Free 2018 IEEE (CSE) synopsis of students requested papers

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Automatic Annotation of Text with Pictures

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Aim: a novel application has emerged that automatically adds pictures to text. Objectives: Is to reduce a reader’s cognitive information processing load1 by showing relevant pictures along with input text. A reader can quickly and easily decide if the text information deserves a closer look. Existing System: Here, provides a detailed comparison of eight well-known picturing systems. 1. WordsEye,17 a text-to-3D-scene rendering system, parses text into a semantic representation and creates a 3D rendering (a scene). 2. Story Picturing Engine9 extracts keywords and matches words to a labeled image database as part of the process of story illustration. 3. ROC-MMS for Complex Sentences12 produces a multimodal (text and pictures) summary of sentences. 4. Simple Sentence Picturing18 is an effort to investigate the idea that pictures can convey simple sentences across language barriers. 5. Enriching Textbooks19 matches sections of a textbook to Internet images...
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Privacy Characterization and Quantification in Data Publishing

Aim:   propose a novel multi-variable privacy characterization and quantification model.  Existing system: Privacy-Preserving Data Publishing (PPDP) techniques (k-anonymity, l-diversity and t-closeness.) have been proposed in literature. However, they lack a proper privacy characterization and measurement. all the existing PPDP schemes have limitations in privacy characterization  Proposed System: PUBLISHING MODEL goals is to quantify privacy loss of individuals having other attribute values (other diseases) within the same class. PRIVACY CHARACTERIZATION proposes two privacy metrics that can measure privacy leakage from two different perspectives.  Methodology: The proposed framework consists of two steps . First, model attributes in a dataset as a multi-variable model. Based on first model, are able to re-define the prior and posterior adversarial belief about attribute values of individuals.  Then characterize privacy of these individuals based on...
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Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

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Aim:   To build a dataset to learn a powerful CNN-based SICE enhancer from multi-exposure images. Proposed system: propose to use the convolutional neural network (CNN) to train a single image contrast enhancement (SICE) enhancer. A CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Overcome: One key issue is how to construct a training data set of low-contrast and high-contrast image pairs for end-to-end CNN learning      Advantage:     CNN based SICE method learns a complex nonlinear mapping function to map a low-contrast (either underexposure or over-exposure) region to a good contrast region.     powerful CNN-based SICE enhancer, which is capable of adaptively generating high quality enhancement result for a single over-exposed or underexposed input image.        Existing System:   Previous SICE methods adjust the tone curve to correct ...
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