Volume 22, Issue 4

This article describes research to build an embodied conversational agent (ECA) as an interface to a question-and-answer (Q/A) system about a National Science Foundation (NSF) program. We call this ECA the LifeLike Avatar, and it can interact with its users in spoken natural language to answer general as well as specific questions about specific topics. In an idealized case, the LifeLike Avatar could conceivably provide a user with a level of interaction such that he or she would not be certain as to whether he or she is talking to the actual person via video teleconference. This could be considered a (vastly) extended version of the seminal Turing test. Although passing such a test is still far off, our work moves the science in that direction. The Uncanny Valley notwithstanding, applications of such lifelike interfaces could include those where specific instructors/caregivers could be represented as stand-ins for the actual person in situations where personal representation is important. Possible areas that come to mind that might benefit from these lifelike ECAs include health-care support for elderly/disabled patients in extended home care, education/training, and knowledge preservation. Another more personal application would be to posthumously preserve elements of the persona of a loved one by family members. We apply this approach to a Q/A system for knowledge preservation and dissemination, where the specific individual who had this knowledge was to retire from the US National Science Foundation. The system is described in detail, and evaluations were performed to determine how well the system was perceived by users.

This article describes a method of automatically detecting, counting and classifying logs on a timber truck using a photograph (taken by the driver). An image-processing algorithm is developed to process the photograph to calculate an estimate of the number of logs present and their respective diameters. The algorithm uses color information in multiple color spaces as well as geometrical operators to segment the image and extract the relevant information. This information enables the sawmill to better plan internal logistics and production in advance of the truck’s arrival time. The algorithm is robust with respect to external factors such as varying lighting conditions and camera angle, but some inaccuracies remain, mainly caused by logs being occluded or covered in mud or snow.

Power provision is coming to be the most important constraint to data center development. The efficient management of power consumption according to the loads of the data center is urgent. As the load for every application hosted in every server node (SN) of the data center and corresponding Service Level Agreement (SLA) requirements can be quite different, it is hard to deploy a power strategy at application. The asynchronies and abruptness characteristics of workload fluctuation make power management policymaking using periodic resource scheduling method invalid. In this article, the design and implementation of the request–response distributed power management scheme is elaborated. Bound by linear time complexity, the method proposed integrates dynamic voltage/frequency scaling, power-on–power-off, and virtual machine migration mechanisms and dynamically optimizes the power consumption of a cloud data center. The significant advantage of the scheme is that it does not need synchronous scheduling between all SNs. Simulation results showed that the scheme could effectively decrease the power consumption of the data center, with a tiny reduction in performance as centralized control methods.

We propose a fast and reliable corner detector that can detect corners under non-uniform illumination and fuzzy mineshaft images effectively. First, we presented an inner mask that used only four pixels to determine the flat and corner regions of an image, which could eliminate unnecessary computation of flat regions, thus reducing computing cost. Second, we separated the corner regions into background and foreground and computed the separate corner threshold to settle non-uniform illumination. Third, we proposed a fast corner-detection algorithm to compute the nucleus continuous contributive segment based on the corner state. Finally, we proposed two effective methods to remove the false corners. Experimental results showed that our approach has a better detection quality and is less time consuming than three other algorithms on an artificial image, a noisy image, and non-uniform images and could meet the real-time requirement of mineshaft applications.

Online social network services have brought a kind of new lifestyle to the world that is parallel to people’s daily offline activities. Social network analysis provides a useful perspective on a range of social computing applications. Social interaction on the Web includes both positive and negative relationships, which is certainly important to social networks. The authors of this article found that the accuracy of the signs of links in the underlying social networks can be predicted. The trust that other users impart on a node is an important attribute of networks. In this article, the authors present a model to compute the prestige of nodes in a trust-based network. The model is based on the idea that trustworthy nodes weigh more. To fulfill this task, the authors first attempt to infer the attitude of one user toward another by predicting signed edges in networks. Then, the authors propose an algorithm to compute the prestige and trustworthiness where the edge weight denotes the trust score. To prove the algorithm’s effectiveness, the authors conducted experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.

Flow prediction in a vegetated channel has been extensively studied in the past few decades. A number of equations that essentially differ from each other in derivation and form have been developed. Because the process is extremely complex, getting the deterministic or analytical form of the process phenomena is too difficult. Hybrid neural network model (combining particle swarm optimization with neural network) is particularly useful in modeling processes where an adequate knowledge of the physics is limited. This hybrid model is presented here as a complementary tool to model channel flow–vegetation interactions in submerged vegetation conditions. The hybrid model is used to overcome the local minima limitations of a feed-forward neural network. The prediction capability of model has been found to be better than past empirical predictors. The model developed herein showed significantly better results in several model performance criteria compared with empirical models.

This article presents the design and development of a genetic algorithm (GA) to generate long-range transmit codes with low autocorrelation side lobes for radar to minimize target profile estimation error. The GA described in this work has a parallel processing design and has been used to generate codes with multiple constellations for various code lengths with low estimated error of a radar target profile.