A disaster risk assessment model for the conservation of cultural heritage sites in Melaka Malaysia

There exist ongoing efforts to reduce the exposure of Cultural Heritage Sites (CHSs) to Disaster Risk (DR). However, a complicated issue these efforts face is that of ‘estimation’ whereby no standardised unit exist for assessing the effects of Cultural Heritage (CH) exposed to DR as compared to other exposed items having standardised assessment units such as; ‘number of people’ for deaths, injured and displaced, ‘dollar’ for economic impact, ‘number of units’ for building stock or animals among others. This issue inhibits the effective assessment of CHSs exposed to DR. Although there exist several DR assessment frameworks for conserving CHSs, the conceptualisation of DR in these studies fall short of good practice such as international strategy for disaster reduction by United Nations which expresses DR to being a hollistic interplay of three variables (hazard, vulnerability and capacity). Adopting such good practice, this research seeks to propose a mechanism of DR assessment aimed at reducing the exposure of CHSs to DR. Quantitative method adopted for data collection involved a survey of 365 respondents at CHSs in Melaka using a structured questionnaire. Similarly, data analysis consisted of a two-step Structural Equation Modelling (measurement and structural modelling). The achievement of the recommended thresholds for unidimensionality, validity and reliability by the measurement models is a testimony to the model fitness for all 8 first-order independent variables and 2 first-order dependent variables. While hazard had a ‘small’ but negative effect, vulnerability had a ‘very large’ but negative effect on the exposure of CHSs to DR. Likewise, capacity had a ‘small’ but positive effect on the exposure of CHSs to DR. The outcome of this study is a Disaster Risk Assessment Model (DRAM) aimed at reducing DR to CHSs. The implication of this research is providing insights on decisions for DR assessment to institutions, policymakers and statutory bodies towards their approach to enhancing the conservation of CHSs

Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions

We consider the paradigm of a black box AI system that makes life-critical decisions. We propose an "arguing machines" framework that pairs the primary AI system with a secondary one that is independently trained to perform the same task. We show that disagreement between the two systems, without any knowledge of underlying system design or operation, is sufficient to arbitrarily improve the accuracy of the overall decision pipeline given human supervision over disagreements. We demonstrate this system in two applications: (1) an illustrative example of image classification and (2) on large-scale real-world semi-autonomous driving data. For the first application, we apply this framework to image classification achieving a reduction from 8.0% to 2.8% top-5 error on ImageNet. For the second application, we apply this framework to Tesla Autopilot and demonstrate the ability to predict 90.4% of system disengagements that were labeled by human annotators as challenging and needing human supervision

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving