This paper focuses on care plans (i.e., rough schedules) in care houses and evaluates them from the viewpoint of a deep and stable sleep which contributes to provide comfortable and healthy life for aged persons. For this purpose, this paper investigates the care plans which are basically based on the current care plans but change a small part of a schedule as an aged person desires. Through the human subject experiments in the actual care house, the following implications have been revealed: (1) the proposed care plan decreases the time of the light sleep; and (2) the proposed care plan provides the deep sleep (i.e., 9 years younger sleep in our experiment).

Do quantified and origin) and molecular aspects of our bodies like DNA can tweeting, heavily monitored and selfreporting animals, converge. Consumer genomics websites, crowdsourcing of humans, environments and food create some new biodata but also social networking over genes, together uniformity, a dangerously homogenous, objectified and with services monitoring food flows and food authenticity standardized collective or these data offer some new can create new models of research in nutrigenomics and opportunity for interaction? Are we creating new symbiotic projects related to dieting, health and relations over these data that can lead to a new sense of lifestyle choices. How to connect various scales from community or we are witnessing some depersonalization molecules to institutions and what will be the function of and objectification? How to make meaning out of large these interactions and interfaces? How to create quantities of data and how to bring user experience to data meaningful interaction across scales and large datasets?

Brain structure exhibits systematic relationships with a variety of an individual’s cognitive abilities and such relationships can be captured by voxel-based morphometry (VBM) that computes regional gray matter volume based on anatomical MRIs. This method has been successfully used to reveal brain regions that are associated with individual differences in a broad range of contexts such as perceptual performance, attention control, face recognition, introspection and personality traits. Here, we show that such relationships with brain structure extend to complex social behaviors by presenting our recent VBM studies that examined the relationships between brain structure and diverse aspects of socio-cognitive behavioral traits. Specifically, we identified brain regions in which individual differences in gray matter volumes were associated with political orientation, moral sentiment, empathy and loneliness. These findings suggest that information derived from standard MRI scans could be used to extract information about an individual’s real-world and online social behavior. Unlike conventional functional neuroimaging research, our structural neuroimaging approach does not require a virtual environment that emulates social interactions and thus can directly link brain structure to real-world human behavior. As such, our approach based on individual differences in brain structure and behavior provides an important anchor point that integrates genetic and environmental factors determining diversity of human cognition and behavior.

Baseline and meditation data was obtained from 31 longterm meditation practitioners using the single-sensor right Over the past several years, a host of simple consumer prefrontal EEG system produced by Neurosky, Inc. Each electroencephalography (EEG) devices have been released subject was asked to complete a 5 minute resting period in at relatively inexpensive price points. These devices allow which they were asked to close their eyes and let their single or multi-channel recording of EEG, generally mind wander (without meditating). This was followed by employing user-friendly design, e.g.

In the current web, developers are able to create new applications by composing already existing services from third-party vendors. However, the vast amount of choices, technologies and repositories can make it a tedious task. This paper describes a semantic metadirectory of services that helps in the process of discovering services. We propose a semantic service discovery process and description of existing service repositories, such as Programmable Web and Yahoo Pipes, which are two service repositories which provide plenty of services that can be reused by developers to build new web applications. The challenges behind integrating these repositories involved the problems of defining a common model, identifying relevant data and integrating and ranking the extracted data.

Service-Oriented Computing (SOC) has received much interest due to its potential to tackle many adaptive system architecture issues that were previously hard to overcome by other computing paradigms. However, it has been facing great difficulty in quickly discovering and dynamically combing available Web services to satisfy given request on-demand. Most of the current researches concentrated o n the semantic model for service discovery, composition, and so on. But there are few studies concerned the intrinsic pattern and law of the service interactions and relationships. To achiev e the vision of SOC in heterogeneous and open environment, in our opinion, not only the semantics of individual Web service but also the interactions and relationships among Web services are needed to be considered seriously. In this paper, beginning with combining Semantic Web and social networking technology within SOC paradigm, we study associations between Web services, mine the relationships among services to design and build Service Network (SN), anal y z e the structural and social characteristics and complexity of SN to reveal the user interests, business requests, information and data flow and direction. In short, we would like to reassess and reconsider the SOC paradigm from the network perspective, through finding new knowledge to build new theoretical basis and approach which can be used to guide and promote the service discovery, composition, and so on, in SOC paradigm.

