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With the development of the location-based social networks (LBSNs) and the popular of mobile devices, a plenty of user's check-in data accumulated enough to enable personalized Point-of-Interest recommendations services. In this paper, we propose a scheme of modeling user's preferences on spatiotemporal topics (UPOST scheme) for accurate individualized POI recommendation. In the UPOST scheme, we discover...
Service constraints are usage restrictions on service features that are imposed by service providers. Such constraints need to be verified prior to the execution of a service in order to ensure correct service execution. In the case of composite services, the set of applicable constraints is derived from the service constraints defined over the individual service components that are part of the service...
Distributed autonomous multi-agent reasoning and classification systems have been thought of to be the basis of intelligence and have wide applications in the space of operational intelligence in closing the loop between sensing, analytics, and actions. This paper targets multi-agent systems that employ rulebased logics (i.e., rules that determine the output/response of an agent depending on the range...
Infrastructure failures have severe consequences which often have a negative impact on the society and the economy. In this paper, we propose a machine learning model to assist in risk management to minimise the cost of infrastructure maintenance. Due to the vast volume and complexity of infrastructure datasets, such problem is often computationally expensive to compute. A Bayesian nonparametric approach...
In this paper we present the results of experimental observation from October till November 2016. Variations in angles-of-arrival (AoA) on two mid-latitude paths with different orientation were calculated and the average accuracy of single-station location (SSL) was estimated. The comparison of obtained data with radio channel simulation results was conducted in terms of naturally disturbed ionosphere...
This paper develops a learning model for personalized adaptive cruise control that can learn from human demonstration online and mimic a human driver's driving strategies in the dynamic traffic environment. Under the framework of the proposed model, reinforcement learning is used to capture the human-desired driving strategy, and the proportion-integration-differentiation controller is adopted to...
We present a novel use for self-organizing maps (SOMs) as an essential building block for incremental learning algorithms. SOMs are very well suited for this purpose because they are inherently online learning algorithms, because their weight updates are localized around the best-matching unit, which inherently protects them against catastrophic forgetting, and last but not least because they have...
The focus of the research is the low voltage electrical system of buildings, where the main switch is up to 2500 A. This paper describes simple calculation of the payback of innovations using a simple model of electrical system of buildings without the use of complex computer programs. For using this method, it is sufficient to have conventional engineering knowledge. The proposed model considers...
As the dynamics of traffic patterns and increased congestion result in challenging problems on road systems, a vast number of real-time corridor management strategies have been introduced in the field of transportation. This study integrates a mesoscopic dynamic traffic assignment simulation model with existing responsive ramp metering strategies. The purpose of this paper is to introduce an initial...
Crowdsourcing labor market platforms consist of a variety of jobs spanning multiple problem domains and their respective specialized or diverse worker pools. Each platform currently operates independently and isolated from the potential benefits of sharing job and worker pool data across platforms. Previous work introduces infrastructure that optimizes the sharing of both job and worker data collectively,...
Current procedure in travel demand estimation models is to separately deal with attraction, production and trip distribution, where the latter typically assumes inverse distance proportionality. We show that this procedure leads to errors in the demand estimation, particularly when dealing with very specific zones and heterogeneous travel behavior. We argue that this traditional procedure is rooted...
We present here the results of our investigation of a transactional model of parallel programming on cluster computing systems. This model is specifically targeted for graph applications with the goal of harnessing unstructured parallelism inherently present in many such problems. In this model, tasks for vertex-centric computations are executed optimistically in parallel as serializable transactions...
Object-oriented design patterns are used to solve recurring problems in the design of object-oriented software. The success of existing design patterns encourages researchers and practitioners to propose new design patterns especially for solving recurring design problems in specific domains. Assessing the quality of the design patterns being developed is an important task for a design pattern developer...
Focus on the first China domestic coking flue gas desulfurization and denitriation integrated device, in order to solve the problem that the entrance parameters fluctuate and a detection lag exists due to the upstream coking workshop, which is extremely unfavorable to the optimal control of desulfurization and denitriation process. An intelligent integrated prediction model of flue gas SO2 concentration,...
Despite the significant evolution of the design and implementation of business process models, a transactional approach that evolves an incremental and adaptive strategy remains an important challenge to be overcome. Traditional frameworks such as BPEL, Process Algebra, and Petri Net require an additional software layer or some third party toolkits to be able to enforce a data-state based transaction...
The increasing amount of data generated every second of time and available from multiple sources and in various formats has generated new ways of dealing with them: Big Data. Methodologies and technologies have been developed to make good use of these data, but their adoption by organizations is complex in many respects. A review of the state of the art shows several models and frameworks where solutions...
The present paper considers constructing associative models for non-linear processes and a condition of stability of the process in a predicted time instant. A methodology of selecting enough quantity of input vectors to construct process models based on the associative search is considered. As well, an example of constructing models on the basis of the associative search and meeting the condition...
Integration of Advanced Driver Assistance Systems (ADAS) and Vehicle to Vehicle communication (V2V) provide a wide range of applications with the potential to enhance road safety and prevent traffic accidents. During the last few years, significant attention has been paid to developing and implementing both systems, since V2V and ADAS are considered as key technologies of future Intelligent Transportation...
Analog/Mixed-Signal (AMS) design and verification strongly relies on more or less abstract models to make extensive simulations feasible. Maintaining consistent behavior between system model and implementation is crucial for a correct verification. Operating conditions have to be a major concern: A faulty model might introduce false-positive verification results despite of erroneous operating conditions...
Studies of the impact of energy efficiency on the electricity consumption of the Brazilian tertiary sector are still few. Recent official studies extrapolate energy conservation figures to provide estimations. In this work, a techno-economic bottom-up model is applied to simulate long-term electricity consumption of the Brazilian tertiary sector under four energy efficiency scenarios. The approach...
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