Disaster early warning and relief: a human-centered approach. - In: Improving Disaster Resilience and Mitigation - IT Means and Tools, (2014), S. 135-151
Effective need collection is a major part of post-disaster assessment and recovery. A system for matching needs with available offers is an essential component for the recovery of communities in the aftermath of natural disasters. In the classic approach, the needs of disaster-stricken communities are collected by rescue personnel sampling a geographic grid and using emergency communication facilities. Subsequently, needs are matched to offers available at some central server and relief interventions are planned to deliver them. In this chapter, after an introduction to this approach, we explore the new notion of peer-to-peer community empowerment for some key activities of disaster management, including early warning systems, needs collection, and need-to-offer matching. Then, we present the design of a framework that leverages on the capacity for the communities to selforganize during crisis management, proposing advanced algorithms and techniques for need-offermatching. Our framework supports pre- and post-disaster use of social networks information and connectivity via an evolvable vocabulary, and supports metrics for resilience assessments and improvement.
http://dx.doi.org/10.1007/978-94-017-9136-6_9
Knowledge acquisition issues for intelligent route optimization by evolutionary computation. - In: IEEE Congress on Evolutionary Computation (CEC), 2014, ISBN 978-1-4799-1486-9, (2014), S. 3252-3257
The paper introduces a Knowledge Acquisition and Maintenance concept for a Case Based Approximation method to solve large scale Traveling Salesman Problems in a short time (around 3 seconds) with an error rate below 3 %. This method is based on the insight, that most solutions are very similar to solutions that have been created before. Thus, in many cases a solution can be derived from former solutions by (1) selecting a most similar TSP from a library of former TSP solutions, (2) removing the locations that are not part of the current TSP and (3) adding the missing locations of the current TSP by mutation, namely Nearest Insertion (NI). This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch.
http://dx.doi.org/10.1109/CEC.2014.6900415
A case based approach for an intelligent route optimization technology. - In: Proceedings and companion publication of the 2014 Genetic and Evolutionary Computation Conference, July 12 - 16, 2014, Vancouver, BC, Canada ; a recombination of the 23rd International Conference on Genetic Algorithms (ICGA) and the 19th Annual Genetic Programming Conference (GP) ; one conference - many mini-conferences ; [and co-located workshops proceedings], ISBN 978-1-4503-2663-6, (2014), S. 1069-1072
The paper introduces a Case Based Approximation method to solve large scale Traveling Salesman Problems in a short time with a low error rate. It is useful for domains with most solutions being similar to solutions that have been created. Thus, a solution can be derived by (1) selecting a most similar TSP from a library of former TSP solutions, (2) removing the locations that are not part of the current TSP and (3) adding the missing locations of the current TSP by mutation, namely Nearest Insertion (NI). This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch.
Optimizing university curricula through correlation analysis. - In: International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2013, ISBN 978-1-4799-3212-2, (2013), S. 324-329
In this paper, we introduce a refined Educational Data Mining approach, which refrains from explicit learner modeling along with an evaluation concept. We use a Data Mining technology, which models students' learning characteristics by considering real data instead of deriving their characteristics explicitly. It aims at mining course characteristics similarities of former students' study traces and utilizing them to optimize curricula of current students based to their performance traits revealed by their educational history. This refined technology generates suggestions of personalized curricula. The technology includes an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.). Finally, the paper shows some pre-validation results and approaches for a final validation.
http://dx.doi.org/10.1109/SITIS.2013.60
An adaptive system for optimal matches between human needs and offers. - In: International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2013, ISBN 978-1-4799-3212-2, (2013), S. 317-323
The paper presents a very general and many purpose technique to represent human needs and offers along with a technology to find optimal matches. Moreover, the system is able to learn from its use by collecting user feedback and changing its parameters accordingly. This way, the system adjusts itself to the human expectations and desires and even follows the trend of these desires and expectations.
http://dx.doi.org/10.1109/SITIS.2013.59
An approach to consider diversity issues from a semantic point of view. - In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, ISBN 978-1-4799-0650-5, (2013), S. 1696-1701
In this paper, we discuss a semantic and application-driven approach to estimate diversity respectively similarity in Genetic Algorithms (GA) based on a relative distance. This diversity metric can used to decide, whether or not a new individual meets a requested degree of diversity. Furthermore, the trade-off between several versions of the metric and their computational complexity is discussed. Finally, the application of this metric and a formerly developed Backtrack- and Restart GA to solve the Travelling Salesman Problem under certain real time requirements is introduced along with experimental evaluation.
https://doi.org/10.1109/SMC.2013.292
Context-assisted test cases reduction for cloud validation. - In: Modeling and Using Context, (2013), S. 288-301
Cloud computing is currently receiving much attention from the industry, government, and academia. It has changed the way computation is performed and how services are delivered to customers. Most importantly, cloud services change the way software is designed, how data is handled, and how testing is performed. In cloud computing, testing is delivered as a service (TaaS). For instance, case testing (one of the most common validation approaches) could be used. However, executing test cases on a cloud system could be expensive and time consuming. Therefore, test case reduction is performed to minimize the number of test cases to be executed on the system. In this paper, we introduce a validation method called Context-Assisted Test Case Reduction (CATCR) for systems that are deployed on the cloud. In CATCR, test cases are reduced based on the context of the validation process. The results of previous test cases are used to select test cases for the next iteration. The minimized set of test cases needs to have effective coverage of the system on the cloud. To evaluate CATCR, an experimental evaluation is performed through Amazon's Cloud and a Java validation tool. Experimental results are recorded and presented.
http://dx.doi.org/10.1007/978-3-642-40972-1_22
Adaptive kansei search method using user's subjective criterion deviation. - In: Intelligent computer vision and image processing, ISBN 978-1-4666-3906-5, (2013), S. 14-26
Structural diversity for genetic algorithms and its use for creating individuals. - In: IEEE Congress on Evolutionary Computation (CEC), 2013, ISBN 978-1-4799-0453-2, (2013), S. 783-788
The paper presents a structural representation of diversity (respectively similarity). This representation can be used to decide, whether or not a new individual meets a requested degree of diversity, but also to estimate and optimize a populations coverage of the solution space to avoid running into a local optimum and missing the global one. Moreover, it can also be constructively used for systematically creating new individuals, which (1) meet a certain diversity requirement, (2) additionally improve the coverage of the solution search space, and (3) have an optimal fitness value.
http://dx.doi.org/10.1109/CEC.2013.6557648
Curriculum optimization by correlation analysis and its validation. - In: Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data, (2013), S. 311-318
The paper introduces a refined Educational Data Mining approach, which refrains from explicit learner modeling along with an evaluation concept. The technology is a "lazy" Data Mining technology, which models students' learning characteristics by considering real data instead of deriving ("guessing") their characteristics explicitly. It aims at mining course characteristics similarities of former students' study traces and utilizing them to optimize curricula of current students based to their performance traits revealed by their educational history. This (compared to a former publication) refined technology generates suggestions of personalized curricula. The technology is supplemented by an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.). Finally, the paper derives some refinement ideas for the evaluation method.
http://dx.doi.org/10.1007/978-3-642-39146-0_28