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arXiv 2019年

作者： Fluri, Janis Kacprzak, Tomasz Lucchi, Aurelien Refregier, Alexandre Amara, Adam Hofmann, Thomas Schneider, Aurel Institute of Particle Physics and Astrophysics Department of Physics ETH Zurich Switzerland Data Analytics Lab Department of Computer Science ETH Zurich Switzerland

Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density Ωm, the fluctuation amplitude σ8, and the intrinsic alignment amplitude AIA. We use a grid of N-body simulations to generate a training set of tomographic weak lensing maps. We test the robustness of the expected constraints to various effects, such as baryonic feedback, simulation accuracy, different value of H0, or the lightcone projection technique. We train a set of ResNet-based CNNs with varying depths to analyze sets of tomographic KiDS mass maps divided into 20 flat regions, with applied Gaussian smoothing of σ = 2.34 arcmin. The uncertainties on shear calibration and n(z) error are marginalized in the likelihood pipeline. Following a blinding scheme, we derive constraints of S8= σ8(Ωm/0.3)0.5= 0.777+0.038-0.036with our CNN analysis, with AIA= 1.398+0.779-0.724. We compare this result to the power spectrum analysis on the same maps and likelihood pipeline and find an improvement of about 30% for the CNN. We discuss how our results offer excellent prospects for the use of deep learning in future cosmological data analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Cosmology

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Cosmological Parameter Estimation and Inference using Deep Summaries

arXiv

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arXiv 2021年

作者： Fluri, Janis Kacprzak, Tomasz Refregier, Alexandre Lucchi, Aurelien Hofmann, Thomas Institute of Particle Physics and Astrophysics Department of Physics ETH Zurich Switzerland Data Analytics Lab Department of Computer Science ETH Zurich Switzerland

The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics. This is often a difficult task given that the observed data can have a very high number of dimensions. In order to address this problem, we propose a novel approach to construct parameter estimators with a quantifiable bias using an order expansion of highly compressed deep summary statistics of the observed data. These summary statistics are learned automatically using an information maximising loss. Given an observation, we further show how one can use the constructed estimators to obtain approximate Bayes computation (ABC) posterior estimates and their corresponding uncertainties that can be used for parameter inference using Gaussian process regression even if the likelihood is not tractable. We validate our method with an application to the problem of cosmological parameter inference of weak lensing mass maps. We show in that case that the constructed estimators are unbiased and have an almost optimal variance, while the posterior distribution obtained with the Gaussian process regression is close to the true posterior and performs better or equally well than comparable methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Parameter estimation

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Robust Perceptual Hashing for Fingerprint Template Protection 1

Robust Perceptual Hashing for Fingerprint Template Protectio...

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1st International Conference on computer Communications and Intelligent Systems, I3CIS 2022

作者： Birouk, Wafa Lahoulou, Atidel Melit, Ali Bouridane, Ahmed University of Bejaia Faculty of Exact Sciences Computer Science Department Bejaia06000 Algeria University Mohammed Seddik Benyahia Computer Science Department Jijel18000 Algeria LaRIA Laboratory University Mohammed Seddik Benyahia Computer Science Department Jijel18000 Algeria University of Sharjah Cybersecurity and Data Analytics Research Center Sharjah United Arab Emirates

ISBN: (纸本)9781665461887

Perceptual hashing has proven its robustness in securing biometric templates, where several methods have been proposed in order to have a robust and discriminating hash code against acceptable attacks. This paper proposes a robust perceptual hashing scheme for biometric template protection that based on SIFT and Harris functions for the feature ex-traction/selection phase and on DWT and SVD functions for the hash code generation phase. The experimental results in this paper discussed the effect of distance value and image blocks size parameters on hash performances (robustness and discrimination), which are used in the feature selection phase and the hash generation phase of our method respectively. The results show that we have could to choose the optimal parameters values in the generation of our hash code to give a best tradeoff between discrimination and robustness against acceptable image manipulations. © 2022 IEEE.

关键词： Signal reconstruction

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Scalable Parallel Algorithms for Shared Nearest Neighbor Clustering

Scalable Parallel Algorithms for Shared Nearest Neighbor Clu...

