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Multireceiver identity (ID) based encryption and ID-based broadcast encryption allow a sender to use the public identities of multiple receivers to encrypt messages so that only the selected receivers or a privileged set of users can decrypt the messages. It can be used for many practical applications such as digital content distribution, pay-per-view and multicast communication. For protecting the privacy of receivers or providing receiver anonymity, several privacy-preserving (or anonymous) multireceiver ID-based encryption and ID-based broadcast encryption schemes were recently proposed, in which receiver anonymity means that nobody (including any selected receiver), except the sender, knows who the other selected receivers are. However, security incompleteness or flaws were found in these schemes. In this paper, we propose a new privacy-preserving multireceiver ID-based encryption scheme with provable security. We formally prove that the proposed scheme is semantically secure for confidentiality and receiver anonymity. Compared with the previously proposed anonymous multireceiver ID-based encryption and ID-based broadcast encryption schemes, the proposed scheme has better performance and robust security.In the following, we redefine the security notions of receiver anonymity including ANON-IND-sID-CPA and ANON-IND-sID-CCA security games in [11] that take into consideration a multireceiver setting.

The Protein Data Bank (PDB) is the worldwide repository of 3D structures of proteins, nucleic acids and complex assemblies. The PDB’s large corpus of data (> 100,000 structures) and related citations provide a well-organized and extensive test set for developing and understanding data citation and access metrics. In this paper, we present a systematic investigation of how authors cite PDB as a data repository. We describe a novel metric based on information cascade constructed by exploring the citation network to measure influence between competing works and apply that to analyze different data citation practices to PDB. Based on this new metric, we found that the original publication of RCSB PDB in the year 2000 continues to attract most citations though many follow-up updates were published. None of these follow-up publications by members of the wwPDB organization can compete with the original publication in terms of citations and influence. Meanwhile, authors increasingly choose to use URLs of PDB in the text instead of citing PDB papers, leading to disruption of the growth of the literature citations. A comparison of data usage statistics and paper citations shows that PDB Web access is highly correlated with URL mentions in the text. The results reveal the trend of how authors cite a biomedical data repository and may provide useful insight of how to measure the impact of a data repository.

Recently, Fan et al. proposed the first anonymous multi-receiver identity (ID)-based encryption (AMIBE) scheme to encrypt a message for multiple selected receivers while achieving anonymity. They claimed that nobody, except the sender, knows who the selected receivers are. However, their scheme has been shown that any selected receiver can extract the identities of the other selected receivers because their security model cannot capture the full requirement of receiver anonymity. In this article, we first re-define the security model of AMIBE. We then propose a new AMIBE scheme to achieve complete receiver anonymity. We prove that the proposed AMIBE scheme is semantically secure against adaptive chosen ciphertext attacks (CCA). Comparisons are given to demonstrate the proposed AMIBE scheme's advantages.

Motivation: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise.Results: We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification. Calibration is essential for a practical quantification method because we need to measure the confidence of the quantification. We applied our method to quantify the degree of partial fragmentation of mitochondria in collections of fluorescent cell images. We show that with transformation, the quantification can be more accurate and sensitive than that without transformation. We also show that our method outperforms competing methods, including neighbourhood component analysis and the multi-variate drug profiling method by Loo et al. We illustrate its utility with a study of Annonaceous acetogenins, a family of compounds with drug potential. Our result reveals that squamocin induces more fragmented mitochondria than muricin A.Availability: Mitochondrial cell images, their corresponding feature sets (SSLF and WSLF) and the source code of our proposed method are available at http://aiia.iis.sinica.edu.tw/.Contact: chunnan@iis.sinica.edu.twSupplementary information: Supplementary data are available at Bioinformatics online.

Recently, applications of Internet of Things create enormous volumes of data, which are available for classification and prediction. Classification of big data needs an effective and efficient metaheuristic search algorithm to find the optimal feature subset. Cat swarm optimization (CSO) is a novel metaheuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two submodes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known metaheuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection and parameter optimization of support vector machines (SVMs). Experiment results demonstrate the superiority of MCSO in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original CSO algorithm. Moreover, experiment results show the fittest CSO parameters and MCSO take less training time to obtain results of higher accuracy than original CSO. Therefore, MCSO is suitable for real-world applications.

Abstract-Transformative research refers to research that shifts or disrupts established scientific paradigms. Notable examples include the discovery of high-temperature superconductivity that disrupted the theory established 30 years ago. Identifying potential transformative research early and accurately is important for funding agencies to maximize the impact of their investments. It also helps scientists identify and focus their attention on promising emerging works. This paper presents a datadriven approach where citation patterns of scientific papers are analyzed to quantify how much a potential challenger idea shifts an established paradigm. The key idea is that transformative research creates an observable disruption in the structure of "information cascades," chains of references that can be traced back to the papers establishing some scientific paradigm. Such a disruption is visible soon after the challenger's introduction. We define a disruption score to quantify the disruption and develop an algorithm to compute it from a large citation network. Experimental results show that our approach can successfully identify transformative scientific papers that disrupt established paradigms in Physics and Computer Science, regardless of whether the challenger paradigm is an instant hit or a classic whose contribution is formally recognized with a Nobel Prize decades later.

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