Adaptation cost as a criterion for solution evaluation

1996 ◽  
pp. 354-361 ◽  
Author(s):  
Juho Rousu ◽  
Robert J. Aarts
Keyword(s):  
Adaptation Cost

scholarly journals Populating Web-Scale Knowledge Graphs Using Distantly Supervised Relation Extraction and Validation

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 316
Author(s):  
Sarthak Dash ◽  
Michael R. Glass ◽  
Alfio Gliozzo ◽  
Mustafa Canim ◽  
Gaetano Rossiello
Keyword(s):  
Deep Learning ◽  
Relation Extraction ◽  
Automated System ◽  
External Information ◽  
Learning Approach ◽  
Wide Margin ◽  
Structure Information ◽  
Adaptation Cost ◽  
Knowledge Graphs ◽  
Scale Experiment

In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. The designed system leverages a deep-learning-based technology for relation extraction that can be trained by a distantly supervised approach. In addition, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations. The designed system does not require any effort for adaptation to new languages and domains as it does not use any hand-labeled data, NLP analytics, and inference rules. Our experiments, performed on a popular academic benchmark, demonstrate that the suggested system boosts the performance of relation extraction by a wide margin, reporting error reductions of 50%, resulting in relative improvement of up to 100%. Furthermore, a web-scale experiment conducted to extend DBPedia with knowledge from Common Crawl shows that our system is not only scalable but also does not require any adaptation cost, while yielding a substantial accuracy gain.

FROM ADAPTATION-GUIDED RETRIEVAL TO REUSE-GUIDED RETRIEVAL: APPLICATION TO CASE RETRIEVAL NET MEMORY MODEL

2013 ◽  
Vol 12 (04) ◽  
pp. 757-787 ◽  
Author(s):  
NABILA NOUAOURIA ◽  
MOUNIR BOUKADOUM
Keyword(s):  
General Framework ◽  
Retrieval System ◽  
Similar Case ◽  
Memory Model ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
Case Retrieval ◽  
Full Cost ◽  
Adaptation Cost ◽  
Process Adaptation

In Case-Based Reasoning (CBR), case retrieval is generally guided by similarity. However, the most similar case may not be the easiest one to reuse (hard to adapt). As recommended by Smyth and Keane, it might be more efficient to use an adaptability criterion to guide the retrieval process (adaptation-guided retrieval or AGR). In the same trend but with the goal of optimizing case reuse, our approach is to consider what is similar to copy and what is different to adapt during the retrieval stage. We introduce a more general framework for retrieval, namely the reuse-guided retrieval (RGR). The goal of this paper is twofold: first, it proposes a case retrieval approach that relies on reuse cost; then, it illustrates its use by integrating adaptation cost into the case retrieval net (CRN) memory model, a similarity-based case retrieval system. The described retrieval framework optimizes case reuse early in the inference cycle, without incurring the full cost of an adaptation step. Our results on two case studies reveal that the proposed approach yields better recall quality than CRN's similarity only-based retrieval while having similar computational complexity.

scholarly journals Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1932
Author(s):  
Ramyar Saeedi ◽  
Keyvan Sasani ◽  
Assefaw H. Gebremedhin
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Mobile Health ◽  
Health Monitoring ◽  
Learning Models ◽  
Healthcare Applications ◽  
User Data ◽  
Adaptation Cost ◽  
Mobile Health Monitoring ◽  
Machine Learning Models

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.

scholarly journals Climate change adaptation cost and residual damage to global crop production

2020 ◽  
Vol 80 (3) ◽  
pp. 203-218
Author(s):  
T Iizumi ◽  
Z Shen ◽  
J Furuya ◽  
T Koizumi ◽  
G Furuhashi ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Change Adaptation ◽  
Crop Production ◽  
Crop Yields ◽  
Climate Mitigation ◽  
Cost Models ◽  
Global Cost ◽  
Adaptation Cost ◽  
Residual Damage ◽  
Adaptation Costs

Adaptation will be essential in many sectors, including agriculture, as a certain level of warming is anticipated even after substantial climate mitigation. However, global adaptation costs and adaptation limits in agriculture are understudied. Here, we estimate the global adaptation cost and residual damage (climate change impacts after adaptation) for maize, rice, wheat and soybean using a global gridded crop model and empirical production cost models. Producers require additional expenditures under climate change to produce the same crop yields that would be achieved without climate change, and this difference is defined as the adaptation cost. On a decadal mean basis, the undiscounted global cost of climate change (adaptation cost plus residual damage) for the crops are projected to increase with warming from 63 US$ billion (B) at 1.5°C to $80 B at 2°C and to $128 B at 3°C per year. The adaptation cost gradually increases in absolute terms, but the share decreases from 84% of the cost of climate change ($53 B) at 1.5°C to 76% ($61 B) at 2°C and to 61% ($8 B) at 3°C. The residual damage increases from 16% ($10 B) at 1.5°C to 24% ($19 B) at 2°C and to 39% ($50 B) at 3°C. Once maintaining yields becomes difficult due to the biological limits of crops or decreased profitability, producers can no longer bear adaptation costs, and residual damages increase. Our estimates offer a basis to identify the gap between global adaptation needs and the funds available for adaptation.

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