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International Conference on High Performance Computing

作者： Sonal Kumari Saurabh Maurya Poonam Goyal Sundar S Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Advanced Data Analytics & Parallel Technologies Laboratory INDIA

ISBN: (纸本)9781509054121

Clustering is a popular data mining technique which discovers structure in unlabeled data by grouping objects together on the basis of a similarity criterion. Traditional similarity measures lose their meaning as the number of dimensions increases and as a consequence, distance or density based clustering algorithms become less meaningful. Shared Nearest Neighbor (SNN) is a solution to clustering high-dimensional data with the ability to find clusters of varying density. SNN assigns objects to a cluster, which share a large number of their nearest neighbors. However, SNN is compute and memory intensive for data of large size and/or dimensionality. Nearest neighbor queries are responsible for a major proportion of computations in SNN, resulting in lower efficiency for higher value of number of nearest neighbors (k). The main motivation of this work is to improve the efficiency of SNN and to parallelize it so that it can be used for clustering large high-dimensional datasets and for large values of k. Existing SNN algorithms become inefficient in these situations. In this paper, we present a new sequential SNN algorithm, R-SNN, which uses R-tree for executing neighborhood queries efficiently and exploiting spatial locality to minimize memory usage. R-SNN is benchmarked against the best available implementation of SNN and is found up to 77 times faster when tested on various real datasets. R-SNN is parallelized for distributed memory, shared memory, and hybrid systems. Significant speedup and scalability achieved can be attributed to parallelization and good load balancing strategies and also to exploitation of spatial locality. Experimental results demonstrate the same for datasets of varying dimensionality and size. The maximum speedup achieved for shared, distributed, and hybrid models are 427.19 using 48 threads, 394.24 using 32 processes, and 1380.69 on 32 nodes (with each node spawning 4 threads), respectively. Super-linear speedup for some datasets is attributed

关键词： Clustering algorithms Algorithm design and analysis Time complexity Spatial databases Parallel algorithms Memory management Density measurement

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A new algorithm for detection of nodes failures and enhancement of network coverage and energy usage in wireless sensor networks

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Materials Today: Proceedings 2023年 80卷 1717-1722页

作者： Satyanarayana, P. Mahalakshmi, T. Sivakami, R. Alahmari, Saad Ali Rajeyyagari, Sivaram Asadi, Srinivasulu ECE V. R. Siddhartha Engineering College Vijayawada India Department of ECE Prasad V Potluri Siddhartha Institute of Technology India Associate Professor CSE Sona College of Technology TN Salem 5 India Department of Computer Science Shaqra University Saudi Arabia Data Analytics Research Laboratory BlueCrest University College Monrovia Liberia

Present natural lives shows rising attention in various applications of the wireless sensor networks (WSNs). Significant applications are isolated and stiff regions where human involvement is hazardous or unfeasible. Some examples are space investigation, battlefield observation, costal, and edge security;nowadays, such networks are needful in several manufacturing and customer applications. A WSN is a device system, indicated as nodes;these nodes intellect the atmosphere and transfer the details collected from the ground through a wireless connection. Statistics are sent, probably via many hops, to a target that can use it nearby or is linked to various networks using the gateway. The sensor nodes might be stationary or changeable. Every node has clear data about the neighborhood or not. Communication over WSN over ecological hazards is a key issue. These constraints might impact the performance of the sensors/direction finding protocols and resource utilization;thus, they may show the way to the node crash situation, i.e., software/hardware breakdown, safety threats, extreme power utilization, etc. It is essential to examine the collision of breakdown over system performance. In the projected method, relay nodes are used as secure nodes for the positioned sensor nodes. The projected method is separated into two phases: the intra segregation stage and inter segregation stage. In the intra segregation stage, the unnecessary nodes reach nearer to the segregation border, and dispersion slowly enlarges the coverage. The projected model is examined through simulations and compares the results with existing methods. © 2021

关键词： Sensor nodes

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Volatile Memory Extraction-Based Approach for Level 0-1 CPS Forensics

Volatile Memory Extraction-Based Approach for Level 0-1 CPS ...

