Data Envelopment Analysis (DEA) method is a linear programming approach that has been widely used as a framework for evaluating efficiency and measurement. DEA decision making is often faced with situations where the ...
Data Envelopment Analysis (DEA) method is a linear programming approach that has been widely used as a framework for evaluating efficiency and measurement. DEA decision making is often faced with situations where the existing DMU has input and output that contains fuzzy and hesitant elements so that it is difficult to make efficiency measurements. This study will design a DEA model in the state of inputs and outputs that contain hesitant elements by using polyhedral uncertainty sets. The results of this study are expected to produce a benchmarking model with DEA that can overcome the hesitant element and have the advantage of using a polyhedral uncertainty set that can measure the linearity of the nominal problem.
For a collaborative assistive device, human intent recognition (IR) is one of the first and foremost requirements. Formalizing the complex process of human IR in a compact yet expressive mathematical model holds promi...
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For a collaborative assistive device, human intent recognition (IR) is one of the first and foremost requirements. Formalizing the complex process of human IR in a compact yet expressive mathematical model holds promise. We put forward a Hierarchical Finite State Machine (H-FSM) for human IR within a generalized framework for collaborative assistive devices. Visual and contextual observations drive the H-FSM to different levels of granularity.
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrot...
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Sharing data among applications is a growing phenomenon. With the IoT, this phenomenon becomes more significant. As already studied in social networks, data sharing has the drawback of privacy risks. Authorization pro...
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Sharing data among applications is a growing phenomenon. With the IoT, this phenomenon becomes more significant. As already studied in social networks, data sharing has the drawback of privacy risks. Authorization protocols and cryptographic systems may not be enough to ensure that user data and metadata are not used for non-legitimate purposes. There are different scenarios and several personal data management proposals aimed to improve privacy protection. However, a risk that is always present is related to the possibility of processing and aggregating public and authorized data to infer sensitive information and data that the user may not want to share. These approaches, often called inference attacks, concern the disclosure of personal user data and have been widely studied in social networks. In this paper we describe the problem and some techniques to face it, showing its relevance in the IoT. Then we present the concept of an Adaptive Inference Discovery Service AID-S, conceived as a service that may support users to prevent this kind of information leakage and that can be integrated into personal data managers.
Alaska's Kenai fisheries are managed for maximum efficiency while ensuring fair allocation across multiple stakeholder groups and long term sustainability of the fishery. Managers must work to meet the fishery'...
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ISBN:
(纸本)9781509064298;9780692946909
Alaska's Kenai fisheries are managed for maximum efficiency while ensuring fair allocation across multiple stakeholder groups and long term sustainability of the fishery. Managers must work to meet the fishery's goals while adapting their commercial and subsistence eco-service management practices to meet changing ecological conditions. Pre- and in-season fishery management practices are supported by historical and location-based management knowledge. Unexpected changes to environmental conditions may be outside the adaptive capacity of the management. Furthermore, managers currently lack a tool to test how proposed policy will alter fishery outcomes. We present an agent-based simulation framework designed for the Kenai case-study that incorporates stakeholder harvest and effort data, as well as Oncorhynchus nerka and Oncorhynchus tshawytscha salmon run-timing dynamics for the last 35 years. The resulting high-fidelity and verified model was used for scenario-based studies to understand how shifting ecological and social drivers effect the seasonal outcomes of fishery dynamics. Scenarios were determined from plausible future changes to system drivers identified during participatory stakeholder meetings and in scientific literature from southeast Alaska. Using our model, we uncovered recent instability in the Kenai fisheries coupled socio-ecological system dynamics, and determined equations that describe the strategy of fishery managers in responding to in-season drivers. Sensitivity of seasonal fishery outcomes to different drivers was quantified via the scenario-based studies. Our results show our agent-based simulation framework to be a capable decision support tool, one that resource managers might use for testing outcomes of proposed policy change in response to environmental change.
We consider the problem of sequentially making decisions that are rewarded by "successes" and "failures" which can be predicted through an unknown relationship that depends on a partially controlla...
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InAs/InAs1-xSbx superlattice system differ distinctly from two well-studied superlattice systems GaAs/AlAs and InAs/GaSb, in terms of electronic band alignment, common-element at the interface, and phonon spectrum ove...
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This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019
This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019
In the context of condition-based maintenance (CBM) for railway transportation systems, the correlation of data coming from several heterogeneous sensors is gaining growing research interest: for this purpose, it is e...
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Home service robot works in unstructured environments with various tasks, where a low-cost, dexterous, and intrinsically safe manipulator is important. Traditionally, a redundant manipulator with high degrees of freed...
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