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Optimal alignments are the basis of conformance checking. For long, researchers have been devoted to the efficiency issue of computing optimal alignments. This paper focuses on the optimality issue. Specifically, we aim to find alignments with maximum synchronous moves and minimum deviations. This paper introduces a coordinate-plane search space, which allows enumerating all the possible alignments. The alignments with maximum synchronous moves are translated into the lowest-cost paths, such that heuristic strategies (such as the Dijkstra algorithm) can be applied. Both theoretical proof and experimental results show that 100% optimality can be achieved.

Foreground object segmentation is an important task which comes with various applications in outdoor surveillance and navigation. Most existing methods focus on the accuracy and therefore are computational expensive and low in speed, which makes them difficult to use for real applications. In this paper, we are aiming to tackle the accuracy and efficiency trade-off. In particular, different from existing methods that use finetuning routine on heavyweight pre-trained models and/or use optimization techniques to enhance results, we propose a lightweight end-to-end network that can be trained from scratch effectively and efficiently. First,long and short skip connections are used among convolutional blocks and within bottleneck block; by doing this, the information flow within the networks is enhanced in the training stage and thus usage of parameters in model is increased which allows more compact and efficient networks design. Second, we use element-wise summing based feature fusions before each up-sampling layer. This is aiming to reduce the size of decoder, accelerate up-sampling process and make training convergence more stable. Our proposed method is tested rigorously. Particularly, we achieved 1000 times higher speed compared to the state-of-the-art methods on CD2014 and SBI2015 datasets with a comparable accuracy.

This paper is a proposal for business simulation compiler. This compiler can utilize and modify a business simulation on the WWW easily for any person who is inexperienced in program development. This compiler was created as part of our business simulation course. The course we are developing consists of: (i) a simple gaming experiment among multiple students using Alexander Islands, a tiny business simulation on the WWW; (ii) lectures to let students understand the core concepts of systems management through the simulation; and (iii) the development of a homemade simulation model by the students themselves using business model description language (BMDL) and a business model development system (BMDS). Our course is unique in the sense that, through the business simulation, we aim to develop students' skills: (1) to implement their own specific models of business firms, (2) to develop information systems for the films, and (3) to understand business processes among companies. This paper describes the background and motivation, basic principles, the architecture and implementation of BMDL/BMDS, and the results of the experiments.

This paper deals with Kansei evaluation focusing on consumers' psychological needs and personal taste. To do so, a preparatory study is conducted beforehand to obtain Kansei data of the products to be evaluated, in which products are assessed according to Kansei attributes by means of the semantic differential method and linguistic variables. These Kansei data are then used to generate Kansei profiles for evaluated products by means of the voting statistics. As consumers' preferences on Kansei attributes of products vary from person to person and target-oriented decision analysis provides a good description of individual preference, the target-oriented decision analysis has been used and extended to quantify how well a product meets consumers' preferences. Due to the vagueness and uncertainty of consumers' preferences, three types of fuzzy targets are defined to represent the consumers' preferences. Considering the priority order of Kansei attributes specified by consumers, a so-called prioritized scoring aggregation operator is utilized to aggregate the partial degrees of satisfaction for the evaluated products. As the aesthetic aspect plays a crucial role in human choice of traditional crafts, an application to evaluate Kanazawa gold leaf, a traditional craft in Ishikawa, Japan, has also been provided to illustrate how the proposed model works in practice.

Recently, an attempt of integration between the theories of fuzzy sets and rough sets has resulted in providing a roughness measure for fuzzy sets [M. Banerjee, S.K. Pal, Roughness of a fuzzy set, Information Sciences 93 (1996) 235-246]. Essentially, Banerjee and Pal's roughness measure depends on parameters that are designed as thresholds of definiteness and possibility in membership of the objects to a fuzzy set. In this paper we first remark that this measure of roughness has several undesirable properties, and then propose a parameter-free roughness measure for fuzzy sets based on the notion of the mass assignment of a fuzzy set. Several interesting properties of this new measure are examined. Furthermore, we also discuss how the proposed approach is used to describe the rough approximation quality of a fuzzy classification.