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2019 IEEE International Symposium on Technologies for Homeland Security, HST 2019

作者： Awad, Rima Asmar Lopez, Juan Rogers, Mike National Security Sciences Direct. Oak Ridge National Laboratory Cyber and Data Analytics Division Oak RidgeTN United States Collage of Engineering Tennessee Technological University Computer Science Department CookevilleTN United States

ISBN: (数字)9781728150925

ISBN: (纸本)9781728150925

Most security analyzers operate on system state that is far removed from end-point components in cyber-physical systems (CPS) identified as level 0-1 in the Purdue Architecture Reference Architecture (PERA) [1]. For example, many operate on system logs and other data dumps to disks. Tremendous value that can be gained in cyber security forensics if low level details such as dynamic changes to volatile memory can be extracted and provided to more sophisticated analysis tools. However, obtaining detailed and dynamic system state at the level of volatile memory is extremely challenging [2]. Here, we attempt to apply IT memory forensic mechanisms to CPS end-point devices and statistically evaluate them. Our focus is to extract volatile and dynamically changing internal information form CPS 0-1 level devices, and design preliminary schemes to exploit that extracted information. This new capability of generating a sequence of volatile memory snapshots for offline, detailed and sophisticated analysis opens a new class of cyber security schemes for CPS forensic analysis. As a case study for our ongoing research, we apply the proposed methodology to Modicon PLC using Modbus protocol. We extract the memory layout and subject the device to read operations at the most critical regions of memory. Similarly, write operations are initiated to carefully determine memory locations (for example, bytes that hold the firmware version number). This capability of generating a sequence of volatile memory snapshots for offline, detailed and sophisticated analysis opens a new class of cyber security schemes for CPS forensic analysis. Also, the ability to dynamically make controlled modifications to specific memory locations opens the potential for new mechanisms such as taint analysis and watermarking. © 2019 IEEE.

关键词： Dynamics

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A Parallel Framework for Grid-Based Bottom-Up Subspace Clustering

A Parallel Framework for Grid-Based Bottom-Up Subspace Clust...

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International Conference on data science and Advanced analytics (DSAA)

作者： Poonam Goyal Sonal Kumari Shubham Singh Vivek Kishore Sundar S. Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Advanced Data Analytics & Parallel Technologies Laboratory Pilani Campus INDIA

ISBN: (纸本)9781509052073

Clustering is a popular data mining and machine learning technique which discovers interesting patterns from unlabeled data by grouping similar objects together. Clustering high-dimensional data is a challenging task as points in high dimensional space are nearly equidistant from each other, rendering commonly used similarity measures ineffective. Subspace clustering has emerged as a possible solution to the problem of clustering high-dimensional data. In subspace clustering, we try to find clusters in different subspaces within a dataset. Many subspace clustering algorithms have been proposed in the last two decades to find clusters in multiple overlapping subspaces of high-dimensional data. Subspace clustering algorithms iteratively find the best subset of dimensions for a cluster from 2d-1 possible combinations in d-dimensional data. Subspace clustering is extremely compute intensive because of exhaustive search of subspaces, especially in the bottom-up subspace clustering algorithms. To address this issue, an efficient parallel framework for grid-based bottom-up subspace clustering algorithms is developed, considering popular algorithms belonging to this category. The framework is implemented for shared memory, distributed memory, and hybrid systems and is tested for three grid-based bottom-up subspace clustering algorithms: CLIQUE, MAFIA, and ENCLUS. All parallel implementations exhibit impressive speedup and scalability on real datasets.

关键词： Conferences

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Classification of Pigmented Skin Lesions using Early Fusion of Multi-Deep Features and Support Vector Machine

Classification of Pigmented Skin Lesions using Early Fusion ...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Jayanth Chowdam Sibarama Panigrahi Department of Computer Science and Engineering Intelligent Data Analytics Laboratory National Institute of Technology Rourkela Odisha India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin Lesions(PSL) is a major challenge due to availability and bias of physician, time consuming, invasive methods are painful and with elevated expenses. To overcome this, in this paper, two automated computer vision based approaches (using and without using dimensionality reduction) are employed by modelling multi-deep features using Support Vector Machine(SVM) for classification of PSL. The suggested system comprises of two operational stages. Initially, the performance of six Convolutional Neural Network models (VGG16, VGG19, AlexNet, Xception, ResNet50, and EfficientB3) in deep feature extraction and three types of hand-crafted features extraction methods (HOG, LBP, and SIFT) employing SVM are evaluated. The top three CNN models (AlexNet, ResNet50, and EfficientB3) based on accuracy are used to form binary and ternary early fusion hybrid models to classify the skin lesions. In the second stage, combining multiple CNN models resulted in increased dimensionality and number of features. These features are then classified using SVM once in the higher dimension and again by reducing the dimensionality by applying Principal Component Analysis(PCA). The accuracies of the combined models are then compared. It is observed that the ternary early fusion model with SVM (Alexnet+Resnet50+EfficientB3-SVM) produces the highest accuracy of 82.22% with higher number of features whereas the same model while using PCA produces the accuracy of 81.57%. Consequently, the findings indicate the suggested techniques are useful for classification of PSL.

