Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 5

Search results for: ProB

5 Challenges for IoT Adoption in India: A Study Based on Foresight Analysis for 2025

Authors: Shruti Chopra, Vikas Rao Vadi


In the era of the digital world, the Internet of Things (IoT) has been receiving significant attention. Its ubiquitous connectivity between humans, machines to machines (M2M) and machines to humans provides it a potential to transform the society and establish an ecosystem to serve new dimensions to the economy of the country. Thereby, this study has attempted to identify the challenges that seem prevalent in IoT adoption in India through the literature survey. Further, the data has been collected by taking the opinions of experts to conduct the foresight analysis and it has been analyzed with the help of scenario planning process – Micmac, Mactor, Multipol, and Smic-Prob. As a methodology, the study has identified the relationship between variables through variable analysis using Micmac and actor analysis using Mactor, this paper has attempted to generate the entire field of possibilities in terms of hypotheses and construct various scenarios through Multipol. And lastly, the findings of the study include final scenarios that are selected using Smic-Prob by assigning the probability to all the scenarios (including the conditional probability). This study may help the practitioners and policymakers to remove the obstacles to successfully implement the IoT in India.

Keywords: Internet of Thing (IoT), foresight analysis, scenario planning, challenges, policymaking

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4 Modeling and Analyzing the WAP Class 2 Wireless Transaction Protocol Using Event-B

Authors: Rajaa Filali, Mohamed Bouhdadi


This paper presents an incremental formal development of the Wireless Transaction Protocol (WTP) in Event-B. WTP is part of the Wireless Application Protocol (WAP) architectures and provides a reliable request-response service. To model and verify the protocol, we use the formal technique Event-B which provides an accessible and rigorous development method. This interaction between modelling and proving reduces the complexity and helps to eliminate misunderstandings, inconsistencies, and specification gaps. As result, verification of WTP allows us to find some deficiencies in the current specification.

Keywords: event-B, wireless transaction protocol, proof obligation, refinement, Rodin, ProB

Procedia PDF Downloads 240
3 A Nonlinear Stochastic Differential Equation Model for Financial Bubbles and Crashes with Finite-Time Singularities

Authors: Haowen Xi


We propose and solve exactly a class of non-linear generalization of the Black-Scholes process of stochastic differential equations describing price bubble and crashes dynamics. As a result of nonlinear positive feedback, the faster-than-exponential price positive growth (bubble forming) and negative price growth (crash forming) are found to be the power-law finite-time singularity in which bubbles and crashes price formation ending at finite critical time tc. While most literature on the market bubble and crash process focuses on the nonlinear positive feedback mechanism aspect, very few studies concern the noise level on the same process. The present work adds to the market bubble and crashes literature by studying the external sources noise influence on the critical time tc of the bubble forming and crashes forming. Two main results will be discussed: (1) the analytical expression of expected value of the critical time is found and unexpected critical slowing down due to the coupling external noise is predicted; (2) numerical simulations of the nonlinear stochastic equation is presented, and the probability distribution of Prob(tc) is found to be the inverse gamma function.

Keywords: bubble, crash, finite-time-singular, numerical simulation, price dynamics, stochastic differential equations

Procedia PDF Downloads 53
2 The Aesthetic and Critiques of Weimar Democracy: The Counter and Complement to Carl Schmitt’s Political Myth

Authors: Peter Jin


Ever since the recent resurgence of interest in political theorist Carl Schmitt’s work, much of the current analysis on Schmitt has fo-cused on evaluating Schmitt’s legacy by exposing contradictions in his rationale. Rather than condemn such contradictions, this paper instead seeks to analyze these contradictions in an effort to better understand the radical shift in Schmitt’s intellectual trajectory from an astute critic of liberal democracy to a fascist apologist towards the end of the Weimar period. An essential part of this change is his interest in what Schmitt called ‘the emergence of aesthetics.’ Schmitt diagnosed the underlying issue with the aesthetic in the political sphere to be its irrationalism, indifference, and indeci-siveness. For Schmitt, the latter two of these were especially prob-lematic for two of his key concepts: the ‘political’ and ‘the shared historical reality.’ Schmitt’s radical depiction of ‘the political’ as an existential opposition of allegiances that necessitated a state of emergency and a decisionist sovereign political struggle required an equally radical justification, or Schmitt’s call for ‘a shared his-torical reality’ not based in historical fact yet able to mobilize the masses. In this way, Schmitt clearly condemns the indifferent, indecisive aesthetic that runs against his decisionist, action-oriented political theory. Yet despite his firm stance against aestheticism, Schmitt himself used the evocative and irrational power of aesthet-icism as a tool to present his own ‘political myth’ that compelled believers to join in decisive unity against a common enemy. In short, Schmitt’s contradictions on aestheticism and his creation of a ‘political myth’ suggest that Schmitt’s underlying conflict with aestheticism was not as much of an issue of irrationality as it was a chronic preoccupation with coercing concrete action at the expense of rational deliberation.

Keywords: aesthetics of the political, Carl Schmitt, political myth, Weimar democracy

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1 Using Statistical Significance and Prediction to Test Long/Short Term Public Services and Patients' Cohorts: A Case Study in Scotland

Authors: Raptis Sotirios


Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

Procedia PDF Downloads 119