Rare category detecion (RCD) aims to discover rare categories in a massive unlabeled data set with the help of a labeling oracle. A challenging task in RCD is to discover rare categories which are concealed by numerous data examples from major categories. Only a few algorithms have been proposed for this issue, most of which are on quadratic or cubic time complexity. In this paper, we propose a novel tree-based algorithm known as RCD-Forest with $$O(\varphi n \log {(n/s)})$$ time complexity and high query efficiency where n is the size of the unlabeled data set. Experimental results on both synthetic and real data sets verify the effectiveness and efficiency of our method.

Mining arbitrary shaped clusters in large data sets is an open challenge in data mining. Various approaches to this problem have been proposed with high time complexity. To save computational cost, some algorithms try to shrink a data set size to a smaller amount of representative data examples. However, their user-defined shrinking ratios may significantly affect the clustering performance. In this paper, we present CLASP an effective and efficient algorithm for mining arbitrary shaped clusters. It automatically shrinks the size of a data set while effectively preserving the shape information of clusters in the data set with representative data examples. Then, it adjusts the positions of these representative data examples to enhance their intrinsic relationship and make the cluster structures more clear and distinct for clustering. Finally, it performs agglomerative clustering to identify the cluster structures with the help of a mutual k-nearest neighbors-based similarity metric called Pk. Extensive experiments on both synthetic and real data sets are conducted, and the results verify the effectiveness and efficiency of our approach.

Data sets collected from crowdsourcing platforms are well known for their cheap costs. But cheap costs may lead to low quality, i.e., labels may be incorrect or missing. Most of the existing work focuses on modeling the labeling errors of crowd workers, but missing labels can also cause problems when modeling the data. In this paper, we present an algorithm to predict the missing labels of crowd workers, in which we adopt thoughts from semi-supervised learning and utilize the particular consistency between crowd workers. We also define the consistency between workers by crowd labels and develop an algorithm to learn them from the data automatically. Experiments on both benchmark and real data show that our algorithm outperforms traditional semi-supervised learning algorithms in predicting missing labels, and the recovered crowd labels are capable of predicting the ground truth and reflecting real properties of crowd workers.

Virtualization technology enables various application services to be distributed and encapsulated within virtual machines (VMs), which are dynamically allocated to physical machines (PMs) in cloud computing environments. However, in many existing virtualized systems, the limited network bandwidth often becomes a bottleneck resource, leading to the intensification of network competition and the performance degradation for communication or data intensive applications. Aiming at reducing communication overheads and improving the application performance, in this paper, we propose an Affinity-Aware Grouping method for Allocation of VMs (AAGA). Firstly, we identity and model the problem of affinity-aware grouping-based allocation for virtual machines, and propose a detailed grouping method based on which a heuristic bin packing algorithm is used to deploy VM groups into PMs. In order to demonstrate the effectiveness of AAGA, we create multiple real virtual clusters (multi-VCs) with 56 VMs running multi-VM applications and compare application performance with Non-Affinity-aware Grouping-based Allocation methods (NAGA). Experimental results show that AAGA achieves better performance than NAGA.

Commodity information such as prices and public reviews is always the concern of consumers. Helping them conveniently acquire these information as an instant reference is often of practical significance for their purchase activities. Nowadays, Web 2.0, linked data clouds, and the pervasiveness of smart hand held devices have created opportunities for this demand, i.e., users could just snap a photo of any commodity that is of interest at anytime and anywhere, and retrieve the relevant information via their Internet-linked mobile devices. Nonetheless, compared with the traditional keyword-based information retrieval, extracting the hidden information related to the commodities in photos is a much more complicated and challenging task, involving techniques such as pattern recognition, knowledge base construction, semantic comprehension, and statistic deduction. In this paper, we propose a framework to address this issue by leveraging on various techniques, and evaluate the effectiveness and efficiency of this framework with experiments on a prototype.

Browse with either web directories or social bookmarks is an important complementation to search by keywords in web information retrieval. To improve users' browse experiences and facilitate the web directory construction, in this paper, we propose a novel browse system called Social Web Directory (SWD for short) by integrating web directories and social bookmarks. In SWD, (1) web pages are automatically categorized to a hierarchical structure to be retrieved efficiently, and (2) the popular web pages, hottest tags, and expert users in each category are ranked to help users find information more conveniently. Extensive experimental results demonstrate the effectiveness of our SWD system.

Various solutions have been proposed to enable mobile users to access location-based services while preserving their location privacy. Some of these solutions are based on a centralized architecture with the participation of a trustworthy third party, whereas some other approaches are based on a mobile peer-to-peer (P2P) architecture. The former approaches suffer from the scalability problem when networks grow large, while the latter have to endure either low anonymization success rates or high communication overheads. To address these issues, this paper deals with an enhanced dual-active spatial cloaking algorithm (EDA) for preserving location privacy in mobile P2P networks. The proposed EDA allows mobile users to collect and actively disseminate their location information to other users. Moreover, to deal with the challenging characteristics of mobile P2P networks, e.g., constrained network resources and user mobility, EDA enables users (1) to perform a negotiation process to minimize the number of duplicate locations to be shared so as to significantly reduce the communication overhead among users, (2) to predict user locations based on the latest available information so as to eliminate the inaccuracy problem introduced by using some out-of-date locations, and (3) to use a latest-record-highest-priority (LRHP) strategy to reduce the probability of broadcasting fewer useful locations. Extensive simulations are conducted for a range of P2P network scenarios to evaluate the performance of EDA in comparison with the existing solutions. Experimental results demonstrate that the proposed EDA can improve the performance in terms of anonymity and service time with minimized communication overhead.

As compared with text spam, the image spam is a variant which is invented to escape from traditional text-based spam classification and filtering. Various approaches to image spam filtering have been proposed with respective advantages and drawbacks in terms of time cost and efficiency. In this paper, we propose a new approach based on Base64 encoding of image files and n-gram technique for feature extraction. By transforming normal images into Base64 presentation, we try to extract features of an image with n-gram technique. With these features we train an SVM (support vector machine) which shows effectiveness and efficiency in detecting spam images from legitimate images. With an online shared personal corpus of images as the input, experimental results show that our approach, in comparison with some of the existing methods of feature extraction, can achieve very high performance for image spam classification in terms of some basic measures such as accuracy, precision, and recall. Moreover, our approach shows its practicability by taking less running time for image spam classification in comparison to other methods.

Autonomous moving objects (AMOs), such as automated guided vehicles (AGVs) and autonomous robots, have widely been used in the industry for decades. In an intelligent transport system with a great number of AMOs involved, it is important to eliminate potential congestion and deadlocks among AMOs to maintain a well-organized traffic flow. In this paper, we propose an algorithm that adapts bitonic merge sort algorithm for concurrent scheduling and routing of a great number (i.e., 4n2) of AMOs on an ntimesn mesh topology of path network without congestion or deadlocks among AMOs during their moves. The results are tested by experiments with randomly generated data and the comparison of a related model.