关键词： Support vector machines Accuracy Computational modeling Feature extraction Skin Vectors Lesions Convolutional neural networks Residual neural networks Principal component analysis

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Automatic Histopathological Image Classification using Multi-Deep Features with Handcrafted Features and Shallow Machine Learning Models

Automatic Histopathological Image Classification using Multi...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Sunrit Banerjee Sibarama Panigrahi Department Of Computer Science and Engineering Intelligent Data Analytics Laboratory National Institute Of Technology Rourkela Odisha India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Invasive ductal carcinoma (IDC) is one of the most common variant of breast cancer affecting women more frequently than any other cancer. One crucial clinical duty is to correctly detect and classify breast cancer subtypes. Automated techniques can help to reduce errors and save time. However, determining the malignancy from tissue biopsies is difficult and dependent on the subjectivity of the observer. To diagnose breast cancer, a novel classification model using fusion of deep features VGG16 and VGG19 with handcrafted feature GLCM alongside the Medium Gaussian SVM classifier is developed in this paper. Additionally, thirteen hybrid classifiers are used to study the classification of breast cancer. These classifiers use four deep features, three handcrafted features, and a further fusion of deep features to produce six more combinations. Resnet50, VGG16, VGG19 and GoogleNet (Inception v3) are the pre-trained networks used to extract the deep features. Similarly, the handcrafted feature extraction techniques used are HOG, LBP and GLCM. Following evaluation, the classification model is framed by considering the best-performing deep features (VGG16 and VGG19 fusion model), handcrafted feature (GLCM) and classifier (Medium Gaussian SVM). Area Under Curve (AUC) of 0.91, Accuracy of 87.8% and Positive Likelihood Ratio (LR+) of 12.2 were attained by the Medium Gaussian SVM based classification model that combined the features of GLCM with the fusion model of VGG16 and VGG19. According to STARD guidelines, the recommended diagnostic technique is Excellent (AUC is between 0.9 and 1.0 and LR+ is above 10.0). The achieved results also outperformed the recently published articles on the same domain tackling similar problems.

关键词： Support vector machines Accuracy Observers Feature extraction Breast cancer Robustness Optimization Image classification Guidelines Residual neural networks

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A Constraint Programming Approach to the Additional Relay Placement Problem in Wireless Sensor Networks

A Constraint Programming Approach to the Additional Relay Pl...

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International Conference on Tools with Artificial Intelligence

作者： Luis Quesada Kenneth N. Brown Barry O'Sullivan Lanny Sitanayah Cormac J. Sreenan INSIGHT Centre for Data Analytics Department of Computer Science University College Cork Mobile and Internet Systems Laboratory Department of Computer Science University College Cork

ISBN: (纸本)9781479929733

A Wireless Sensor Network (WSN) is composed of many sensor nodes which transmit their data wirelessly over a multi-hop network to data sinks. Since WSNs are subject to node failures, the network topology should be robust, so that when a failure does occur, data delivery can continue from all surviving nodes. A WSN is k-robust if an alternate length-constrained route to a sink is available for each surviving node after the failure of up to k-1 nodes. Determining whether a network is k-robust is an NP-complete problem. We develop a Constraint Programming (CP) approach for solving this problem which outperforms a Mixed-Integer Programming (MIP) model on larger problems. A network can be made robust by deploying extra relay nodes, and we extend our CP approach to an optimisation problem by using QuickXplain to search for a minimal set of relays, and compare it to a state-of-the-art local search approach.

关键词： Wireless sensor networks relay constraint handling wireless multi-hop networks Nondeterministic polynomial-time hard Minimal Set Sinks Ganglia Network topology WSN gene failures

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