Influence maximization (im) is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. Despite the progress achieved by state-of-the-art greedy im techniques, they suffer from two key limitations. Firstly, they are inefficient as they can take days to find seeds in very large realworld networks. Secondly, although extensive research in social psychology suggests that humans will readily conform to the wishes or beliefs of others, surprisingly, existing im techniques are conformity-unaware. That is, they only utilize an individual's ability to influence another but ignores conformity (a person's inclination to be influenced) of the individuals. In this paper, we propose a novel conformityaware cascade (c 2 ) model which leverages on the interplay between influence and conformity in obtaining the influence probabilities of nodes from underlying data for estimating influence spreads. We also propose a variant of this model Electronic supplementary material The online version of this article (called c 3 model that supports context-specific influence and conformity of nodes. A salient feature of these models is that they are aligned to the popular social forces principle in social psychology. Based on these models, we propose a novel greedy algorithm called cinema that generates highquality seed set for the im problem. It first partitions, the network into a set of non-overlapping subnetworks and for each of these subnetworks it computes the influence and conformity indices of nodes by analyzing the sentiments expressed by individuals. Each subnetwork is then associated with a cog-sublist which stores the marginal gains of the nodes in the subnetwork in descending order. The node with maximum marginal gain in each cog-sublist is stored in a data structure called mag-list. These structures are manipulated by cinema to efficiently find the seed set. A key feature of such partitioning-based strategy is that each node's influence computation and updates can be limited to the subnetwork it resides instead of the entire network. This paves way for seamless adoption of cinema on a distributed platform. Our empirical study with real-world social networks comprising of millions of nodes demonstrates that cinema as well as its context-aware and distributed variants generate superior quality seed set compared to state-of-the-art im approaches.

Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.

Hash-based methods achieve fast similarity search by representing high-dimensional data with compact binary codes. However, both generating binary codes and encoding unseen data effectively and efficiently remain very challenging tasks. In this article, we focus on these tasks to implement approximate similarity search by proposing a novel hash based method named sparse hashing (SH for short). To generate interpretable (or semantically meaningful) binary codes, the proposed SH first converts original data into lowdimensional data through a novel nonnegative sparse coding method. SH then converts the low-dimensional data into Hamming space (i.e., binary encoding low-dimensional data) by a new binarization rule. After this, training data are represented by generated binary codes. To efficiently and effectively encode unseen data, SH learns hash functions by taking a-priori knowledge into account, such as implicit group effect of the features in training data, and the correlations between original space and the learned Hamming space. SH is able to perform fast approximate similarity search by efficient bit XOR operations in the memory of a modern PC with short binary code representations. Experimental results show that the proposed SH significantly outperforms state-of-the-art techniques.

Hidden policy ciphertext-policy attribute-based encryption with keyword search against keyword guessing attack SCIENCE CHINA Information Sciences 60, 052105 (2017); Ciphertext-policy attribute-based proxy re-encryption via constrained PRFs SCIENCE CHINA Information Sciences 64, 169301 (2021); Efficient large-universe multi-authority ciphertext-policy attribute-based encryption with white-box traceability SCIENCE CHINA Information Sciences 61, 032102 (2018); Accountable authority key policy attribute-based encryption SCIENCE CHINA Information Sciences 55, 1631 (2012); Adaptively secure multi-authority attribute-based encryption with verifiable outsourced decryption SCIENCE CHINA Information Sciences 59, 099105 (2016);. RESEARCH PAPER. SCIENCE CHINA Information Sciences

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BackgroundOcular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial.MethodsIn this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient.ResultsQualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method.ConclusionOur study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.Electronic supplementary materialThe online version of this article (10.1186/s12938-017-0420-1) contains supplementary material, which is available to authorized users.

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