
Authority Control ◽  
Test Execution ◽  
Health Authorities ◽  
Proposed Model ◽  
Effective System ◽  
Result Management

As a consequence of the Sars-CoV-2 pandemic, the causative agent of the COVID-19 coronavirus, the world is currently witnessing profound changes in everyday life. The infection and the resulting death number forecasts generate an increasing threat to the lives of people and the economics of countries. As the acute phase of the pandemic ends, the greatest challenge that most governments are currently undergoing is the lack of tools to certify the immunity status of citizens and the related infection risk of the spread of the COVID-19 virus. To mitigate this challenge, this study proposes an innovative approach to implement a set of IT tools, here named VPassport, that assist large-scale test execution/result management in a distributed way and store the results of all tests made through all channels in a blockchain under country authority control. The proposed approach aims to produce an effective system able to support governments, health authorities, and citizens to take informed decisions on which services and social activities can be accessed respecting policies and rules set by the authorities. This aims to allow a controlled restart of the activities of the country, giving to all citizens the possibility to manage their immunity tests while allowing the authorities to manage the reopening of services and social activities. The proposed model helps in managing this phase and, therefore, the resulting outcome can be used to authorize possible behaviors (e.g., going to the office, production plants, public transportation, theaters, cinemas, etc.). The knowledge of being infected or not in a secure and not modifiable way that can be shown in a simple way, accessible to all, will be the real change in managing the coexistence with the virus until a vaccine will be available for all people.

Why Has There Been No People’s Power Rebellion in North Korea?

2018 ◽  
pp. 1-34
Author(s):  
Andrew Jackson
Keyword(s):  
Rural Areas ◽  
North Korea ◽  
Large Scale ◽  
Political Elite ◽  
Information Control ◽  
Al Jazeera ◽  
The North ◽  
New Information ◽  
Effective System ◽  
The Right

One scenario put forward by researchers, political commentators and journalists for the collapse of North Korea has been a People’s Power (or popular) rebellion. This paper analyses why no popular rebellion has occurred in the DPRK under Kim Jong Un. It challenges the assumption that popular rebellion would happen because of widespread anger caused by a greater awareness of superior economic conditions outside the DPRK. Using Jack Goldstone’s theoretical expla-nations for the outbreak of popular rebellion, and comparisons with the 1989 Romanian and 2010–11 Tunisian transitions, this paper argues that marketi-zation has led to a loosening of state ideological control and to an influx of infor-mation about conditions in the outside world. However, unlike the Tunisian transitions—in which a new information context shaped by social media, the Al-Jazeera network and an experience of protest helped create a sense of pan-Arab solidarity amongst Tunisians resisting their government—there has been no similar ideology unifying North Koreans against their regime. There is evidence of discontent in market unrest in the DPRK, although protests between 2011 and the present have mostly been in defense of the right of people to support themselves through private trade. North Koreans believe this right has been guaranteed, or at least tacitly condoned, by the Kim Jong Un government. There has not been any large-scale explosion of popular anger because the state has not attempted to crush market activities outright under Kim Jong Un. There are other reasons why no popular rebellion has occurred in the North. Unlike Tunisia, the DPRK lacks a dissident political elite capable of leading an opposition movement, and unlike Romania, the DPRK authorities have shown some flexibility in their anti-dissent strategies, taking a more tolerant approach to protests against economic issues. Reduced levels of violence during periods of unrest and an effective system of information control may have helped restrict the expansion of unrest beyond rural areas.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

scholarly journals Assessing the Response of Snow Avalanche Runout Altitudes to Climate Fluctuations Using Hierarchical Modeling: Application to 61 Winters of Data in France

2010 ◽  
Vol 23 (12) ◽  
pp. 3157-3180 ◽  
Author(s):  
N. Eckert ◽  
H. Baya ◽  
M. Deschatres
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Hierarchical Modeling ◽  
Control Measures ◽  
Level Shift ◽  
Snow Avalanches ◽  
Winter Climate ◽  
Climate Fluctuations ◽  
High Magnitude ◽  
Proposed Model

Abstract Snow avalanches are natural hazards strongly controlled by the mountain winter climate, but their recent response to climate change has thus far been poorly documented. In this paper, hierarchical modeling is used to obtain robust indexes of the annual fluctuations of runout altitudes. The proposed model includes a possible level shift, and distinguishes common large-scale signals in both mean- and high-magnitude events from the interannual variability. Application to the data available in France over the last 61 winters shows that the mean runout altitude is not different now than it was 60 yr ago, but that snow avalanches have been retreating since 1977. This trend is of particular note for high-magnitude events, which have seen their probability rates halved, a crucial result in terms of hazard assessment. Avalanche control measures, observation errors, and model limitations are insufficient explanations for these trends. On the other hand, strong similarities in the pattern of behavior of the proposed runout indexes and several climate datasets are shown, as well as a consistent evolution of the preferred flow regime. The proposed runout indexes may therefore be usable as indicators of climate change at high altitudes.

scholarly journals An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 79 ◽  
Author(s):  
Xiaoyu Han ◽  
Yue Zhang ◽  
Wenkai Zhang ◽  
Tinglei Huang
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Additional Information ◽  
Named Entity ◽  
Proposed Model ◽  
The Relationship ◽  
Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

A Multi-Period Evacuation Vehicle Routing Problem Model

2014 ◽  
Vol 931-932 ◽  
pp. 578-582
Author(s):  
Sunarin Chanta ◽  
Ornurai Sangsawang
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
High Water ◽  
Mixed Integer ◽  
Routing Problem ◽  
Problem Model ◽  
Proposed Model ◽  
Evacuation Routing

In this paper, we proposed an optimization model that addresses the evacuation routing problem for flood disaster when evacuees trying to move from affected areas to safe places using public transportation. A focus is on the situation of evacuating during high water level when special high vehicles are needed. The objective is to minimize the total traveled distance through evacuation periods where a limited number of vehicles is given. We formulated the problem as a mixed integer programming model based on the capacitated vehicle routing problem with multiple evcuation periods where demand changing by the time. The proposed model has been tested on a real-world case study affected by the severe flooding in Thailand, 2011.

The Two-Party System in British Politics

1953 ◽  
Vol 47 (2) ◽  
pp. 337-358 ◽  
Author(s):  
Leslie Lipson
Keyword(s):  
Economic Policy ◽  
Party System ◽  
Large Scale ◽  
British Politics ◽  
State Planning ◽  
Party Government ◽  
Contemporary State ◽  
Long Duration ◽  
Effective System ◽  
Laissez Faire

Britain may fairly be called the classic home of two-party government. This claim is justifiable because of some characteristics for which the system, as employed in Britain, is distinctive. Chief among these is its long duration. Although there is room for disagreement among historians about the time and circumstances of its birth, it would be difficult to deny that two-party government was established earlier, has lasted longer, and at the present time is probably more firmly rooted there than in any contemporary state. Indeed, the practice of simplifying the complexities of politics into a contest for office between a pair of major claimants has endured in Britain through a catalogue of changes which would assuredly have wrecked a less effective system. In that country it has survived the evolution from an oligarchy of aristocrats to a democracy of the whole people; the transfer of power from monarchy to parliament and then from parliament to cabinet; the rise of large-scale industry with its social aftermath; the switch in economic policy from mercantilism to laissez faire and from this to state planning; and withal, the expansion and subsequent shrinkage of Britain's international might.

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