A Tool for Privacy-Aware Online Personal Photo Sharing Using Deep Learning Technique

In recent years, online social networking has been considered as a sharing information platform and has occupied an essential part in many individual's lives and business growths. Consequently, there has been a marked increase in the collection and illegal exploitation of photos amassed online without owner consent, thus violating individual privacy rights such as contravention of online published photo laws which contribute to public social anxiety. To address these concerns, we propose a face recognition tool based on the Deep Learning Convolutional Neural Network (CNN) technique, which may be utilized within a social networking website as a gateway control for posting images. The goal of this paper is to preserve user privacy by preventing their images from being posted on social networking sites without prior consent. This tool will extract features from an input photo posted on a social network site and compare those attributes against the facial characteristics of photos in a prohibited dataset, which is comprised of users unwilling to share their photos. Depending on the result, the CNN-based tool could either allow sharing of the photo or prevent and alert the user attempting to post or share a given photo about his/her potential violation of end-user privacy provided the image belongs to a person on the banned list. Additionally, the CNN tool will provide an option for a user to add his/her photo to the banned list. The proposed tool includes two main elements which have been developed in Python with Jupyter Notebook. The first component is a deep learning model which is trained on LFW images dataset capable of achieving 91.89% matching accuracy. The second is the GUI of the tool which allows the user to input photos and use the trained model to predict whether this photo belongs to the person in banned list, thus preventing illicit sharing downstream. The integration between two elements has been tested and achieved 85% accuracy. Keywords: Deep Learning, Face Recognition (FR), Convolution Neural Networks (CNNs), Online Social Networks (OSNs), Online Photo Sharing.

Functionalized polymers of intrinsic microporosity for highly energy- intensive gas separations

Membrane-based gas separation is a rapidly emerging technology that has been established in the purification of air and hydrogen streams and is showing an increasingly larger role in natural gas sweetening and vapor/gas separations. One strategy actively pursued to generate polymers with combinations of high permeability and high selectivity is the introduction of microporosity (pores \u3c 20 Å) in the polymer matrix. It has been shown that rigid ladder-type chains comprising fused rings joined by sites of contortion pack inefficiently in the solid state to produce polymers of intrinsic microporosity (PIMs). Recently, a successful integration of monomers contorted by spirobisindane, ethanoanthracene, Tröger’s base and triptycene moieties into polyimide structures has also generated highly permeable intrinsically microporous polyimides (PIM-PIs). Some of these PIM-PIs have shown significantly enhanced performance for O2/N2, H2/N2 and H2/CH4 separations with properties defining the most recent 2015 permeability/selectivity upper bounds. Here, we will discuss several series of novel PIM-PIs and ladder PIMs based on rigid and bicyclic moieties, which are solution processable to form mechanically robust films with high internal surface areas (up to 1100 m2/g). Gas permeation and physisorption data indicate the development of an ultramicroporous structure that is tunable for different gas separation applications. Specific emphasis will be placed on the potential use of hydroxyl- and carboxyl-functionalized PIMs for highly-energy demanding applications for natural gas treatment and olefin/paraffin separation

Tahdzib Syarh Ath-Thahawiyah: dasar-dasar aqidah menurut ulama salaf

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Synthesis of Highly Gas-Permeable Polyimides of Intrinsic Microporosity Derived from 1,3,6,8-Tetramethyl-2,7-diaminotriptycene

A simple synthetic route to a novel sterically hindered triptycene-based diamine, 1,3,6,8-tetramethyl-2,7-diaminotriptycene (TMDAT), and its use in the preparation of high molecular weight polyimides of intrinsic microporosity (PIM-PIs) are reported. The organosoluble TMDAT-derived polyimides displayed high Brunauer–Emmett–Teller surface areas ranging between 610 and 850 m2 g–1 and demonstrated excellent thermal stability of up to 510 °C. Introduction of the rigid three-dimensional paddlewheel triptycene framework and the tetramethyl-induced restriction of the imide bond rotation resulted in highly permeable polyimides with moderate gas-pair selectivity. The best performing polyimide made from TMDAT and a triptycene-based dianhydride showed gas transport properties located between the 2008 and 2015 polymer permeability/selectivity trade-off curves with H2 and O2 permeabilities of 2858 and 575 barrer combined with H2/N2 and O2/N2 selectivities of 24 and 4.8, respectively, after 200 days of physical aging

Functionalized Nanomembranes and Plasma Technologies for Produced Water Treatment: A Review

The treatment of produced water, associated with oil & gas production, is envisioned to gain more significant attention in the coming years due to increasing energy demand and growing interests to promote sustainable developments. This review presents innovative practical solutions for oil/water separation, desalination, and purification of polluted water sources using a combination of porous membranes and plasma treatment technologies. Both these technologies can be used to treat produced water separately, but their combination results in a significant synergistic impact. The membranes functionalized by plasma show a remarkable increase in their efficiency characterized by enhanced oil rejection capability and reusability, while plasma treatment of water combined with membranes and/or adsorbents could be used to soften water and achieve high purity