On robust estimation of the location parameter

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Insurance with multiple insurers: A game-theoretic approach

https://cyberleninka.org/article/n/777209.pdf

Robust and Pareto optimality of insurance contracts

Minimizing total tardiness and earliness on unrelated parallel machines with controllable processing times

Scheduling of parallel machines to minimize total completion time subject to s-precedence constraints

Intra-group transfers are risk management tools that are usually widely used to optimise the risk position of an insurance group. In this paper, it is shown that premium and liability transfers could be optimally made in such a way as to reduce the amount of Technical Provisions and Minimum Capital Requirement for the entire insurance conglomerate. These levels of required capital represent the minimal amount that needs to be held by the insurance group without regulator intervention, according to the Solvency II regulation. We assume that only proportional risk transfers are feasible, since such transfers are not difficult to administer for a large scaled insurance group, as is always the case. In addition, any risk shifting should be made for commercial purposes in order to be considered acceptable by the local regulators that impose restrictions on how much the assets within an insurance group are fungible. Our numerical examples illustrate the efficiency of the optimal proportional risk transfers which can easily be implemented, in terms of computation, in any well-known solver even for an insurance conglomerate with many subsidiaries. We found that our proposed optimal proportional allocations are more beneficial for large insurance group, since the relative reduction in capital requirement tends to be small, whereas the gain in absolute terms is quite significant for large scaled insurance group.

Efficient Risk Allocation within a Non-Life Insurance Group Under Solvency II Regime

As cultural heritage domain enters the digital heritage era based on advanced digital technology, Augmented Reality(AR) technology has arisen as a new medium for enhancing user experience by augmenting additional information or content. Although there have been several AR applications in the cultural heritage domain, insufficient re-usability and interoperability of AR applications has inhibited the development of AR ecosystems. To address these issues, we propose the 5W1H-based metadata schema for context-aware AR applications in the cultural heritage domain, which consists of the PoI-AR Anchor-AR Contents relation based on Who, When, Where, What, Why, How and its whole metadata structure. We also propose a modified AR reference model to investigate the usefulness and effectiveness of the proposed schema. The 5W1H-based metadata schema provides a context-aware mediating platform that secures extensibility for further contextual information, and its whole metadata structure ensures the re-usability of the AR contents and interoperability of AR applications. The modified AR reference model describes the universal workflow and the generic framework for AR applications to encourage compatibility of following AR applications. Through these proposals, contextualized content retrieval according to user preference will be possible for AR applications in the cultural heritage domain.

Metadata schema for context-aware augmented reality applications in cultural heritage domain

Periodic scheduling has many attractions for wireless telecommunications. It offers energy saving where equipment can be turned off between transmissions, and high-quality reception through the elimination of jitter, caused by irregularity of reception. However, perfect periodic schedules, in which each (of $$n$$n) client is serviced at regular, prespecified intervals, are notoriously difficult to construct. The problem is known to be NP-hard even when service times are identical. This paper focuses on cases of up to three distinct periodicities, with unit service times. Our contribution is to derive a $$O(n^4)$$O(n4) test for the existence of a feasible schedule, and a method of constructing a feasible schedule if one exists, for the given combination of client periodicities. We also indicate why schedules with a higher number of periodicities are unlikely to be useful in practice. This methodology can be used to support perfect periodic scheduling in a wide range in real world settings, including machine maintenance service, wireless mesh networks and various other telecommunication networks transmitting packet size data.

/pdf/perfect-periodic-scheduling-for-three-basic-cycles-u9rknmgy6f.pdf

Eun-Seok Kim

Papers

Robust and Pareto optimality of insurance contracts

Scheduling of parallel machines to minimize total completion time subject to s-precedence constraints

Efficient Risk Allocation within a Non-Life Insurance Group Under Solvency II Regime

Metadata schema for context-aware augmented reality applications in cultural heritage domain

Perfect periodic scheduling for three basic cycles