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Contemporary Stegnalysis Schemes for Reliable Detection of Steganography 2

Contemporary Stegnalysis Schemes for Reliable Detection of S...

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2nd IEEE international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Babu, G. John Sridevi, R. Sreekavitha Engn Coll Khammam Telangana India JNTUH Coll Engn Hyderabad Telangana India

ISBN: (纸本)9781509044429

Steganalysis is the process of detecting the hidden information in the carrier. Most used carriers for steganography are images due to the redundant information present in the images and frequency of their use on the Internet. Steganalysis methods are classified into two categories, Targeted steganalysis and universal steganalysis. Targeted steganalysis is based on analysis of individual and known steganographic scheme. Blind steganalysis methods detect steganographic schemes created by unknown random stego-systems. The objective of steganalysis algorithms is to distinguish stego images from pure images. A classifier is built based on stego and pure images. When the knowledge of steganographic scheme is not available, a general steganalyzer is built, which is trained with a set of pure images and a set of stego images generated by various steganographic algorithms. The performance of steganalysis algorithm depends on three important aspects, preprocessing technique, feature selection & extraction and classification. This paper presents the contemporary steganalysis schemes discussing the details and comparing various aspects of these methods.

关键词： Image steganalysis Feature Selection Feature Extraction machine learning

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Does Race Play a Role in Invasive Procedure Treatments? An Initial Analysis 17

Does Race Play a Role in Invasive Procedure Treatments? An I...

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2nd IEEE/ACM international conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Hammarlund, Noah Natarajan, Sriraam Indiana Univ Sch Publ & Environm Affairs Bloomington IN 47405 USA Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA

ISBN: (纸本)9781509047222

This study applies machine learning to a popular inpatient data set to identify whether black patients are treated differently than other patients in the invasive heart treatment decision. We use reverse machine learning to predict patient race using treatment and comorbidities in a matched patient sample. Our finding is suggestive that treatment choice only moderately depends on patient race.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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2017 IEEE 2nd international conference on signal and Image processing, ICSIP 2017

2017 IEEE 2nd International Conference on Signal and Image P...

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2nd IEEE international conference on signal and Image processing, ICSIP 2017

ISBN: (纸本)9781538609682

The proceedings contain 97 papers. The topics discussed include: improved k-d tree-segmented block truncation coding for color image compression;an error detection and recovery technique for images compressed with the CCSDS compression algorithm;adaptive compressive-sensing of 3D point clouds;compressed sensing MRI with total variation and frame balanced regularization;medical image fusion based on GPU accelerated nonsubsampled shearlet transform and 2D principal component analysis;a novel approach on classification of infant activity post surgery based on motion vector;automatic localization of optic disc based on deep learning in fundus images;a novel compensation algorithm of aerial image registration;three-dimensional positioning using ALOS/prism triple linear-array satellite images;digital anthropometry for human body measurement on android platform;stacked hidden Markov model for motion intention recognition;image processing algorithm for extracting the phase map from structured lights;supervised 3D graph-based automated epidermal thickness estimation;optimizing cognitive analysis sensitivity of photospheres using cube maps;detecting AMD caused vision scotoma through eye tracking;and learning visual odometry for unmanned aerial vehicles.

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machine learning resistant arbiter PUFs scheme based on polynomial reconstruction 2

Machine learning resistant arbiter PUFs scheme based on poly...

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2nd IEEE international conference on signal and Image processing, ICSIP 2017

作者： Chen, Shuai Li, Bing Dan, Fukui Chen, Jian Advanced Cloud-System Research Center School of Microelectronics Southeast University Nanjing China

