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Quantum Dimension Reduction for Pattern Recognition in High-Resolution Spatio-Spectral Data
کاهش ابعاد کوانتومی برای تشخیص الگو در داده های فضایی-طیفی با وضوح بالا-2022 The promises of advanced quantum computing technology have driven research in the simulation of quantum computers on
classical hardware, where the feasibility of quantum algorithms for real-world problems can be investigated. In domains such as High
Energy Physics (HEP) and Remote Sensing Hyperspectral Imagery, classical computing systems are held back by enormous readouts
of high-resolution data. Due to the multi-dimensionality of the readout data, processing and performing pattern recognition operations
for this enormous data are both computationally intensive and time-consuming. In this article, we propose a methodology that utilizes
Quantum Haar Transform (QHT) and a modified Grover’s search algorithm for time-efficient dimension reduction and dynamic pattern
recognition in data sets that are characterized by high spatial resolution and high dimensionality. QHT is performed on the data to
reduce its dimensionality at preserved spatial locality, while the modified Grover’s search algorithm is used to search for dynamically
changing multiple patterns in the reduced data set. By performing search operations on the reduced data set, processing overheads
are minimized. Moreover, quantum techniques produce results in less time than classical dimension reduction and search methods.
The feasibility of the proposed methodology is verified by emulating the quantum algorithms on classical hardware based on field
programmable gate arrays (FPGAs). We present designs of the quantum circuits for multi-dimensional QHT and multi-pattern Grover’s
search. We also present two emulation techniques and the corresponding hardware architectures for this methodology. A high
performance reconfigurable computer (HPRC) was used for the experimental evaluation, and high-resolution images were used as the
input data set. Analysis of the methods and implications of the experimental results are discussed.
Index Terms— Quantum computing | field-programmable gate arrays (FPGAs) |
مقاله انگلیسی |
2 |
A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
بررسی جامع تشخیص حملات سایبری: مجموعه دادهها، روشها، چالش ها و جهتگیریهای تحقیقاتی آینده-2022 Rapid developments in network technologies and the amount and scope of data transferred on networks
are increasing day by day. Depending on this situation, the density and complexity of cyber threats
and attacks are also expanding. The ever-increasing network density makes it difficult for cybersecurity professionals to monitor every movement on the network. More frequent and complex cyberattacks make the detection and identification of anomalies in network events more complex. Machine
learning offers various tools and techniques for automating the detection of cyber attacks and for
rapid prediction and analysis of attack types. This study discusses the approaches to machine learning
methods used to detect attacks. We examined the detection, classification, clustering, and analysis of
anomalies in network traffic. We gave the cyber-security focus, machine learning methods, and data
sets used in each study we examined. We investigated which feature selection or dimension reduction
method was applied to the data sets used in the studies. We presented in detail the types of classification
carried out in these studies, which methods were compared with other methods, the performance
metrics used, and the results obtained in tables. We examined the data sets of network attacks presented
as open access. We suggested a basic taxonomy for cyber attacks. Finally, we discussed the difficulties
encountered in machine learning applications used in network attacks and their solutions
Keywords: Cyber attacks | Machine learning | Deep learning | Geometric deep learning | Cyber security | Adversarial machine learning | Intrusion detection |
مقاله انگلیسی |
3 |
A Low-Complexity Quantum Principal Component Analysis Algorithm
یک الگوریتم تحلیل مولفه اصلی کوانتومی با پیچیدگی کم-2022 In this article, we propose a low-complexity quantum principal component analysis (qPCA)
algorithm. Similar to the state-of-the-art qPCA, it achieves dimension reduction by extracting principal
components of the data matrix, rather than all components of the data matrix, to quantum registers, so that the
samples of measurement required can be reduced considerably. Both our qPCA and Lin’s qPCA are based
on quantum singular-value thresholding (QSVT). The key of Lin’s qPCA is to combine QSVT, and modified
QSVT is to obtain the superposition of the principal components. The key of our algorithm, however, is to
modify QSVT by replacing the rotation-controlled operation of QSVT with the controlled-not operation
to obtain the superposition of the principal components. As a result, this small trick makes the circuit
much simpler. Particularly, the proposed qPCA requires three phase estimations, while the state-of-the-art
qPCA requires five phase estimations. Since the runtime of qPCA mainly comes from phase estimations, the
proposed qPCA achieves a runtime of roughly 3/5 of that of the state of the art. We simulate the proposed
qPCA on the IBM quantum computing platform, and the simulation result verifies that the proposed qPCA
yields the expected quantum state.