Nearly 20% of total energy consumption in the United States is accounted for in heating, ventilation, and air conditioning (HVAC) systems. Smart sensing and adaptive energy management agents can greatly decrease the energy usage of HVAC systems in many building applications, for example by enabling the operator to shut off HVAC to unoccupied rooms. We implement a multimodal sensor agent that is nonintrusive and low-cost, combining information such as motion detection, CO2 reading, sound level, ambient light,and door state sensing. We show that in our live test bed at the USC campus, these sensor agents can be used to accurately estimate the number of occupants in each room using machine learning techniques, and that these techniques can also be applied to predict future occupancy by creating agent models of the occupants. These predictions will be used by control agents to enable the HVAC system increase its efficiency by continuously adapting to occupancy forecasts of each room.

Illegal extraction of forest resources is fought, in many developing countries, by patrols through the forest that seek to deter such activity by decreasing its profitability. With limited resources for performing such patrols, a patrol strategy will seek to distribute the patrols throughout the forest, in space and time, in order to minimize the resulting amount of extraction that occurs or maximize the degree of forest protection, according to one of several potential metrics. We pose this problem as a Stackelberg game. We adopt and extend the simple, geometrically elegant model of (Albers 2010). First, we study optimal allocations of patrol density under generalizations of this model, relaxing several of its assumptions. Second, we pose the problem of generating actual schedules whose site visit frequencies are consistent with the analytically computed optimal patrol densities.

Stackelberg security games form the backbone of systems like ARMOR, IRIS and PROTECT, which are in regular use by the Los Angeles International Police, US Federal Air Marshal Service and the US Coast Guard respectively. An understanding of the runtime required by algorithms that power such systems is critical to furthering the application of game theory to other real-world domains. This paper identifies the concept of the deployment-to-saturation ratio in random Stackelberg security games, and shows that in a decision problem related to these games, the probability that a solution exists exhibits a phase transition as the ratio crosses 0.5. We demonstrate that this phase transition is invariant to changes both in the domain and the domain representation. Moreover, problem instances at this phase transition point are computationally harder than instances with other deployment-to-saturation ratios for a wide range of different equilibrium computation methods, including (i) previously published different MIP algorithms, and (ii) different underlying solvers and solution mechanisms. Our findings have at least two important implications. First, it is important for new algorithms to be evaluated on the hardest problem instances. We show that this has often not been done in the past, and introduce a publicly available benchmark suite to facilitate such comparisons. Second, we provide evidence that this phase transition region is also one where optimization would be of most benefit to security agencies, and thus requires significant attention from researchers in this area.

Computational social science, as with all complex adaptive systems sciences, involves a great amount of uncertainty on several fronts, including intrinsic arbitrariness such as that due to path dependence, disagreement on social theory and how to capture it in software, input data of different credibility that does not exactly match the requirements of software because it was gathered for another purpose, and inexactly matching translations between models that were designed for different purposes than the study at hand. This paper presents a method of formally tracking that uncertainty, keeping the data input parameters proportionate with logical and probabilistic constraints, and capturing proportionate dynamics of the output ordered by the decision points of policy change, for the purpose of risk-based analysis. Once ordered this way, the data can be compared to other data similarly expressed, whether that data is from simulation excursions or from the real world, for objective comparison and distance scoring at the level of dynamic patterns as opposed to single outcome validation. This method enables wargame adjudicators to be run out with data gleaned from the wargame, enables data to be repurposed for both training and testing set, and facilitates objective validation scoring through soft matching. Artificial intelligence tools used in the method include probabilistic ontologies with crisp and Bayesian inference, game trees that are multiplayer non-zero sum and decision point based rather than turn-based, and Markov processes to represent the dynamic data and align the models for objective comparison.