Seasonal dynamics and inter-annual variations of ecosystem productivity are closely linked to temperature variations in most terrestrial biomes, but soil moisture can also affect the regional carbon cycle in the water-limited regions. Studies using in-situ observation and remote sensing have been widely used to monitor the trends and variability of soil moisture (SM) and greenhouse gas (GHG) emissions. However, there are few studies on measuring the seasonal variations of SM, GHG (CO2, CH4, N2O) emissions, and water properties on a plot scale in hyper-arid areas. For the first time in the Arabian desert, we used portable devices to monitor temporal patterns of surface soil moisture (SFSM), soil GHG emissions, and water pH and temperatures in a dryland site surrounding an ephemeral water body for 9 months in 2023. The employed instruments include Delta-T WET-2-K4 sensor for SFSM, Orion Star A121 pH meter for water pH and temperatures, and a static chamber for GHG emissions. For monitoring multi-year SM over the study site, SMAP soil moisture data records were also used for 2016-2021.

Rivers contribute to carrying biogeochemical constituents from terrestrial to marine biospheres. Land-margin ecosystems in rivers are critical ecosystems that provide an interaction between land and ocean. The ecosystems have experienced increasing climate and environmental changes in recent years. The variations of the surface water at the river mouth areas are closely linked with the changes in biogeochemical cycles, including greenhouse gas emission. Multi-source environmental remote sensing data records were utilized to analyze the relationship between surface open water fraction (Fw) and various environmental and biogeochemical parameters, such as precipitation, surface soil moisture, root-zone soil moisture, heterotrophic respiration, and CH4. The study focused on 253 river mouth locations, which were determined using the MERIT-Hydro map. The long-term (2002-2021) Fw data shows a strong, negative trend in the mean annual summer (Jun – Aug) Fw in northern low latitudes (45°N). The results show that 46% of the river mouths has a decreasing Fw trend, implying drier surface moisture conditions. The correlation results show the stronger correspondence between Fw and carbon variables at the river mouth.

This paper introduces a coloring application for dual-sided transparent surfaces. It extends the traditional coloring experience with see-through display technology and dual-sided interaction techniques that enable a playful user experience. Twosome Coloring allows two users to interact with a see-through color picker and twosome interaction techniques on dual-sided transparent surfaces. We describe design concerns related to see-through technology, dual-sided interaction and application design, and present the results from a preliminary user study that indicates the benefit of see-through interface and twosome interactions.

Foot positions are features of how humans express their intentions during conversations. This inspired us to investigate the usage of foot expressions during interactions with computers. We conducted an observational study to determine how foot positions correlate with the intentions of users at public displays. Using our findings, we performed a case study to demonstrate the usefulness of adapting content according to the intentions detected based on foot positions. Our results are valuable for researchers designing context-aware public displays.

A video see-through head mounted display (HMD) has a different viewing point than does the real eye, resulting in visual displacement (VD). VD deteriorates visuomotor performance due to sensory conflict. Previous work has investigated this deterioration and human adaptation by comparing fixed VD and real eye conditions. In this study we go a step further to investigate whether any differences in visuomotor and adaptation trends exist across 16 distinct VD conditions. The performance tasks studied were of two types: foot placement and finger touch. In contrast to our initial prediction, the results showed equal task performance levels and adaptation within about 5 minutes regardless of VD conditions. We found that human adaptation covered a variety of VDs --- up to 55 mm in the X, Y direction; up to 125mm in the Z direction; and up to 140mm of interocular distance (IOD). In addition, we found that partial adaptation gave participants the interesting experience of a sense of body structure distortion for a few minutes.

This paper proposes a new algorithm to simulate fluid surface waves for real-time graphics applications. The external forces and object-interactions are described by a set of wave seeds. A wave seed is a physically-based wave model, which contains all information needed to present the waves. The wave seeds are stored into a texture, and the shader programs evaluate each wave seed for the sub-area of the height field affected by the wave seed. The evaluation results are accumulated into the height field, and finally the height field is rendered. The proposed algorithm implemented completely within GPU is quite fast, extremely memory-efficient, and is unconditionally stable.

The three-dimensional (3D) reconstruction of objects has been well studied in the literature of augmented reality (AR) [1, 2]. Most existing studies have assumed that the to-be-constructed target object is rigid, whereas objects in the real world can be dynamic or deformable. Therefore, AR systems are required to deal with non-rigid objects to be adaptive to environmental changes. In this paper, we address the problem of reconstructing articulated objects as a starting point for modeling deformable objects. An articulated object is composed of partially rigid components linked with joints. After building a mesh model of the object, the model is segmented into the components along their boundaries by a graph-cut-based approach that we propose.

This paper describes a new learning by observation algorithm based on Bayesian networks and game pattern graphs. Even with minimal knowledge of a game or human instructions, the robot can learn the game rules by watching human demonstrators repeatedly play the game multiple times. Based on the knowledge acquired from this learning process, represented in Bayesian networks and game pattern graphs, the robot can play games as robustly as humans do. Our learning algorithm for human-robot game interaction is implemented using a teddy bear-like robot and is demonstrated by application to well-known social games, specifically Rock-Paper-Scissors, Muk-Chi-ba and Blackjack.

In an environment where the contexts of users are complex and the degree of freedom of user activity is very high, such as in daily life, several factors need to be considered for constructing user models. Such a model should include changes in the meanings of activities that reflect the user's situation both temporally and individually. In this paper we propose a novel approach for personalizing the user model and adapting it to individual circumstances with a wearable sensor network. We also describe the process for determining the repetitive activities of a user by using incremental clustering and Bayesian network. We show experimental results for an adaptive user model based on a real wearable sensor platform. Multimedia data of user experience are acquired from the multimodal sensors, and processed to metadata that have meanings.

Most information systems share an architectural style that emphasizes flexibility and run-time adaptability. However, any knowledge or business rules related to the information are stored implicitly to the external programs such as database. It can be called "semantics" that they have a definition of a domain model and rules for its integrity and can be configured by domain experts external to the execution of the program. Therefore, heterogeneous database schemas which have different information or knowledge each other should be integrated to present relevant information consistently. In this paper a semantic approach for the specification and the management of databases with evolving schemata is introduced. It is shown how a general object-oriented model for schema versioning and evolution can be formalized. We show how the semantics of schema change operations can be defined, and how interesting reasoning tasks can be supported, based on an encoding in description logics.