INDEX TERMS: Quantum computing | quantum principal component analysis (qPCA) | quantum singular value threshold. |
مقاله انگلیسی |
4 |
Feature based classification of voice based biometric data through Machine learning algorithm
طبقه بندی مبتنی بر ویژگی داده های بیومتریک مبتنی بر صدا از طریق الگوریتم یادگیری ماشین-2021 In the era of big data and growing artificial intelligence, the requirement and necessity of biometric identification increase in a rapid manner. The digitalization and recent Pandemic crisis gives a boost to need to authorized identification which get fulfilled with biometric identification. Our paper focuses on same concept of checking the identification accuracy of machine learning algorithm REPTree on selected bio- metric dataset which is being deployed and evaluated on a data mining tool WEKA. Our target is to achieve more or equal to 95 percentages in order to predict the given sample data is accurately classified into our target variables values i.e. male female. The selected algorithm REPTree is a kind of decision tree classification algorithm which works on same concept as C4.5 and decision tree algorithm with speciality of generation of both kind of output i.e. discrete and continuous. The selection of algorithm gives us ben- efits with achievement of higher accuracy and selection of dataset also become easy with some required modification and pre-processing of data with some dimension reduction filters.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Con- ference on Computations in Materials and Applied Engineering – 2021. Keywords: Prediction | Biometric data | Voice samples | Male | Female | Cost complexity pruning (CCP) | Dimension reduction |
مقاله انگلیسی |
5 |
A novel approach for multi-objective optimal scheduling of large-scale EV fleets in a smart distribution grid considering realistic and stochastic modeling framework
یک رویکرد جدید برای برنامه ریزی بهینه چند منظوره از ناوگان های مقیاس بزرگ EV در یک شبکه توزیع هوشمند با توجه به چارچوب مدل سازی واقع گرایانه و تصادفی-2020 The ever-increasing number of grid-connected electric vehicles (EVs) has led to emerging new opportunities and
threats in electrical distribution systems (DS). Developing a realistic model of EV interaction with the DS, as well
as developing a strategy to optimally manage these interactions in line with distribution system operators (DSOs)
intentions, are the most important prerequisites for gaining from this phenomenon especially in modern smart
distribution systems (SDS). In this paper, a comprehensive model describing the electric vehicle integration to an
SDS is presented by considering the real-world data from EV manufacturers and DSOs. Moreover, a novel energy
management strategy (EMS) based on the multi-objective optimization problem (MOOP) is developed to fulfill
the operational objectives of DSO and EV owner, including peak load shaving, loss minimization, and EV owner
profit maximization. In this regard, an innovative dimension reduction approach is presented, to make it feasible
to apply the heuristic optimization methods to a MOOP with a large number of decision variables. Thanks to this
method, the improved electromagnetism like algorithm (IEMA) is employed to perform the multi-objective
energy scheduling for a large-scale EV fleet. In addition, a novel method is devised for estimating the optimal
hosting capacity of an SDS in adopting EVs without the need for sophisticated computations. The presented
method is applied to the modified IEEE-33 bus test system. Obtained results reveal that employment of a realistic
model concludes to more accurate results than a simplified model. In addition, the efficiency of the proposed
EMS in satisfying EV owner and DSO objectives are approved by analyzing obtained computation results. Keywords: Smart grid | Energy management | Electric vehicle | Vehicle to grid | Multi-objective optimization |
مقاله انگلیسی |
6 |
Development of self-adaptive low-dimension ventilation models using OpenFOAM: Towards the application of AI based on CFD data
توسعه مدل های تهویه کم بعد خود تطبیقی با استفاده از OpenFOAM: به سمت استفاده از هوش مصنوعی بر اساس داده های CFD-2020 Numerous state-of-art CFD (Computational Fluid Dynamics) studies have shown their validity and feasibility in
engineering applications but still lack prediction efficiency. It is of great potential to apply artificial intelligence
(AI) on the basis of CFD considering their fast development. Thus, the data-dimension reduction of CFD can be
very important for the efficiencies of database construction, training and storage. Our previously developed
linear low-dimension ventilation model (LLVM) is able to convert high-resolution CFD data into low-dimension
grid levels, facilitated the use of fast prediction for ventilation online control. However, limitation still exists
considering the dilemma of prediction speed and accuracy, e.g., case of a larger building space. This is due to the
neglect of volume contribution ratio from single mesh as well as correlations of cells information when using
uniform low-dimension methods. Therefore, we proposed a self-adaptive non-uniform low-dimension model for
the data conversion but using lower dimension size with acceptable accuracy. The open-source CFD platform
OpenFOAM was used for the package development, called self-adaptive low-dimension tool (LDT), including two
modules, i.e., ‘non-uniform dividing’ and ‘self-update’. Error index was defined considering the contribution
ratio of individual mesh volume. A series of cases were carried out for demonstration and evaluation. It is found
that the proposed model is able to largely improve the data accuracy but with smaller dimension requirement
compared to uniform dividing method (e.g., with comparable error index around 16.5% when using zone
