There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Matrix Factorization Techniques for Recommender Systems

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A Break in the Clouds: Towards a Cloud Definition

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change. Application of this technology relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as "Virtual Network Embedding (VNE)" algorithms. This paper presents a survey of current research in the VNE area. Based upon a novel classification scheme for VNE algorithms a taxonomy of current approaches to the VNE problem is provided and opportunities for further research are discussed.

Virtual Network Embedding: A Survey

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.This article1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

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Search-based software engineering: Trends, techniques and applications

Rapid growth in social networks (SNs) presents a unique scalability challenge for SN operators because of the massive amounts of data distribution among large number of concurrent online users. A request from any user may trigger hundreds of server activities to generate a customized page and which has already become a huge burden. Based on the theoretical model and analytical study considering realistic network scenarios, this article proposes a hybrid P2P-based architecture called PAIDD. PAIDD fulfills effective data distribution primarily through P2P connectivity and social graph among users but with the help of central servers. To increase system efficiency, PAIDD performs optimized content prefetching based on social interactions among users. PAIDD chooses interaction as the criteria because user’s interaction graph is measured to be much smaller than the social graph. Our experiments confirm that PAIDD ensures satisfactory user experience without incurring extensive overhead on clients’ network. More importantly, PAIDD can effectively achieve one order of magnitude of load reduction at central servers.

PAIDD：a hybrid P2P-based architecture for improving data distribution in social networks

Location-aware publish/subscribe is an important location-based service based on server-initiated model. Often times, the owner of massive spatio-textual messages and subscriptions outsources its location-aware publish/subscribe services to a third-party service provider, for example, cloud service provider, who is responsible for delivering messages to their relevant subscribers. The issue arising here is that the messages delivered by the service provider might be tailored for profit purposes, intentionally or not. Therefore, it is essential to develop mechanisms which allow subscribers to verify the correctness of the messages delivered by the service provider. In this paper, we study the problem of authenticating messages in outsourced location-aware publish/subscribe services. We propose an authenticated framework which not only can deliver the messages efficiently but also can make the subscribers’ authentication available with low cost. Extensive experiments on a real-world dataset demonstrate the effectiveness and efficiency of our proposed authenticated framework.

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Authenticated Location-Aware Publish/Subscribe Services in Untrusted Outsourced Environments

A spatio-textual similarity join searches a spatiotextual data collection and reports the object pairs that satisfy the specified spatial distance threshold and textual similarity threshold. However, when the data owner outsources the join computations to a third-party cloud service provider, the service provider may send incomplete or incorrect join results to the data owner. In this paper, we propose a pairwise authentication scheme, a cluster based scheme and an order and bound based scheme to authenticate the results of spatiotextual similarity joins. Extensive experiments on a real-world dataset verify the effectiveness and efficiency of our proposed schemes in terms of various performance metrics.

Authenticated Spatio-Textual Similarity Joins in Untrusted Cloud Environments

Supervised topic classifiers for Twitter and other media sources are important in a variety of long-term topic tracking tasks. Unfortunately, over long periods of time, features that are predictive during the training period may prove ephemeral and fail to generalize to prediction at future times. For example, if we trained a classifier to identify tweets concerning the topic of “Celebrity Death”, individual celebrity names and terms associated with these celebrities such as “Nelson Mandela” or “South Africa” would prove to be temporally unstable since they would not generalize over long periods of time; in contrast, terms like “RIP” (rest in peace) would prove to be temporally stable predictors of this topic over long periods of time. In this paper, we aim to design supervised learning methods for Twitter topic classifiers that are capable of automatically downweighting temporally unstable features to improve future generalization. To do this, we first begin with an oracular approach that chooses temporally stable features based on knowledge of both train and test data labels. We then search for feature metrics evaluated on only the training data that are capable of recovering the temporally stable features identified by our oracular definition. We next embed the top-performing metric as a temporal stability regularizer in logistic regression with the important property that the overall training objective retains convexity, hence enabling a globally optimal solution. Finally, we train our topic classifiers on 6 Twitter topics over roughly one year of data and evaluate on the following year of data, showing that logistic regression with our temporal stability regularizer generally outperforms logistic regression without such regularization across the full precision-recall continuum. Overall, these results establish a novel regularizer for training longterm temporally stable topic classifiers for Twitter and beyond.

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A novel regularizer for temporally stable learning with an application to Twitter topic classification

As a mature distributed object computing middleware, CORBA is being used more and more widely in many fields to build large scale distributed software system. In telecommunication system, load balance and fault tolerance are especially important because of strict requirements for high reliability and capacity. In former studies, load balance and fault tolerance are considered separately more often. This paper introduces a new CORBA design pattern, named GenericFactory pattern, and discusses how to combine load disturbance and fault tolerance effectively. Advantages of this pattern are pointed out.

Sen Su

Papers

PAIDD：a hybrid P2P-based architecture for improving data distribution in social networks

Authenticated Location-Aware Publish/Subscribe Services in Untrusted Outsourced Environments

Authenticated Spatio-Textual Similarity Joins in Untrusted Cloud Environments

A novel regularizer for temporally stable learning with an application to Twitter topic classification

A CORBA design pattern to build load balancing and fault tolerant telecommunication software