Intelligent Personalized Searching

Search engine is a very useful tool for almost everyone nowadays. People use search engine for the purpose of searching about their personal finance, restaurants, electronic products, and travel information, to name a few. As helpful as search engines are in terms of providing information, they can also manipulate people behaviors because most people trust online information without a doubt. Furthermore, ordinary users usually only pay attention the highest-ranking pages from the search results. Knowing this predictable user behavior, search engine providers such as Google and Yahoo take advantage and use it as a tool for them to generate profit. Search engine providers are enterprise companies with the goal to generate profit, and an easy way for them to do so is by ranking up particular web pages to promote the product or services of their own or their paid customers. The results from search engine could be misleading. The goal of this project is to filter the bias from search results and provide best matches on behalf of users’ interest

An intelligent, free-flying robot

The ground based demonstration of the extensive extravehicular activity (EVA) Retriever, a voice-supervised, intelligent, free flying robot, is designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The major objective of the EVA Retriever Project is to design, develop, and evaluate an integrated robotic hardware and on-board software system which autonomously: (1) performs system activation and check-out; (2) searches for and acquires the target; (3) plans and executes a rendezvous while continuously tracking the target; (4) avoids stationary and moving obstacles; (5) reaches for and grapples the target; (6) returns to transfer the object; and (7) returns to base

Telepresence and the Role of the Senses

The telepresence experience can be evoked in a number of ways. A well-known example is a player of videogames who reports about a telepresence experience, a subjective experience of being in one place or environment, even when physically situated in another place. In this paper we set the phenomenon of telepresence into a theoretical framework. As people react subjectively to stimuli from telepresence, empirical studies can give more evidence about the phenomenon. Thus, our contribution is to bridge the theoretical with the empirical. We discuss theories of perception with an emphasis on Heidegger, Merleau-Ponty and Gibson, the role of the senses and the Spinozian belief procedure. The aim is to contribute to our understanding of this phenomenon. A telepresence-study that included the affordance concept is used to empirically study how players report sense-reactions to virtual sightseeing in two cities. We investigate and explore the interplay of the philosophical and the empirical. The findings indicate that it is not only the visual sense that plays a role in this experience, but all senses

Advancing Artificial Intelligence in Sensors, Signals, and Imaging Informatics.

ObjectiveTo identify research works that exemplify recent developments in the field of sensors, signals, and imaging informatics.MethodA broad literature search was conducted using PubMed and Web of Science, supplemented with individual papers that were nominated by section editors. A predefined query made from a combination of Medical Subject Heading (MeSH) terms and keywords were used to search both sources. Section editors then filtered the entire set of retrieved papers with each paper having been reviewed by two section editors. Papers were assessed on a three-point Likert scale by two section editors, rated from 0 (do not include) to 2 (should be included). Only papers with a combined score of 2 or above were considered.ResultsA search for papers was executed at the start of January 2019, resulting in a combined set of 1,459 records published in 2018 in 119 unique journals. Section editors jointly filtered the list of candidates down to 14 nominations. The 14 candidate best papers were then ranked by a group of eight external reviewers. Four papers, representing different international groups and journals, were selected as the best papers by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board.ConclusionsThe fields of sensors, signals, and imaging informatics have rapidly evolved with the application of novel artificial intelligence/machine learning techniques. Studies have been able to discover hidden patterns and integrate different types of data towards improving diagnostic accuracy and patient outcomes. However, the quality of papers varied widely without clear reporting standards for these types of models. Nevertheless, a number of papers have demonstrated useful techniques to improve the generalizability, interpretability, and reproducibility of increasingly sophisticated models

A macroscopic analytical model of collaboration in distributed robotic systems

In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased

Integrating explanation-based and empirical learning methods in OCCAM

This paper discusses an approach to integrating empirical and explanation based learning techniques. The paper focuses on OCCAM, a program that has the capability to acquire via empirical means the knowledge needed for analytical learning. Two examples of this capability are discussed:The ability to use empirical techniques to acquire a domain theory for explanation based learning.The ability to use empirical learning techniques to find common patterns for causal relationships. These patterns encode a theory of causality (i.e., a set of general principles for recognizing causal relationships). Once acquired, a theory of causality can facilitate later learning by focusing on hypotheses which are consistent with the theory

Holistic Measures for Evaluating Prediction Models in Smart Grids

The performance of prediction models is often based on "abstract metrics" that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging "big data" domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.Comment: 14 Pages, 8 figures, Accepted and to appear in IEEE Transactions on Knowledge and Data Engineering, 2014. Authors' final version. Copyright transferred to IEE