numbers of 80 for non-uniform and 210 for uniform). Moreover, the self-update module enables users to efficiently
and automatically identify the optimal low-dimension zone numbers. This work can be of great importance
for the application of CFD-AI techniques. Keywords: Ventilation | Fast prediction | Low-dimensional models (LVM) | Dimension reduction | OpenFOAM | CFD-AI/ANN database construction |
مقاله انگلیسی |
7 |
تشخیص چند نمایی چهره با استفاده از شبکه های عصبی عمیق
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 19 تشخیص چهره به طور گسترده در سیستم های هوشمندی مدرن مانند نظارت تصویری هوشمند، پرداخت آنلاین و سیستم دسترسی هوشمند مورد استفاده قرار گرفته است. الگوریتم های تشخیص چهره فعلی در معرض حمله انواع حملات ارائه چهره می باشند؛ کاغذ چاپ شده، بازپخش ویدئویی و ماسک های سیلیکونی از این جمله حملات اند. ما به منظور مدیریت بهینه مشکلات مذکور، معماری عمیق و جدیدی را صورت بندی نموده ایم که دقت تشخیص چندنمایی چهره انسان را افزایش می دهد. به ویژه، در وهله اول، شبکه عصبی عمیق و جدیدی به منظور رمزگذاری عمیق نواحی صورت ساخته شده است که در آن الگوریتم جدید تنظیم و تطبیق چهره به کار رفته است تا بر روی نقاط کلیدی موجود در چهره متمرکز گردد. بعد از آن، فناوری شناخته شده PCA را برای کم کردن ابعاد ویژگی های عمیق و به طور همزمان، حذف ویژگی های تصویری ناخالص و غیرضروری به کار برده ایم. سپس چارچوب اتصال بیزی را برای ارزیابی شباهت بردارهای ویژگی و دقت بسیار رقابتی دسته بندی چهره ها که می توان به آن دست یافت مطرح نمودیم. آزمایشات جامع بر روی مجموعه داده های کامپایل شده کاس-پیل انجام گرفته و عملکرد تشخیص چهره به میزان 98.52% موفقیت آمیز بود. علاوه بر این، سامانه پیشنهادی تشخیص چهره، به صورت سفت و سخت قادر به مدیریت حملات مختلف تشخیص چهره در زمینه های مختلف می باشد.
کلمات کلیدی: یادگیری عمیق | ناحیه صورت | تشخیص تصویر چهره | شبکه عصبی عمیق | کاهش ابعاد ویژگی PCA |
مقاله ترجمه شده |
8 |
Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition
Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition-2019 Heterogeneous face recognition (HFR) is still a challenging problem in computer vision community due to large
appearance difference between near infrared (NIR) and visible light (VIS) modalities. Recently, breakthroughs
have been made for traditional face recognition by applying deep learning on a huge amount of labeled
VIS face samples. However, the same deep learning approach cannot be simply applied to HFR task due
to large domain difference as well as insufficient pairwise images in different modalities during training. In
general, the pooling layer of deep network can play the role of feature reduction, but also lead to the loss
of useful face information, resulting in a decrease in the performance of HFR problem. It is important to
eliminate modal-related information and retain more facial identity information. In this paper, we propose
a novel method called Discriminant Deep Feature Learning Based on Joint Supervision Loss and Multi-layer
Feature Fusion (DDFLJM) for HFR task. In most of the available CNNs, the softmax loss function is used as
the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply
learned features, this paper proposes a new loss function called Scatter Loss (SL), which embeds both interand
intra-class information for effectively training the deep model. To make full use of the various layers
of the deep network, a Dimension Reduction Block (DRB) is designed to effectively extract the auxiliary
features on multiple mid-level layers. An orthogonality constraint is introduced to the DRB block to reduce
spectrum variations of two different modalities. The proposed SL is applied to multiple layers of network
for joint supervision training, which enables multiple layers of the network to obtain discriminative identity
features. Moreover, a Modified Gate Two-stream Neural Network (MGTNN) is adopted to fuse multiple-layer
features. Extensive experiments are carried out on two challenging NIR-VIS HFR datasets CASIA NIR-VIS 2.0
and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method. Keywords: Heterogeneous face recognition | Deep learning | Joint supervision loss | Feature fusion |
مقاله انگلیسی |
9 |
Local polynomial contrast binary patterns for face recognition
الگوهای باینری کنتراست چند جمله ای محلی برای تشخیص چهره-2019 We propose a novel face representation model, called the polynomial contrast binary patterns (PCBP), based on the polynomial filters, for robust face recognition. It is assumed that the discrete array of pixel values comes about by sampling an underlying smooth surface in an image. The proposed method effi- ciently estimates the underlying local surface information, which is approximately represented as linear projection coefficients of the pixels in a local patch. The decomposition using polynomial filters can cap- ture rich image information at multiple orientations and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative pow- ers of each filter response image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image for dimension reduction of features. Our extensive experiments on the public FERET and LFW databases demonstrate that the non-weighted Polynomial contrast binary patterns performs better than most of methods and the weighting scheme further improves the recognition rates. WPCBP + FLD(CD) and WPCBP + FLD(HI) can achieve much competitive or even better recognition perfor- mance compared with the state-of-the-art face recognition methods. Keywords: Face recognition | Polynomial filters | Local binary patterns | Surface fitting |
مقاله انگلیسی |
10 |
On dimension reduction models for functional data
مدل های کاهش ابعاد داده های عملکردی-2018 This contribution is part of the recent links between Functional Data and Big Data commu
nities. A selected survey highlights how earlier ideas in high dimensional problems can be
adapted in functional setting.
Keywords: Functional data ، Dimension reduction ، Semi-parametrics ، Sparse regression |
مقاله انگلیسی |