ISBN: (纸本)9781538609682

Physical Unclonable Functions (PUFs) are emerging as an important building block in hardware security. It has been widely used in key generation and authentication. However, Strong PUFs are vulnerable to the machine learning (ML) attack by modeling the challenge-response pairs (CRPs) behavior. The traditional resistant methods based on non-linear blocks (e.g. XOR) have a negative impact on the reliability of Arbiter PUFs (APUFs). In this paper, we proposed a novel error-Tolerant countermeasure to resist this attack. In our Randomized Arbiter PUFs (R-APUFs) scheme, the mapping of CRPs is randomized based on the unordered feature of points in polynomial reconstruction to meet the randomness requirements of ML resistance. Moreover, the error-Tolerant feature of this scheme will improve the reliability of R-APUFs when used in authentication. We exemplified the Evolution Strategy (ES)-based ML attacks on APUFs and R-APUFs. The experimental result indicates that the modeling accuracy can be reduced from over 93% to below 60%, the percentage of authentication success based on R-APUFs in 1000 tests is 100%. Therefore, this scheme can be a good supplement for the existing ML resistance work. © 2017 IEEE.

关键词： Polynomials

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Towards learning Efficient Intervention Policies for Wearable Devices 17

Towards Learning Efficient Intervention Policies for Wearabl...

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2nd IEEE/ACM international conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Saponaro, Matthew Wei, Haoran Decker, Keith Univ Delaware Dept Comp Sci Newark DE 19711 USA

ISBN: (纸本)9781509047222

To serve large user populations, autonomous intervention systems (i.e. intelligent agents) are being developed to play more active roles such as fitness coaches and clinical disease prevention aids. Although generic user models have been developed, users may require extensive individualization to meet their personal needs. machine learning techniques may be applied to learn tailored intervention policies for users. However, traditional machine learning requires significant amounts of data to learn an optimal policy. For wearable technology, this may mean probing the user to perform some activity and gauging user response. This paper presents a feasible intervention system model and discusses learners for tailoring user intervention policies. We examine how similar the general user model has to be with respect to the tailored model in order for our learner to perform well.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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Boosting for Postpartum Depression Prediction 17

Boosting for Postpartum Depression Prediction

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2nd IEEE/ACM international conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Natarajan, Sriraam Prabhakar, Annu Ramanan, Nandini Baglione, Anna Connelly, Kay Siek, Katie Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA Univ Cincinnati Sch Informat Technol Cincinnati OH 45221 USA

ISBN: (纸本)9781509047222

Pregnancy and childbirth are important transitional life events for women. Like many other transitional life events, the effects of pregnancy and childbirth can have significant impact on a mother's physical and mental well-being. Sometimes they can even lead to Postpartum Depression (PPD). If left untreated, PPD can be debilitating for the mother and can adversely affect her ability to take care of herself and her infant. Since PPD is not clinically diagnosable, we consider the problem of predicting PPD from survey data about demographics, depression, and pregnancy etc. We adapt the successful functional-gradient boosting algorithm that can handle class imbalance in a principled manner. Our results demonstrate that the proposed machine learning approach can outperform the baseline classifiers and, consequently, demonstrate the potential of machine learning in predicting PPD.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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CNFL: Categorical to Numerical Feature learning for Clustering and Classification 2

CNFL: Categorical to Numerical Feature Learning for Clusteri...

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2nd IEEE international conference on Data Science in Cyberspace (DSC)

作者： Golinko, Eric Sonderman, Thomas Zhu, Xingquan Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

ISBN: (纸本)9781538616000

Categorical data exist in many domains, such as text data, gene sequences, or data from Census Bureau. While such data are easy for human interpretation, they cannot be directly used by many classification methods, such as support vector machines and others, which require underlying data to be represented in a numerical format. To date, most existing learning methods convert categorical data into binary features, which may result in high dimensionality and sparsity. In this paper, we propose a method to convert category data into an arbitrary number of numerical features. Our method, named CNFL, uses simple matching to calculate proximity between instances, then uses an eigendecomposition to convert the proximity matrix into a low-dimensional space, which can be used to represent instances for classification or clustering. Experiments on 21 datasets demonstrate that numerical features learned from CNFL can effectively represent the original data for machine learning tasks.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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COMPLEXIS 2017 - proceedings of the 2nd international conference on Complexity, Future Information Systems and Risk

COMPLEXIS 2017 - Proceedings of the 2nd International Confer...

