Multi-label classification via learning a unified object-label graph with sparse representation

Automatic annotation is an essential technique for effectively handling and organizing Web objects (e.g., Web pages), which have experienced an unprecedented growth over the last few years. Automatic annotation is usually formulated as a multi-label classification problem. Unfortunately, labeled data are often time-consuming and expensive to obtain. Web data also accommodate much richer feature space. This calls for new semi-supervised approaches that are less demanding on labeled data to be effective in classification. In this paper, we propose a graph-based semi-supervised learning approach that leverages random walks and ? ₁ sparse reconstruction on a mixed object-label graph with both attribute and structure information for effective multi-label classification. The mixed graph contains an object-affinity subgraph, a label-correlation subgraph, and object-label edges with adaptive weight assignments indicating the assignment relationships. The object-affinity subgraph is constructed using ? ₁ sparse graph reconstruction with extracted structural meta-text, while the label-correlation subgraph captures pairwise correlations among labels via linear combination of their co-occurrence similarity and kernel-based similarity. A random walk with adaptive weight assignment is then performed on the constructed mixed graph to infer probabilistic assignment relationships between labels and objects. Extensive experiments on real Yahoo! Web datasets demonstrate the effectiveness of our approach.     

Complex-query web image search with concept-based relevance estimation

Complex queries are widely used in current Web applications. They express highly specific information needs, but simply aggregating the meanings of primitive visual concepts does not perform well. To facilitate image search of complex queries, we propose a new image reranking scheme based on concept relevance estimation, which consists of Concept-Query and Concept-Image probabilistic models. Each model comprises visual, web and text relevance estimation. Our work performs weighted sum of the underlying relevance scores, a new ranking list is obtained. Considering the Web semantic context, we involve concepts by leveraging lexical and corpus-dependent knowledge, such as Wordnet and Wikipedia, with co-occurrence statistics of tags in our Flickr corpus. The experimental results showed that our scheme is significantly better than the other existing state-of-the-art approaches.     

Subgraph similarity maximal all-matching over a large uncertain graph

Recently, uncertain graph data management and mining techniques have attracted significant interests and research efforts due to potential applications such as protein interaction networks and social networks. Specifically, as a fundamental problem, subgraph similarity all-matching is widely applied in exploratory data analysis. The purpose of subgraph similarity all-matching is to find all the similarity occurrences of the query graph in a large data graph. Numerous algorithms and pruning methods have been developed for the subgraph matching problem over a certain graph. However, insufficient efforts are devoted to subgraph similarity all-matching over an uncertain data graph, which is quite challenging due to high computation costs. In this paper, we define the problem of subgraph similarity maximal all-matching over a large uncertain data graph and propose a framework to solve this problem. To further improve the efficiency, several speed-up techniques are proposed such as the partial graph evaluation, the vertex pruning, the calculation model transformation, the incremental evaluation method and the probability upper bound filtering. Finally, comprehensive experiments are conducted on real graph data to test the performance of our framework and optimization methods. The results verify that our solutions can outperform the basic approach by orders of magnitudes in efficiency.     

Factors affecting the performance of knowledge collaboration in virtual team based on capital appreciation

Knowledge collaboration (KC) is an important strategy measure to improve knowledge management, focusing on not only efficiency of knowledge cooperation, but also adding value of intellectual capital and social capital. In virtual teams, many factors, such as team’s network characteristics, collaborative culture, and individual collaborative intention, affect the performance of KC. By discussing the nature of KC, this paper presents that the performance of can be measured from two aspects: effectiveness of collaboration and efficiency of cooperation. Among them, effectiveness of collaboration is measured through value added and efficiency of cooperation is measured through accuracy and timeliness. Then the paper discusses the factors affecting the performance of KC from network characteristics, individual attributes and team attributes. The results show that network characteristics, individual attributes and team attributes in virtual team have significant impacts on the performance of KC.     

Random oracle-based anonymous credential system for efficient attributes proof on smart devices

Attributes proof in anonymous credential systems is an effective way to balance security and privacy in user authentication; however, the linear complexity of attributes proof causes the existing anonymous credential systems far away from being practical, especially on resource-limited smart devices. For efficiency considerations, we present a novel pairing-based anonymous credential system which solves the linear complexity of attributes proof based on aggregate signature scheme. We propose two extended signature schemes, BLS+ and BGLS+, to be cryptographical building blocks for constructing anonymous credentials in the random oracle model. Identity-like information of message holder is encoded in a signature in order that the message holder can prove the possession of the input message along with the validity of a signature. We present issuance protocol for anonymous credentials embedding weak attributes which are referred to what cannot identify a user in a population. Users can prove any combination of attributes all at once by aggregating the corresponding individual credentials into one. The attributes proof protocols on AND and OR relation over multiple attributes are also given. The performance analysis shows that the aggregation-based anonymous credential system outperforms both the conventional Camenisch–Lysyanskaya pairing-based system and the accumulator-based system when prove AND and OR relation over multiple attributes, and the size of credential and public parameters are shorter as well.     

Discriminative sequential association latent dirichlet allocation for visual recognition

Graphical models have been employed in a wide variety of computer vision tasks. Assignments of latent variables in typical models usually suffer the confused explanation in sampling way. In this paper we present discriminative sequential association Latent Dirichlet Allocation, a novel statistical model for the task of visual recognition, and especially focus on the case of few training examples. By introducing the switching variables and formulating the direct discriminative analysis, the sequential associations are considered as priori to establish a relevance determination mechanism to obtain the reasonable assignments of latent variables and avoid the invalid labeling oscillations. We demonstrate the power of our model on two common-used datasets, and the experiment results show that our model can achieve better performances with efficient convergence and give well interpretations of specific topic assignments at the same time.     

Four-state quantum key distribution exploiting maximum mutual information measurement strategy

We propose a four-state quantum key distribution (QKD) scheme using generalized measurement of nonorthogonal states, the maximum mutual information measurement strategy. Then, we analyze the eavesdropping process in intercept–resend and photon number splitting attack scenes. Our analysis shows that in the intercept–resend and photon number splitting attack eavesdropping scenes, our scheme is more secure than BB84 protocol and has higher key generation rate which may be applied to high-density QKD.     

Teleportation of a general two-photon state employing a polarization-entangled $$\chi $$ state with nondemolition parity analyses

Employing a polarization-entangled \(\chi \) state, which is a four-photon genuine entangled state, we propose a protocol teleporting a general two-photon polarization state. Firstly, the sender needs to perform one Controlled-NOT gate, one Hadamard gate, and one Controlled-NOT gate on the state to be teleported in succession. Secondly, the sender performs local nondemolition parity analyses based on cross-Kerr nonlinearities and publicizes the achieved outcomes. Finally, conditioned on the sender’s analysis outcomes, the receiver executes the single-photon unitary transformation operations on his own photons to obtain the state originally sit in the sender’s location. Due to the employment of nondemolition parity analyses rather than four-qubit joint measurement, it can be realized more feasible with currently available technologies. Moreover, the resources of Bell states can be achieved because the nondestructive measurement is exploited, which facilitates other potential tasks of quantum information processing.