Abstract-We perform a large-scale study to quantify just how severe the privacy leakage problem is in Facebook. As a case study, we focus on estimating birth year, which is a fundamental human attribute and, for many people, a private one. Specifically, we attempt to estimate the birth year of over 1 million Facebook users in New York City. We examine the accuracy of estimation procedures for several classes of users: (i) highly private users, who do not make their friend lists public; (ii) users who hide their birth years but make their friend lists public.To estimate Facebook users' ages, we exploit the underlying social network structure to design an iterative algorithm, which derives age estimates based on friends' ages, friends of friends' ages, and so on. We find that for most users, including highly private users who hide their friend lists, it is possible to estimate ages with an error of only a few years. We also make a specific suggestion to Facebook which, if implemented, would greatly reduce privacy leakages in its service.

In the recent years, polyoxometalate (POM) encapsulated metal–organic framework (MOF) composites have attracted much attention in photocatalysis. Both POMs and MOFs have been attracting immense attention in this area. Furthermore, in order to promote charge transfer and separation between POMs and MOFs, theoretical and experimental analysis can be applied to match their energy levels; however, their individual applications are hindered by several defects, such as poor visible-light utilization efficiency. The combination of MOFs and POMs can benefit from the virtues of both POMs and MOFs while avoiding the drawbacks of them. Notably, MOFs with high specific surface area and long-range ordered structure ensure a uniform distribution of POMs, which cannot only prevent the self-aggregation of POMs but also allow the exposure of more active sites for catalysis. POM@MOF composites have been identified as promising materials for photocatalysis because of their diverse unique advantages, such as ultrahigh porosity, large specific surface area, and excellent electron redox transformation. In this work, we present an overview of the developments in POM@MOF composite-based catalysts for visible light induced photocatalysis. The strategies employed for the preparation of POM@MOF composites are summarized and discussed with a particular focus on the stability of such materials. The representative works on photocatalytic water splitting, CO2 reduction, degradation of pollutants, and selective oxidation of organics are highlighted. Special attention is paid to the synergistic effects between the MOF and POM that result in an enhanced performance. Besides, the stability and reusability of these materials are also discussed. Also, the unsolved problems and development opportunities of POM@MOF composites in the field of photocatalysis are proposed.

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Among all possible options, semiconductor photocatalytic technology is considered as an environmentally friendly and ideal strategy among all possible options and has been rapidly developed around the world. [5][6][7][8][9] Since the first report of artificial photocatalysis in 1972 by Fujishima et al., traditional TiO 2 has been widely used because of its intrinsic photocatalytic performance, low toxicity, and thermodynamic stability. [10][11][12] However, TiO 2 can be only photogenerated under ultraviolet light (UV-light), limiting its utilization efficiency of solar energy. [13,14] Accordingly, researchers are actively searching for photocatalysts with excellent visible light response and satisfactory photocatalytic performance. [15][16][17] Graphitic carbon nitride (g-C 3 N 4 ) is a metal-free nanomaterial composing of C and N with a defect-N bridges of s-triazine or tri-s-triazine (Figure 1A,B). [19,20] The history of C 3 N 4 could be traced back to 1834, but g-C 3 N 4 was not formally proposed until 1996 by Teter and Hemley, probably due to its high stability and mysterious structure. [21] Subsequently, g-C 3 N 4 has emerged as an encouraging candidate for photocatalysts based on its moderate bandgap, suitable energy band structure (Figure 1C), visible light absorption and high stability, as well as been widely concerned by countries all over the world. [18,[22][23][24] Since the photocatalytic H 2 evolution of g-C 3 N 4 was first reported in 2009, the research on improving the photocatalytic performance of g-C 3 N 4 -based photocatalysts has gradually become a popular research direction (Figure 2). [18,25,26] Pristine g-C 3 N 4 can be prepared by using the heat treatment of some low-cost nitrogen-rich organic precursors, such as urea, melamine, dicyandiamide, and so on. [27] However, their practical applications have been limited by several shortcomings of pristine g-C 3 N 4 , including low specific surface area, insufficient utilization of visible light (< 460 nm), and rapid recombination of photogenerated electron-hole (e − -h + ) pairs. [28,29] For the sake of overcoming these challenges, many methods have been employed to modify g-C 3 N 4 to improve the photocatalytic activity, such as element/heteroatomic doping, [30] structural design, [31,32] and heterojunction construction. [33][34][35] Among the recent progresses of modifying g-C 3 N 4 , heterojunctions formed Recently, graphitic carbon nitride (g-C 3 N 4 ) has attracted increasing interest due to its visible light absorption, suitable energy band structure, and excellent stability. However, low specific surface area, finite visible light response range (<460 nm), and rapid photogenerated electron-hole (e − -h + ) pairs recombination of the pristine g-C 3 N 4 limit its practical applications. The small size of quantum dots (QDs) endows the properties of abundant active sites, wide absorption spectrum, and adjustable bandgap, but inevitable aggregation. Studies have confirmed that the integration of g-C 3 N 4 and QDs not only overcomes these limita...

A multi-view object detection approach based on deep learning is proposed in this paper. Classical object detection methods based on regression models are introduced, and the reasons for their weak ability to detect small objects are analyzed. To improve the performance of these methods, a multi-view object detection approach is proposed, and the model structure and working principles of this approach are explained. Additionally, the object retrieval ability and object detection accuracy of both the multi-view methods and the corresponding classical methods are evaluated and compared based on a test on a small object dataset. The experimental results show that in terms of object retrieval capability, Multi-view YOLO (You Only Look Once: Unified, Real-Time Object Detection), Multi-view YOLOv2 (based on an updated version of YOLO), and Multi-view SSD (Single Shot Multibox Detector) achieve AF (average F-measure) scores that are higher than those of their classical counterparts by 0.177, 0.06, and 0.169, respectively. Moreover, in terms of the detection accuracy, when difficult objects are not included, the mAP (mean average precision) scores of the multi-view methods are higher than those of the classical methods by 14.3%, 7.4%, and 13.1%, respectively. Thus, the validity of the approach proposed in this paper has been verified. In addition, compared with state-of-the-art methods based on region proposals, multi-view detection methods are faster while achieving mAPs that are approximately the same in small object detection.

Abstract-Attack graphs play important roles in analyzing network security vulnerabilities, and previous works have provided meaningful conclusions on the generation and security measurement of attack graphs. However, it is still hard for us to understand attack graphs in a large network, and few suggestions have been proposed to prevent inside malicious attackers from attacking networks. To address these problems, we propose a novel approach to generate and describe attack graphs. Firstly, we construct a two-layer attack graph, where the upper layer is a hosts access graph and the lower layer is composed of some host-pair attack graphs. Compared with previous works, our attack graph has simpler structures, and reaches the best upper bound of computation cost in O(N 2 ). Furthermore, we introduce the adjacency matrix to efficiently evaluate network security, with overall evaluation results presented by gray scale images vividly. Thirdly, by applying prospective damage and important weight factors on key hosts with crucial resources, we can create prioritized lists of potential threatening hosts and stepping stones, both of which can help network administrators to harden network security. Analysis on computation cost shows that the upper bound computation cost of our measurement methodology is O(N 3 ), which could also be completed in real time. Finally, we give some examples to show how to put our methods in practice.

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