A study of detecting child pornography on smart phone

© Springer International Publishing AG 2018. Child Pornography is an increasingly visible rising cybercrime in the world today. Over the past decade, with rapid growth in smart phone usage, readily available free Cloud Computing storage, and various mobile communication apps, child pornographers have found a convenient and reliable mobile platform for instantly sharing pictures or videos of children being sexually abused. Within this new paradigm, law enforcement officers are finding that detecting, gathering, and processing evidence for the prosecution of child pornographers is becoming increasingly challenging. Deep learning is a machine learning method that models high-level abstractions in data and extracts hierarchical representations of data by using a deep graph with multiple processing layers. This paper presents a conceptual model of deep learning approach for detecting child pornography within the new paradigm by using log analysis, file name analysis and cell site analysis which investigate text logs of events that have happened in the smart phone at the scene of the crime using physical and logical acquisition to assists law enforcement officers in gathering and processing child pornography evidence for prosecution. In addition, this paper shows an illustrative example of logical and physical acquisition on smart phones using forensics tools

Simulating aerosol microphysics with the ECHAM/MADE GCM ? Part I: Model description and comparison with observations

International audienceThe aerosol dynamics module MADE has been coupled to the general circulation model ECHAM4 to simulate the chemical composition, number concentration, and size distribution of the global submicrometer aerosol. The present publication describes the new model system ECHAM4/MADE and presents model results in comparison with observations. The new model is able to simulate the full life cycle of particulate matter and various gaseous precursors including emissions of primary particles and trace gases, advection, convection, diffusion, coagulation, condensation, nucleation of sulfuric acid vapor, aerosol chemistry, cloud processing, and size-dependent dry and wet deposition. Aerosol components considered are sulfate (SO4), ammonium (NH4), nitrate (NO3), black carbon (BC), particulate organic matter (POM), sea salt, mineral dust, and aerosol liquid water. The model is numerically efficient enough to allow long term simulations, which is an essential requirement for application in general circulation models. In order to evaluate the results obtained with this new model system, calculated mass concentrations, particle number concentrations, and size distributions are compared to observations. The intercomparison shows, that ECHAM4/MADE is able to reproduce the major features of the geographical patterns, seasonal cycle, and vertical distributions of the basic aerosol parameters. In particular, the model performs well under polluted continental conditions in the northern hemispheric lower and middle troposphere. However, in comparatively clean remote areas, e.g. in the upper troposphere or in the southern hemispheric marine boundary layer, the current model version tends to underestimate particle number concentrations

Wiring up pre-characterized single-photon emitters by laser lithography

Future quantum optical chips will likely be hybrid in nature and include many single-photon emitters, waveguides, filters, as well as single-photon detectors. Here, we introduce a scalable optical localization-selection-lithography procedure for wiring up a large number of single-photon emitters via polymeric photonic wire bonds in three dimensions. First, we localize and characterize nitrogen vacancies in nanodiamonds inside a solid photoresist exhibiting low background fluorescence. Next, without intermediate steps and using the same optical instrument, we perform aligned three-dimensional laser lithography. As a proof of concept, we design, fabricate, and characterize three-dimensional functional waveguide elements on an optical chip. Each element consists of one single-photon emitter centered in a crossed-arc waveguide configuration, allowing for integrated optical excitation and efficient background suppression at the same time

Public health program capacity for sustainability: A new framework

Abstract Background Public health programs can only deliver benefits if they are able to sustain activities over time. There is a broad literature on program sustainability in public health, but it is fragmented and there is a lack of consensus on core constructs. The purpose of this paper is to present a new conceptual framework for program sustainability in public health. Methods This developmental study uses a comprehensive literature review, input from an expert panel, and the results of concept-mapping to identify the core domains of a conceptual framework for public health program capacity for sustainability. The concept-mapping process included three types of participants (scientists, funders, and practitioners) from several public health areas (e.g., tobacco control, heart disease and stroke, physical activity and nutrition, and injury prevention). Results The literature review identified 85 relevant studies focusing on program sustainability in public health. Most of the papers described empirical studies of prevention-oriented programs aimed at the community level. The concept-mapping process identified nine core domains that affect a program’s capacity for sustainability: Political Support, Funding Stability, Partnerships, Organizational Capacity, Program Evaluation, Program Adaptation, Communications, Public Health Impacts, and Strategic Planning. Concept-mapping participants further identified 93 items across these domains that have strong face validity—89% of the individual items composing the framework had specific support in the sustainability literature. Conclusions The sustainability framework presented here suggests that a number of selected factors may be related to a program’s ability to sustain its activities and benefits over time. These factors have been discussed in the literature, but this framework synthesizes and combines the factors and suggests how they may be interrelated with one another. The framework presents domains for public health decision makers to consider when developing and implementing prevention and intervention programs. The sustainability framework will be useful for public health decision makers, program managers, program evaluators, and dissemination and implementation researchers

Arnol'd Tongues and Quantum Accelerator Modes

The stable periodic orbits of an area-preserving map on the 2-torus, which is formally a variant of the Standard Map, have been shown to explain the quantum accelerator modes that were discovered in experiments with laser-cooled atoms. We show that their parametric dependence exhibits Arnol'd-like tongues and perform a perturbative analysis of such structures. We thus explain the arithmetical organisation of the accelerator modes and discuss experimental implications thereof.Comment: 20 pages, 6 encapsulated postscript figure

Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate

Lithium-ion batteries with solid electrolytes offer safety, higher energy density and higher long-term performance, which are promising alternatives to conventional liquid electrolyte batteries. Lithium aluminum titanium phosphate (LATP) is one potential solid electrolyte candidate due to its high Li-ion conductivity. To evaluate its performance, influences of the experimental factors on the materials design need to be investigated systematically. In this work, a materials design strategy based on machine learning (ML) is employed to design experimental conditions for the synthesis of LATP. In the variation of parameters, we focus on the tolerance against the possible deviations in the concentration of the precursors, as well as the influence of sintering temperature and holding time. Specifically, models built with different design selection strategies are compared based on the training data assembled from previous laboratory experiments. The best one is then chosen to design new experiment parameters, followed by measuring the corresponding properties of the newly synthesized samples. A previously unknown sample with ionic conductivity of 1.09 × 10$^{-3}$ S cm$^{-1}$ is discovered within several iterations. In order to further understand the mechanisms governing the high ionic conductivity of these samples, the resulting phase compositions and crystal structures are studied with X-ray diffraction, while the microstructures of sintered pellets are investigated by scanning electron microscopy. Our studies demonstrate the advantages of applying machine learning in designing experimental conditions by the synthesis of desired materials, which can effectively help researchers to reduce the number of required experiments