As a common technology for fault tolerance and load balance, rollback-recovery faces the challenges of scalability and inherent variability in those long-running large-scale applications with grids as the computing infrastructure. Among the rollback recovery schemes, pessimistic message logging protocols (PMLPs) and coordinated checkpointing protocols (CCPs) are the most popular in practice. Although PMLPs are good in scalability, their fault-free overhead sometimes is prohibitive. CCPs introduce relatively lower overhead, but they are poor in scalability. This work employs partition strategy and introduces the concept of pessimism grain to rollback recovery, striking a balance between good scalability and acceptable overhead. For a partitioned system, a coarse-grained pessimistic message-logging protocol is proposed to achieve scalability and asynchrony both in fault-free execution and in fault recovery. The impact of pessimism grain on the performance is evaluated theoretically. Experimental results show that the pessimism grain is one of the key configuration parameters to reach a desired performance level.
In memory-based collaborative filtering, the existing methods conduct a prediction based on the overall consistency of two users or items. The major problem with these methods is that it is hard to find users/items that are overall consistent with the test user/item in the system. In addition, these methods are sometimes being over optimistic, and disregard some useful information in user profiles in making a prediction. This paper exposes the drawbacks in these methods and proposes an inference-based recommendation scheme to overcome those drawbacks. This model is based on the fact that any two users may have common interest genres as well as different ones, with the capability of making full use of rating information to capture accurately the relevance between item and user. Experimental results from two popular public datasets, EachMovie and Movielens, show that our approach improves significantly the prediction accuracy.
In this study, we propose a recommender system for e-learning by utilizing a hybrid feedback method that extracts a user's preference and Web-browsing behavior. This system is capable of recommending learning content of potential interest to a user and also the likely Web-browsing action on the current item using a novel similarity measure approach. The recommender is adaptive to individual user's preference as well as one's changing interest in Web-based learning activity. A proof-of-concept system has been designed and is being implemented. Experiments are being formulated to illustrate the system's capability to acquire knowledge from user feedback and Web-browsing behavior, and to provide personalized recommendation adaptively in an e-learning environment.
Developing image classification modules in embedded systems is a complex task due to the limited resources available. In this brief, a multi-class image classifier using HOG feature extractor and SVM classifier is proposed for binary images. The novelty of the proposed system is applying two steps of binarization to the HOG technique to improve processing speed and area efficiency. First, HOG features are extracted from binary images to simplify the feature extraction process. Second, block normalization of the HOG is replaced with binarization to reduce hardware resource utilization. Compared to a similar existing work, our system speeds up the classification process while utilizing fewer hardware resources, with an 11.4% higher classification accuracy using the same setting.
In this paper, we present a mobile learning tool for ecosystem study using an augmented reality user interface. Our system provides a recommender function of learning content according to a learner's interest, which is extracted in the form of a feature term set that is based on the user's browsing behavior of learning content and its related objects, which are displayed through the augmented reality user interface. Using a prototype, we confirmed that our system can properly personalize the recommended results for ecosystem-related learning content, and we have the promising vision that our system can be utilized in a practical way for mobile learning in fieldwork for ecosystem studies.
Wide-area systems are becoming a popular infrastructure for long-running applications. Rollbackrecovery, as a common technology for fault tolerance and load balance, must meet the challenges of scalability and inherent variability in such applications. Most of the rollback-recovery protocols, however, are poor in scalability. Although pessimistic message logging protocols have no such problem, their fault-free overhead sometimes is prohibitive. Aiming at good scalability and acceptable overhead, this paper introduces the concept of pessimism grain and presents a coarse-grained pessimistic message-logging scheme. The paper also evaluates the impact of pessimism grain on the performance of the recovery scheme. Experimental results show that pessimism grain is one of the key configuration parameters to reach a desired performance level. In practice, the proper pessimism grain should be selected based on the characteristics of the applications.
For many users with a disability it can be difficult or impossible to use a computer mouse to navigate the web. An alternative way to select elements on a web page is the label typing approach, in which users select elements by typing part of the label. In most cases, these labels are specified by the page authors, but some selectable elements do not have an obvious textual description, thus requiring that a label be generated. The set of element labels on a web page must be both efficient to select by text input and meaningful to the user. This paper discusses our approach to this problem, using page structural analysis and user history to determine important elements of a page, and then matching this information with the efficiency model of the input device.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)