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

Negotiation is a crucial stage of Web Services interaction lifecycle. By exchanging a sequence of proposals in the negotiation stage, a service provider and a consumer try to establish a formal contract, to specify agreed terms on the service, particularly terms on non-functional aspects. To react to an ever-changing environment, flexible negotiation strategies that can make adjustable rates of concession should be adopted. This paper presents such flexible strategies for Web Services negotiation. In a negotiation round, the negotiation strategies first examine the environment situations by evaluating certain factors, which include time, resources, number of counterparts and current proposals from the counterparts. For each factor, there is a corresponding function that suggests the amount of concession in terms of that factor. Then considering the importance of each service attribute, the target concession per attribute is determined. As a final step, a set of experimental tests is executed to evaluate the performance of the negotiation strategies.

Flexible decision making in web services negotiation

In this paper, we present an encoder-decoder model for distant supervised relation extraction. Given an entity pair and its sentence bag as input, in the encoder component, we employ the convolutional neural network to extract the features of the sentences in the sentence bag and merge them into a bag representation. In the decoder component, we utilize the long short-term memory network to model relation dependencies and predict the target relations in a sequential manner. In particular, to enable the sequential prediction of relations, we introduce a measure to quantify the amounts of information the relations take in their sentence bag, and use such information to determine the order of the relations of a sentence bag during model training. Moreover, we incorporate the attention mechanism into our model to dynamically adjust the bag representation to reduce the impact of sentences whose corresponding relations have been predicted. Extensive experiments on a popular dataset show that our model achieves significant improvement over state-of-the-art methods.

/pdf/exploring-encoder-decoder-model-for-distant-supervised-7q0xyzblzi.pdf

Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction

Recently, more and more devices with small buffer size such as PDAs or mobile phones are joining in the VoD system, which leads to two major challenges: how to efficiently distribute their bandwidth resources with small buffer size, and how to provide assistant mechanism to make them playback smoothness. In face of this situation and for the purpose of decreasing the server bandwidth costs, we propose a peers’ downloading mechanism called NCDLT to solve above challenges. It contains two algorithms. The first is neighbors and chunks downloading selection (NCS) algorithm and it ensures peers to find neighbors who can provide video data with lower refusal rate. The second is distributed linear taxation algorithm (DLT) and it makes peers with lower capability acquire enough download rate to reduce the request to servers. The simulation results demonstrate that our algorithms can offload the server bandwidth costs and improve the download rate of peers with small buffer size.

An efficient server bandwidth costs decreased mechanism towards mobile devices in cloud-assisted P2P-VoD system

Social network alignment, aligning different social networks on their common users, is receiving dramatic attention from both academic and industry. All existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic social Network Alignment (DNA) framework, a unified optimization approach over deep neural architectures, to unfold the fruitful dynamics to perform alignment. However, it faces tremendous challenges in both modeling and optimization: (1) To model the intra-network dynamics, we explore the local dynamics of the latent pattern in friending evolvement and the global consistency of the representation similarity with neighbors. We design a novel deep neural architecture to obtain the dual embedding capturing local dynamics and global consistency for each user. (2) To model the inter-network alignment, we exploit the underlying identity of an individual from the dual embedding in each dynamic social network. We design a unified optimization approach interplaying proposed deep neural architectures to construct a common subspace of identity embeddings. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed DNA framework substantially outperforms the state-of-the-art methods.

DNA: Dynamic Social Network Alignment

This paper addresses a popularity-aware collective keyword (PAC-K) query in road networks. Given a road network with POIs (Points of Interest), which is modeled as a road network graph, where each node locating in a two-dimensional space represents a road intersection or a POI, and each edge with weight represents a road segment, the PACK query aims to find a group of popular POIs (i.e., a popular region) that cover the query’s keywords and satisfy the distance requirements from each node to the query node and between each pair of nodes, such that the sum of rating scores over these nodes for the query keywords is maximized. We show the problem of answering the PACK query is NP-Hard. To solve this problem, we present exact and heuristic solutions on small and large road networks, respectively. In particular, to improve query performance, we propose a rating score scaling technique to reduce the search space and a redundant computation reducing technique to reduce the excessive redundant computations in query processing. Extensive performance studies using two real datasets confirm the efficiency, accuracy, and scalability of the proposed solutions.

Sen Su

Papers

Flexible decision making in web services negotiation

Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction

An efficient server bandwidth costs decreased mechanism towards mobile devices in cloud-assisted P2P-VoD system

DNA: Dynamic Social Network Alignment

Popularity-aware collective keyword queries in road networks