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2nd international conference on Complexity, Future Information Systems and Risk, COMPLEXIS 2017

ISBN: (纸本)9789897582448

The proceedings contain 18 papers. The topics discussed include: security against collective attacks of a modified BB84 QKD protocol with information only in one basis;unikernels for cloud architectures: how single responsibility can reduce complexity, thus improving enterprise cloud security;parallelism strategies for neurophysiological delayed transfer entropy data processing - towards causal inference in big data;automatic tuning of a local search algorithm for estimating biological signals JPDs;a network of networks to reproduce the electrical features of an aptamer-ligand complex - what an electrical network tells about affinity;improvement of the detection of the QRS complex, T and P waves in an electrocardiogram signal using 12 leads versus 2 leads;a data-aware MultiWorkflow scheduler for clusters on WorkflowSim;real-world examples of agent based decision support systems for deep learning based on complex feed forward neural networks;probing complexity with epidemics: a new reactive immunization strategy;studying complex interactions in real time: an XMPP-based framework for behavioral experiments;Paramo and high-Andean simulation using reactive agents - hydrological role of high Andean ecosystems;analysis on the graph techniques for data-mining and visualization of heterogeneous biodiversity data sets;and data driven web experimentation on design and personalization.

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Enhancing CNN with Preprocessing Stage in Automatic Emotion Recognition 2nd

Enhancing CNN with Preprocessing Stage in Automatic Emotion ...

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2nd international conference on Computer Science and Computational Intelligence (ICCSCI)

作者： Pitaloka, Diah Anggraeni Wulandari, Ajeng Basaruddin, T. Liliana, Dewi Yanti Univ Indonesia Fac Comp Sci Depok 16424 West Java Indonesia

ISBN: (纸本)9781510849914

Emotion recognition from facial expression is the subfield of social signal processing which is applied in wide variety of areas, specifically for human and computer interaction. Many researches have been proposed for automatic emotion recognition, which is fundamentally using machine learning approach. However, recognizing basic emotions such as angry, happy, disgust, fear, sad, and surprise is still becoming a challenging problem in computer vision. Lately, deep learning has gained more attention to solve many real-world problems, including emotion recognition. In this research, we enhanced Convolutional Neural Network method to recognize 6 basic emotions and compared some preprocessing methods to show the influences of its in CNN performance. The compared data preprocessing methods are: resizing, face detection, cropping, adding noises, and data normalization consists of local normalization, global contrast normalization and histogram equalization. Face detection as single pre-processing phase achieved significant result with 86.08 % of accuracy, compared with another pre-processing phase and raw data. However, by combining those techniques can boost performance of CNN and achieved 97.06% of accuracy. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Emotion recognition Facial expression Convolutional neural network machine learning Computer vision Normalization

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2017 2nd international conference on Opto-Electronic Information processing, ICOIP 2017

2017 2nd International Conference on Opto-Electronic Informa...

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2nd international conference on Opto-Electronic Information processing, ICOIP 2017

ISBN: (纸本)9781509062515

The proceedings contain 18 papers. The topics discussed include: an improved scheme of initial radius selection in SD algorithm under the low SNR;self-localization algorithm for deep mine wireless sensor networks based on MDS and rigid subset;adaptive equalization in hybrid DS-TH CDMA system using state-space RLS with adaptive memory;deep learning detection method for signal demodulation in short range multipath channel;hybrid optical encoding structures for two-layer optical information authentication;impact of fiber effective core area on transmission performance of various data rates over 390 km repeaterless bidirectional Raman span;non-rigid registration of multimodal images (ultrasound and CT) of liver using gradient orientation information;a super resolution algorithm based on L-R iteration geometric mean in electromagnetic imaging;a novel image defogging algorithm based on multi-resolution fusion transform;single-photon avalanche diode array with column-level time-to-digital converter for unmanned vehicle;electron trapping/detrapping model in electrically stressed oxide;combing structured light measurement technology with binocular stereo vision;dependence analysis of the GaN HEMT parameters for space application on the thickness AlGaN barrier layer by numerical simulation;and a case of charging induced damage into the common metal interconnect during chemical mechanical polishing.

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