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Members of the brackish-water species Lamprothamnium succinctum (Charales, Charophyceae) are widely distributed from tropical to temperate regions, including East Asia. In Japan, L. succinctum is listed as an endangered species and is protected by the government, because it was recorded only at two localities, Lake Hachiro-gata (Akita Prefecture) and Oo-ike pond (Deba-jima Island, Tokushima prefecture), and has become extinct in the former. In this study, we identified five new localities of this species in Japan. The morphological characteristics of their thalli agreed with those provided in the original description of this species, with distinctive reproductive characteristics. Moreover, the oospores of Japanese specimens of L. succinctum were examined for the first time using scanning electron microscopy. The oospores of Japanese specimens exhibited granulate fossa wall patterns, which were consistent with those described in previous studies. Our genetic analyses based on the DNA sequences of two chloroplast DNA markers, including both the coding and non-coding regions, revealed that the sample from Oo-ike pond is distinguishable from those from other Japanese specimens, although they are genetically very similar.

In marker-based augmented reality (AR) systems, markers are usually relatively independent and predefined by the system creator in advance. Users can only use these predefined markers to complete the construction of certain specified content. Such systems usually lack flexibility and cannot allow users to create content freely. In this paper, we propose a virtual marker technique to build a marker-based AR system framework, where multiple AR markers including virtual and physical markers work together. Information from multiple markers can be merged, and virtual markers are used to provide user-defined information. We conducted a pilot study to understand the multi-marker cooperation framework based on virtual markers. The pilot study shows that the virtual marker technique will not significantly increase the user’s time and operational burdens, while actively improving the user’s cognitive experience.

A loyalty program is an important link between consumers and merchants in daily consumption. While new technologies (e.g. gamification, social networks, augmented reality, and so on) make it possible to strengthen this bond, their potential has not yet been fully exploited. In our research, we explore a novel approach to integrate gamification and social interaction into a loyalty program based on augmented reality (AR). We propose an AR-based gamified point system which supports users in obtaining pet-based dynamic feedback on mobile devices and provides a multi-user environment for social interaction. Compared to traditional point systems, pet-based designs help to establish an emotional connection between users and the system. The multi-user environment is designed to increase the user’s motivation through the positive effects of social interactions. Our system provides interpersonal communication channels between users, including competitive and non-competitive interactions. We performed an evaluation consisting of two experiments to examine the effects of game elements (mission and feedback) and social cues (competitive and non-competitive interactions). In the first experiment, we analyze the change in online shopping behavior before and after adding game elements. The results show that gamification can increase user participation in online shopping. In the second experiment, we study the effects of social cues. The results show that social cues can motivate users to participate in the use of a gamified point system.

Biometric authentication is popular in authentication systems, and gesture as a carrier of behavior characteristics has the advantages of being difficult to imitate and containing abundant information. This research aims to use three-dimensional (3D) depth information of gesture movement to perform authentication with less user effort. We propose an approach based on depth cameras, which satisfies three requirements: Can authenticate from a single, customized gesture; achieves high accuracy without an excessive number of gestures for training; and continues learning the gesture during use of the system. To satisfy these requirements respectively: We use a sparse autoencoder to memorize the single gesture; we employ data augmentation technology to solve the problem of insufficient data; and we use incremental learning technology for allowing the system to memorize the gesture incrementally over time. An experiment has been performed on different gestures in different user situations that demonstrates the accuracy of one-class classification (OCC), and proves the effectiveness and reliability of the approach. Gesture authentication based on 3D depth cameras could be achieved with reduced user effort.

Requirements specification is one of the most important phases in developing a software application. In defining the behavior of a system, requirements specifications make use of a number of scenarios that are interrelated in many ways. Most of the current approaches, even though giving directions on how to translate them into state machines, treat each scenario separately. Because different relationships between scenarios result in different state machines, we believe it is significant to emphasize and represent these relationships. In order to illustrate them we propose a new type of diagrams named dependency diagrams. We offer a set of rules and steps for the synthesis of state machines from multiple inter-related scenarios, based on the initial scenarios and on the newly introduced dependency diagrams, as a means to describe the requirements specifications and to offer support during the design and implementation phases of developing a system.

The sea slug Plakobranchus ocellatus (Sacoglossa, Gastropoda) retains photosynthetically active chloroplasts from ingested algae (functional kleptoplasts) in the epithelial cells of its digestive gland for up to 10 months. While its feeding behavior has not been observed in natural habitats, two hypotheses have been proposed: 1) adult P. ocellatus uses kleptoplasts to obtain photosynthates and nutritionally behaves as a photoautotroph without replenishing the kleptoplasts; or 2) it behaves as a mixotroph (photoautotroph and herbivorous consumer) and replenishes kleptoplasts continually or periodically. To address the question of which hypothesis is more likely, we examined the source algae for kleptoplasts and temporal changes in kleptoplast composition and nutritional contribution. By characterizing the temporal diversity of P. ocellatus kleptoplasts using rbcL sequences, we found that P. ocellatus harvests kleptoplasts from at least 8 different siphonous green algal species, that kleptoplasts from more than one species are present in each individual sea slug, and that the kleptoplast composition differs temporally. These results suggest that wild P. ocellatus often feed on multiple species of siphonous algae from which they continually obtain fresh chloroplasts. By estimating the trophic position of wild and starved P. ocellatus using the stable nitrogen isotopic composition of amino acids, we showed that despite the abundance of kleptoplasts, their photosynthates do not contribute greatly to the nutrition of wild P. ocellatus, but that kleptoplast photosynthates form a significant source of nutrition for starved sea slugs. The herbivorous nature of wild P. ocellatus is consistent with insights from molecular analyses indicating that kleptoplasts are frequently replenished from ingested algae, leading to the conclusion that natural populations of P. ocellatus do not rely on photosynthesis but mainly on the digestion of ingested algae.